Many modern organizations use data analytics to build strategies, make decisions, and identify trends. This approach is also being adopted by the healthcare industry, reinforcing the value of healthcare predictive analytics for organizations and patients.
Data analytics helps medical institutions assess and develop medical practitioners, identify anomalies, and predict new disease outbreaks.
In addition, investments in data analysis allow organizations to reduce costs while optimizing many processes.
Among the main benefits of data analytics is the ability for institutions to make better decisions about patient care, as well as improve management.
Thus, decisions based on data and facts contribute to the achievement of the organization’s goals, reduce risks, and allow better treatment and medical services to be provided to patients.
Let’s take a look at some of the main types of healthcare data analytics, how it works, and why it’s essential.
Main Types of Healthcare Data Analytics
Healthcare data analytics is of several types and can have different objectives. The most commonly encountered types of analytics include:
- Descriptive analytics. This type of data analytics allows organizations and clinicians to gain insight into key metrics and trends. Here, historical data about patients and their health are most often used.
- Discovery analytics. The core value of this type of analytics is the study of clinical data to identify several patterns, thereby obtaining useful information. Often, such a type of analytics uses artificial intelligence and machine learning algorithms to analyze data.
- Prescriptive analytics. This type of analytics is usually applied to propose a strategy. Similar to discovery, prescriptive analytics also makes the most use of AI and machine learning to get things done.
- Predictive analytics. This is one of the most valuable types of analytics in today’s healthcare industry. Data-driven predictive analysis facilitates forecasting and modeling of different scenarios, which allows medical institutions to predict various probabilities and events that may occur in the future. Thus, predictive analytics makes it possible to prepare in advance for bad events and avoid many risks.
Each of these types of analytics uses different tools and categories of data. The primary value of analytics in healthcare is that it provides organizations with actionable insights that enable them to make informed decisions and deliver better patient care plans. Moreover, data analysis helps to prepare for risks, which became especially critical after the COVID-19 pandemic, when many medical institutions were not ready for a large influx of patients.
How Data Analytics Is Used In Healthcare
Data analytics is essential to modern medicine. The collection of different categories of data and their deep analysis makes it possible to assess what is happening right now, what happened in the past, and what may happen in the future.
These three types of questions allow you to make effective decisions and adapt to possible changes in time.
In the case of the healthcare industry, data analytics provides several key benefits:
- It helps to prevent future diseases
- It helps to prepare for possible epidemics
- It reduces the risk of re-hospitalization of patients
- It improves patient outcomes
- It leads to lower health insurance costs
Data analytics provides significant benefits for both medical organizations and patients.
Investment in health software development is essential for today’s medical institutions, as it gives valuable information that can be converted into robust solutions. This approach contributes to the positive dynamics of the development of the healthcare industry and also helps service providers achieve the desired results when it comes to both treatment outcomes and the organization’s management.
At the same time, one of the biggest benefits of data analytics in the healthcare industry is that it helps physicians make better decisions about patient care. Also, the ability to quickly collect and analyze large amounts of data allows you to more accurately predict the development of large-scale events and create plans for the long term.
Another area where data analytics is a valuable tool is public health management. This leads to better clinical outcomes for different patient groups and also helps to improve the interaction between physicians and patients. Here, artificial intelligence algorithms allow the creation of predictive models, thereby helping to promote health initiatives for specific populations. With this approach, physicians can identify the most vulnerable patients to maximize the quality of care for these groups and reduce risks.
Thus, data analytics is one of the most reliable tools to improve the efficiency of healthcare systems, adapt to changes in time and provide better healthcare services and patient care plans.
Data analytics is among the major trends in today’s healthcare industry. The two main reasons this is happening are the digital transformation and the COVID-19 pandemic, which has demonstrated the poor preparation of medical organizations for sudden and massive epidemics.
That’s why the importance of data analysis in healthcare has increased markedly. To cope with existing challenges and improve patient care and positive outcomes, data analytics is an extremely valuable and promising tool that allows you to make informed decisions based on a large amount of data and facts.
- Learn more about the role of healthcare software at Ralabs.
GIS Technology is truly incredible, with features that significantly improve our lives being taken for granted on a regular basis. Sometimes we need to realize that a good idea can become a great idea, and without much additional work. Such is the case for the many emerging outdoor apps that developers are creating for countless new use cases. From gaming, to adventure tourism, to enhanced hiking and exploration, outdoor apps show the marriage of high technology with our desire to step outside and enjoy the larger world around us. For most of these apps, there is some element of geospatial data involved in order to understand where the user is, or help them identify and explore places they’d like to go.
For normal apps, the environment is fairly self contained, but when the activity moves outside, everything becomes much more dependent on the weather. This can be at best frustrating when the weather surprises you as you go out to make use of an outdoors app, and can at worst be dangerous when you are caught unprepared in a situation that could cause harm.
Fortunately, for any developer working on an outdoor app, technology has advanced to the point that creating a real time weather app within your own outdoor app is incredibly easy, and can significantly increase the overall value of the app as it aids the user in an incredibly custom way, showing them their hyperlocal weather report and forecast to help them make the best decisions possible when using your outdoor app. By taking the geospatial data from the outdoor app, along with any additional needed prompts, you can deliver a feature for your app that seamlessly integrates with the user experience, and makes the user feel catered to with customized insights as they enjoy your app.
Creating A Weather API Request for Incorporation Into Geospatial-Based Apps
This tutorial will walk through the various steps to set up a basic API to weather insights/forecasts. We will use Tomorrow.io’s weather API because of its simplicity and customizable options so that you can fine tune exactly what your outdoor app needs.
In order to set up this API-driven weather app (full tutorial and code here), you need to configure the initialization lines shown here (1-3) that create a Node fetch-request. The remaining steps will help to customize and configure this request. While a single use case is shown in this example, if you are going to utilize a weather app with your geospatial driven app, you will need to use the geospatial data to determine the user’s current location and timezone. Other scenarios might include a user looking at other locations around the globe depending on the purpose of the app, and in those cases you can capture the geospatial data in the app and pass it along as inputs for the weather data request.
To use this free API connection, you must first sign up on the Tomorrow.io platform and get a key after you log in.
Select the Location (variable)
This step is flexible depending on how the geospatial app is set up. The weather call can accept either a latlong pair (separated by a comma), or a locationId variable that represents a predefined location (see the list here). Again, this is where the geospatial app will either pull the current location the user is at, or the location a user is interested in. For either case, it will capture the latlong or the locationId and pass it into the weather API call.
Select the Key Fields
The key fields you can request will depend on the context of your geospatial app, but could include several precipitation elements, wind characteristics (speed, gust, direction), temperature, cloud characteristics (cover, base, ceiling), and the weathercode itself.
Choose Unit Type
This item selects either imperial or metric units, and you could choose to pick it based on location, based on the user’s home country, or have the user select the unit type in the geospatial app settings.
The timestep is another context based element, and the data can be pulled for the current weather, the weather by hour, or the weather by day.
Configure the Timeframe
The timeframe allows you to select what type of forecast you want to see, up to six hours prior and up to 15 days out. This allows significant customization to give users exactly what they need to see for your app, and nothing more.
Similar to the location, this should be thought out to see if you want to present the weather as the current location time zone or somewhere else, depending on the purpose of a particular API call.
This final step allows you to compile the full list of query string parameters, then parse out the intervals of the timelines to get the data you want. As the result is raw data, be sure to enrich the result with data units.
Folding in weather updates and forecasts is incredibly easy, and can add significant value to a geospatial data-based outdoor app. As you develop new apps, think outside the box to see how to best enhance the user experience. Chances are, it might be easier than you think to find and incorporate those key insights.
Few small-to-medium sized businesses have the capability of doing any full-scale data analysis in-house, which is why most of them will eventually seek out a third party to do this on their behalf. The term outsourcing developed a negative connotation in the computer industry because it was often associated with organizations that wanted to slash costs through rapid reductions in staff. This kind of aggressive maneuver would often end up with managers receiving very low-quality results back from the groups they worked with.
Today it’s possible to hire high-end data science personnel on a contract basis. Small business owners who can’t afford to maintain their own major analysis projects should be able to locate professional evaluation consultants who can crunch their numbers for them and return an accurate result that makes sense for the kind of business they’re involved with. Hiring representatives from a service that’s dealt with the particular sector someone works in is perhaps the best way to reduce the risk of receiving subpar work back.
Reaching Out to Professionally Trained Staff
Human resource department personnel should take the time to look over the credentials of any service provider that they plan to work with. Treating this with the same attention that would be used when interviewing a potential hire. Around 37% of all small businesses already outsource at least some of their workflows, which means that there’s a good chance they have experience in the field and know what to look for.
Data analysis may be a complex field that involves a great deal of specialized knowledge, but that doesn’t mean that the process of outsourcing this kind of work has to be any different from any other field. New developments in outstaffing procedures make it easy to pick through individual offshore candidates regardless of where they are in the world. A majority of data science organizations in the Indian subcontinent, Eastern Europe and Southeast Asia now offer some form of video conferencing service that enables near instant communication with the professionals curating a particular database.
Education and experience are always major concerns when outsourcing work to an outside organization, but those who are reviewing potential opportunities shouldn’t let conventional paradigms stand in the way of acquiring new talent. A large number of computer science experts earned their experience in non-traditional ways. Look for those who were involved in on-the-job training or attended an orientation session. Indian prospects may have taken a data science course in Bangalore while those in some international locations may have primarily studied online. These candidates may very well be exactly what you’re looking for.
Setting Realistic Expectations
Offshoring operations were notorious for overpromising and then underdelivering, which made companies somewhat distrustful of the practice. Others have a tendency to set their sights too low, which is potentially dangerous when working in the data science field because managers will more than likely base their decisions on the outcomes of studies conducted by the analyst. Never settle for less than what’s actually required, but ensure that there’s plenty of time for the organization accepting the outsourcing contract to complete all of the work in question. Newer data science training programs, like those being tested by Bangladeshi authorities, are focused on increasing the current talent pools in order to cut down on timing delays.
That being said, companies that expect to shift all of their data management and analysis workflows to an outside provider should expect to do at least some refactoring during the first few weeks. Choosing a solid cloud service that’s compatible with the organization being outsourced to will help to smooth things out. Firms that currently have little or no experience in making data-driven decisions will also want to give some thought to what kind of analysis work they need done.
Analysis Services Offered by Offshore Organizations
A majority of outsourcing services have focused on software development, but data science is probably the fastest growing segment of the market. Those who adhere to any of the major enterprise planning systems should find at least one professional experienced enough to work with them. Companies that have their own in-house problem structuring methods will want to discuss these with any service they plan on contracting with.
Nevertheless, most techniques like SWOT analysis and Porter’s four corners model are going to be covered by most data analysis firms offering services to an international audience. Relatively few organizations are going to want to drastically alter their existing modeling methods just to make it easier to outsource their data analysis procedures. However, this might make sense for companies that have no data science personnel and need to reduce costs.
Considering just how many international brands are providing additional data management services these days, this may end up being a much more popular option.
Data science has emerged as a rapidly growing field fueled by the proliferation of data in our digital age. Organizations across industries leverage data to make informed decisions, optimize operations, and gain a competitive edge. As a result, the demand for data scientists, or professionals with certifications like PGP in data science, who possess the skills to extract insights from data, has skyrocketed. While data science requires proficiency in multiple areas, including programming, statistical analysis, and domain knowledge, one critical question aspiring data scientists often ask is: “How much mathematics do I need to know to get into data science?”
In this article, we will delve into this question and explore the role of mathematics in data science.
Mathematics is the foundation of data science, providing the tools and techniques to analyze and interpret data. It is the language of quantitative analysis, enabling data scientists to understand patterns, relationships, and trends in data. While not all data science roles require advanced mathematical knowledge, having a solid understanding of key mathematical concepts can greatly enhance a data scientist’s ability to analyze data effectively and extract meaningful insights.
Foundational Mathematical Concepts in Data Science:
- Probability and Statistics: Probability and statistics form the cornerstone of data science. Probability theory is used to model uncertainty and randomness in data, while statistical techniques are employed to analyze data and make inferences. Understanding concepts such as probability distributions, hypothesis testing, regression analysis, and sampling methods is crucial for data scientists to interpret data accurately and draw conclusions.
- Linear Algebra: It is the study of vectors and matrices, and it is an essential mathematical tool in data science. Many data science techniques, such as machine learning algorithms, involve working with large datasets that can be represented as matrices. Understanding concepts such as matrix operations, eigenvalues, and eigenvectors is essential for tasks such as data transformation, dimensionality reduction, and feature extraction.
- Calculus: It is the study of change and motion, and it is used in data science for tasks such as optimization and machine learning algorithms. Understanding concepts such as derivatives, integrals, and optimization methods is essential for data scientists to develop and implement advanced algorithms that can efficiently process and analyze data.
- Discrete Mathematics: Discrete mathematics deals with objects that are distinct and separate, such as integers, graphs, and networks. It is used in data science for tasks such as graph theory, network analysis, and combinatorial optimization. Understanding concepts such as graph algorithms, combinatorics, and set theory is valuable for data scientists working on tasks that involve analyzing data with a discrete structure.
Advanced Mathematical Concepts in Data Science:
In addition to foundational mathematical concepts, data scientists may also encounter advanced mathematical concepts in specific areas of data science. For example:
- Machine Learning: This subject is a subset of data science that involves building predictive models from data. Many machine learning algorithms, including support vector machines, neural networks, and decision trees, rely on advanced mathematical concepts, such as optimization, convex optimization, and probability theory. Understanding these concepts is crucial for data scientists working on machine learning tasks, as it enables them to develop and implement sophisticated algorithms that can accurately predict outcomes from data.
- Deep Learning: This topic is a subset of machine learning that focuses on building artificial neural networks to mimic the human brain’s ability to learn from data. Deep learning algorithms, including convolutional neural networks and recurrent neural networks, require a solid understanding of advanced mathematical concepts, including calculus, linear algebra, and probability theory. Deep learning is widely used in applications such as natural language processing, image recognition, and speech recognition, and understanding the underlying mathematical concepts is essential for data scientists working in these domains.
- Bayesian Statistics: This branch of statistics deals with updating probabilities based on new data. It is widely used in data science for tasks such as model selection, parameter estimation, and uncertainty quantification. Bayesian statistics involves complex mathematical concepts, such as Bayes’ theorem, prior and posterior probabilities, and Markov Chain Monte Carlo (MCMC) methods. Understanding Bayesian statistics is essential for data scientists who work on probabilistic modelling, Bayesian machine learning, and Bayesian inference tasks.
- Signal Processing: Signal processing is a field of mathematics that deals with analyzing and processing signals, such as audio, video, and sensor data. It is widely used in data science applications, such as speech recognition, image processing, and time-series analysis. Signal processing involves advanced mathematical concepts, such as Fourier analysis, wavelets, and digital filters. Understanding signal processing techniques and their underlying mathematical concepts are crucial for data scientists working on tasks that involve analyzing and processing signals.
The Importance of Mathematical Fluency in Data Science:
Having a solid understanding of mathematics is crucial for data scientists for several reasons:
- Robust Analysis: Mathematics provides the tools and techniques to perform a robust analysis of data. Data scientists need to analyze data accurately, draw conclusions, and make decisions based on statistical significance. Understanding mathematical concepts such as probability, statistics, and linear algebra enables data scientists to analyze data effectively and draw meaningful insights from it.
- Algorithm Development: Many data science tasks, such as machine learning and optimization, rely on advanced mathematical algorithms. Understanding the mathematical concepts behind these algorithms allows data scientists to develop and implement complex algorithms that can efficiently process and analyze data.
- Problem-Solving: Data science involves solving complex problems and making data-driven decisions. Mathematics provides the foundation for problem-solving skills, as it teaches logical reasoning, critical thinking, and quantitative analysis. Having strong mathematical skills enables data scientists to approach problems systematically and develop effective solutions.
- Domain Expertise: Data science often involves working with domain-specific data, such as healthcare, finance, or marketing data. Understanding the mathematical concepts related to the specific domain allows data scientists to understand the data better, identify relevant patterns and trends, and develop domain-specific models.
In conclusion, mathematics plays a critical role in data science, providing the foundation for understanding and analyzing data. While not all data science roles require expertise in advanced mathematics, having a solid understanding of foundational mathematical concepts is crucial for data experts to analyze data effectively, develop algorithms, and make data-driven decisions.
Additionally, advanced mathematical concepts may be required in specific areas of data science, such as machine learning, deep learning, Bayesian statistics, and signal processing. Aspiring data scientists should invest time in building their mathematical fluency to enhance their analytical skills and excel in the field of data science.
Today, business analytics is a rapidly growing field. The demand for business analyst services and qualified analysts is growing. It takes a certain set of skills to make it as a business analyst. With them under your belt, you’ll be well-equipped to launch new ventures and climb the corporate ladder as a professional business analyst. Everything you need to know to find work in this competitive field is included in this article.
- Comprehending the Business Goals
- Analytical and Critical Thinking
- Negotiation and Cost-Benefit Analysis
- Decision-Making Skills
- Verbal and Nonverbal Communication
- Software Development Languages
- Database and SQL
- Proficiency in Microsoft Excel/Google Sheets
- Data Visualization and Report Generation
- Records and Presentations
A business analyst’s versatility necessitates a well-rounded mix of technical and non-technical abilities that make up their top-tier business analytics skill set. In this article, we’ll discuss the top 10 most important business analytics abilities for IT professionals.
#1 Comprehending the Business Goals
A business analyst has to understand the organization’s goals and problems. They need to be able to see problems in the company and choose the best way to fix them. A business analyst’s domain competence should include the business they are analyzing. This will help them get the job done faster and better. Understanding the company’s motive is the first step in becoming a successful business analyst. In addition, these specialists are responsible for conclusively deciding whether or not a given technology design meets the business criteria.
#2 Analytical and Critical Thinking
Thinking is sometimes underestimated because of its seeming simplicity. The ability to analyze and evaluate information critically is a vital skill for every business analyst. Business analysts are responsible for understanding and communicating their clients’ requirements. A business analyst may use critical thinking to evaluate the qualities of many options before settling on a course of action. Analysts in this field pay close attention to the needs of their customers. Thanks to their ability to think critically, they are able to prioritize company needs. An analytical mindset allows to achieve the goals even when resources are limited, and environmental factors are not perfect.
#3 Negotiation and Cost-Benefit Analysis
Bargaining is obviously an important ability for any business analyst to have. As with any other profession, business analysts are always haggling. In the beginning phases of a project, skilled negotiators are used to identify which goals and objectives are absolutely essential. As a further step, business analysts use their negotiating skills to choose which suggestions should be formalized as requirements and which should be given lower priority.
#4 Decision-Making Skills
The decisions made by a business analyst have direct and indirect effects on the running of a business. Therefore, they should weigh all the options before making that decision. Before offering a recommendation, a business analyst investigates the problem at hand and catalogs all viable alternatives. Then, they put each potential course of action to the test and make a decision based on their thoughts of the results. At long last, they tried out the solution. In addition, business analysts are responsible for conclusively deciding whether or not a given technology design meets the above business criteria.
#5 Verbal and Nonverbal Communication
To effectively communicate with consumers and stakeholders, you must clearly and simply express the requirements at hand. A business analyst’s verbal and nonverbal communication skills will be put to the test at numerous points during a project, including the early phases when requirements are being gathered, dealing with stakeholders, validating the final solution, and so on. Business analysts engage in verbal and written exchanges with stakeholders to convey and receive information, ideas, and perspectives. Strong verbal and nonverbal communication abilities will provide a business analyst with more confidence while conducting meetings.
#6 Software Development Languages
For efficient and timely data analysis, business analysts need to have strong programming skills. Knowledge of R and Python may be quite useful. Complex problems may sometimes be tackled with the help of well-crafted computer code. R and Python both provide several packages and tools that may be used for data cleaning, manipulation, visualization, and analysis. Expertise in statistical software, such as SAS or SPSS, is also recommended. Such granular analysis and display of massive data sets is made possible by the aforementioned computer languages. Business models may also be used to make predictions about the future of a company.
#7 Database and SQL
Structured data is the gold standard for business analysts. To effectively manage this mountain of information, they need to be conversant with traditional relational databases like Microsoft SQL Server, MySQL, and Oracle DB and the more modern NoSQL database. An essential skill for every business analyst is hands-on expertise with SQL. They may use this to get entry to data, retrieve it, edit it, and examine it. They need to be able to define data, remove it, choose it, modify it, and add to it, among other things.
#8 Proficiency in Microsoft Excel
To develop revenue growth models for new products based on existing consumer predictions, Excel is used to establish an editorial calendar, list expenses for items, generate charts to demonstrate how close the product is to budget across each category, and manage the product’s inventory. Discounts for customers are calculated in Excel by business analysts each month based on total monthly product sales volume. They even aggregate client revenue by product to see where more in-depth engagement with customers is needed.
#9 Data Visualization and Report Generation
Business analysts need to have a strong grasp of a wide range of BI tools to construct meaningful reports and visualizations. Business analysts make regular reports and interactive dashboards to aid in decision-making. A thorough familiarity with Tableau, QlikView, and Power BI is necessary for developing a variety of reports to meet organizational requirements.
#10 Records and Presentations
To do their job well, business analysts need to report on project outcomes and lessons learned in a way that is both concise and clear. They need to exude confidence while discussing the project’s outcomes with clients and other stakeholders. With the use of structured documentation, business analysts may easily communicate technical ideas to non-technical staff members. Recording and reporting project learning is essential since doing so will help them make better decisions in the future. Business analysts may save time and avoid mistakes by revisiting previously resolved comparable problems.
We have now gone through each of the 10 abilities we identified as essential for a successful business analyst.
You should now know that it takes a lot of expertise to be a competent business analyst. But if you feel incompetent in business analysis and need assistance analyzing your project, Agiliway has the required abilities and expertise to assist you; just contact us and one of our experts will get back to you.
Thanks to the desire and anticipation of changes, insurers can make the future with their own hands. Insurers are demanding faster transactions, both payments, and payouts, and more and more consumers want to work directly with insurance companies without intermediaries. This shift is due to the development of technology because the smartphone has provided consumers with a mobile device that meets their requirements.
5 Key Trends That Will Affect the Global Insurance Sector in the Next Decade
- Social Sphere: all forces go to the client.
- Technological: advances in software and hardware that turn “big data” into useful insights.
- Environment: Emergence of more sophisticated risk and risk transfer models to address the increasing severity and frequency of catastrophic events.
- Economic: growth of economic and political power in emerging markets.
- Political: globalization, harmonization, and standardization of the insurance market. It is also possible for insurance software development.
These trends will intensify, leading to a situation where customers will be more willing to buy “direct”, using their online and offline network of “trusted” friends and family to guide their choices. This will lead to a fundamental rethinking of the role of “recommendations” of intermediaries and the disappearance of distributors as a sales channel.
From a reactive business model to preventive: commercial insurers are already ready to use connected devices and sensors for the development of risk management and loss and increase in performance, but there are assumptions that life insurers will also use them.
Technological Developments that may Impact the Insurance Industry
- The rise of smartphones and tablets, coupled with cloud computing, provides constant access to the internet.
- Rapid growth in computing power and storage enables the accumulation and analysis of extremely large amounts of data.
- Growth in the number of active sensors and devices connected to the Internet.
Not all new technologies need to be used to grow a company. Knowing these developments will help you communicate with clients who will feel more comfortable working with someone who understands their concerns.
Advances in artificial intelligence technologies such as machine learning, natural language understanding, and intelligent decision-making will enable insurers to move from using technology to processing transactions to making decisions. Today, analytical methods are used to make ad hoc decisions using structured data.
In 2023, the use of unstructured data (such as social media, devices, video, and audio) successfully complements structured data, enabling insurers to make strategic forward-looking decisions.
Insurance Sector and Data
The insurance industry has always used, first and foremost, internal data in a structured format to make operational decisions about which customers to target, how to assess risk, and how to assess losses. Now, non-insurers will increasingly use large amounts of real-time sensor data, unstructured social media data, and multimedia data such as text, voice, and video.
As sophisticated AI techniques evolve, insurers will begin to use this unstructured data to make forward-looking strategic decisions, such as which product or solution is best for a customer given their current and future situation, which developing countries to enter, and when and how to proactively manage customer experience to retain the most profitable customers and get rid of unprofitable ones. Insurers that can leverage real-time “big data” and advanced predictive modeling techniques will gain a significant competitive advantage. At the end of 2021, life insurance company Northwestern Mutual signed insurance for 18.8 billion U.S. dollars.
Another adaptation that needs to happen for continued growth in the insurance industry in 2023 is understanding what it means to ensure people are in an on-demand economy. The technology of “insurance on demand” is a new challenge for traditional insurers. Inshurtech startups have long offered such products on a turn-on/turn-off basis.
Data and technology to remain competitive in the insurance industry.
As technology improves, competitors in the insurance market will have the same advantages and opportunities, but success will depend on those who first use them. To use this gift of technology, you need to analyze the data that are collected from consumers. Knowing this data will help insurers update underwriting and insurance tariffs.
The Value Chain is the series of activities a company goes through to create and deliver a product or service to its customers. It includes everything from procuring raw materials to distributing the finished product. Optimizing your value chain helps increase efficiency, reduce costs, and improve the overall quality of your products and services.
One way to optimize the value chain is through cloud computing. This computing approach involves delivering computing services and resources, such as storage, processing, networking, and software, over the internet. It allows companies to access and use these resources on a pay-as-you-go basis rather than investing in and maintaining their infrastructure.
Read more to discover several ways cloud computing can optimize your value chain.
7 Ways to Optimize the Value Chain Through Cloud
Here are some ways that cloud computing can help optimize the value chain:
- Communication and Collaboration
Cloud-based tools such as Google Workspace and Microsoft 365 allow teams to collaborate and communicate in real-time, regardless of location. This can improve the efficiency of the value chain by enabling faster decision-making and reducing the time it takes to complete tasks.
For example, a company might use a cloud-based project management tool to monitor project progress and share updates with team members. Doing so will ensure that every team is on the same page and that tasks are completed on time.
- Data Analytics
The cloud offers a centralized location for storing and analyzing data. Companies can use big data analytics tools to analyze large chunks of centrally-located data to generate meaningful patterns and trends. As a result, business leaders make more informed decisions about their value chain by providing insights into various business areas such as demand, supply, and cost.
For instance, a company might use a cloud-based data analytics platform to track sales data and identify trends and patterns. This can help the company make more informed decisions about production and inventory levels, which can help optimize the value chain.
- Supply chain management
Cloud-based supply chain management systems help companies track and manage their inventory, orders, and deliveries in real time. These systems help reduce the risk of delays or disruptions in the value chain.
For example, a company might use a cloud-based supply chain management system to track the shipment of goods from supplier to customer. This can help the company identify bottlenecks and other issues causing delays and take steps to address them.
Cloud computing can help companies automate specific tasks and processes, such as invoicing and billing. This can help reduce the time and effort required to complete these tasks, optimizing the value chain.
For instance, your company can use a cloud-based invoicing system to automatically generate and send customer invoices. Doing so will help streamline the billing process and ensure that invoices are sent out on time, which can help to improve cash flow.
The cloud can help companies scale up or down quickly and easily based on demand. You can build highly scalable enterprise applications that adapt quickly to changes in the cloud. This can help you optimize the value chain by ensuring you have the right resources at the right time.
For example, a company might use a cloud-based infrastructure to scale up its computing resources during peak periods and then scale them down during slower periods. This can help ensure the company has the resources to meet emerging customer demand without incurring unnecessary costs.
- Customer Relationship Management
Cloud-based customer relationship management (CRM) systems allow companies to manage customer interactions and data in one place. These systems provide a single view of the customer and enable personalized interactions. Brands can also incorporate customer feedback to improve their products. As a result, it enhances customer satisfaction and loyalty, leading to increased revenue.
There are several other benefits to using the cloud to optimize the value chain. One is the ability to access resources on a pay-as-you-go basis. This helps you reduce upfront costs and avoid investing in expensive infrastructure. Additionally, the cloud can minimize the risk of data loss or downtime, as data is typically stored in multiple locations and can be quickly restored should a disaster occur.
- Financial management
Cloud-based financial management systems allow companies to track and analyze financial data in real-time. As a result, business leaders can make informed business decisions that optimize financial performance.
What to Consider When Optimizing Value Chain Through Cloud
There are multiple considerations to keep in mind when implementing cloud solutions to optimize your value chain:
It is crucial to ensure that data is secure when it is stored in the cloud. Companies should carefully evaluate security measures and consider implementing additional actions as needed.
The last thing you need is to expose your sensitive data to unauthorized parties in the cloud. Therefore, companies should protect data privacy when using cloud services, mainly if the data includes sensitive information such as customer or financial data. They should take measures like encrypting the data to ensure that their sensitive data observes the three fundamental tenets of data security, confidentiality, integrity, and availability.
When implementing cloud solutions, it is crucial to ensure they are integrated with existing systems and processes. Evaluate your IT infrastructure and environment to help minimize disruptions and ensure a smooth transition to the cloud.
It is essential to carefully evaluate potential vendors to ensure they can meet the company’s needs and provide the necessary level of support. Companies should also consider the vendor’s long-term viability to prevent potentially costly vendor lock-in.
Optimizing your value chain enhances efficiency, reduces the wastage of resources, and improves the overall quality of your services. Cloud computing can be an effective way to optimize your value chain. It enables you to store and analyze data, collaborate and communicate, manage the supply chain, and improve customer relationships.
Thanks to its advanced technologies and flexibility, you can scale your services to meet emerging customer demands. However, when implementing cloud solutions to optimize your value chain, you should consider various factors, such as data privacy, integration requirements, and security. Doing so will ensure a seamless and secure cloud transition.
Marketing intelligence platforms pull publicly available data from multiple sources and provide organizations with a database for viewing the information.
What is marketing intelligence?
Marketing Intelligence is data crucial to an organization’s marketing efforts. When businesses collect this data, they can effectively analyze it to apply it to decision-making. It supports multiple marketing objectives and aids in making informed decisions regarding competitors, products, and consumer trends and behaviors.
How can market intelligence transform your business?
Market intelligence covers a lot of things. It includes all the information your company gathers about the markets it works in. The data can include customer demographics, trends, rules, competitors, geographic areas, etc. And your company gets useful information from that data to help it figure out and improve market segments, brand recognition, and growth prospects.
Market intelligence gives a full market picture because it includes competitive, product, and business intelligence. So, you can see the big picture, connect the dots, find insights, and make better business decisions based on data. That’s the reason 26% of companies adopted market intelligence in their business globally.
Benefits of marketing intelligence in your business
- Gain a comprehensive overview of the market.
A good market intelligence plan provides your company with a continuous market overview. And, with an overview of the market, your company can stay adaptable in unpredictable marketplaces, quickly pivoting as necessary.
- Gain a competitive advantage
One form of market intelligence is competitor intelligence, and its primary purpose is to provide insight into how your company stacks up against its competitors. You can keep an eye on your competitor, enhance your offers and positioning, and reach and acquire more of your target market if you combine competitor data with your other market intelligence.
- Prepare for the future.
Your marketing teams can monitor, predict, and act on market opportunities before market changes and effects if they have access to market intelligence.
- Invest in the right projects
When your company specializes in market intelligence, it can minimize losses and invest in the correct projects. The goal is to store all the market intelligence in one place to get a complete picture of the market, clients, and competition. As a result, the company can effectively validate and invest in new initiatives, products, and services while reducing time and resource waste.
Types of Marketing Intelligence Platforms
Marketers can get actionable market intelligence in several ways. But here are the most common ways to get a better understanding of the market:
Polls are designed to answer a single question. They are the opposite of open-ended questions seen in other approaches. Polls may be answered quickly and easily, resulting in a greater response rate.
- Focus groups
Focus groups typically choose a small number of people to produce a sample size of their target market. Then, planned questions are given to inspire further group discussion. Marketers get comprehensive insights into their audience’s opinions, helping them make better-educated judgments about future promotions.
- Surveys by Mail
This is another low-cost method for a marketer to reach many people. It works well for organizations that want to get people who don’t have access to technology.
Questionnaires make it easier for marketers to reach a larger audience. This lets marketers learn about their customers in both qualitative and quantitative ways. You can fill out the questionnaires online or in person.
Benefits of using a market intelligence platform:
A leading market intelligence platform can benefit your business in the following ways:
- An Overview Of The Market
Using real-time data, this platform enables businesses to comprehend their market with ease. Consequently, companies can remain competitive and meet market expectations as their plans and procedures are woven around the data.
- Improves Sales
Most companies struggle with selecting the correct target audience and determining the product’s success. However, with market information, companies can readily determine market segmentation.
- Competitive advantage
Marketing Intelligence gives you a better competitive advantage. It provides data about your competition, emerging trends, and full market analysis. You can, therefore, constantly be one step ahead of your competition.
- Customer retention
Marketing intelligence for client retention aids in the study of repeat buyers. It helps retain clients and increase their lifetime value by giving you information on what parts of your business need work.
As with any investment, businesses should use a marketing intelligence platform that gives them a lot of data about marketing online, offline, and in the real world. This gives them an edge over their competitors and lets them learn about upcoming market trends and much more. It is, without a doubt, a very important part of any business’s success.
Here is an unpleasant yet rather mundane scenario. One day you go looking for an important file — and alas, you discover that the hard drive, which contains it, has been formatted. Maybe you formatted it along with other disks, maybe you decided that those files were obsolete, or mistakenly hoped that you have a backup somewhere safe. Whatever the reason behind this scenario, the grim reality is that instead of having an important file, you find yourself essentially file-less.
Nonetheless, you should not panic. There are numerous ways to deal even with such an unpleasant situation. We tend to think that formatting destroys all traces of data on the disk. However, it is not necessarily true. On the contrary, you can attempt to recover formatted hard drive data with specialized recovery utilities. Armed with versatile utilities and useful knowledge, you may be able to restore necessary data even.
Formatting: How Does It Work?
Firstly, we need to understand its exact consequences. How does it erase your files? In most cases, quick formatting does not transform the files themselves. It merely manipulates the referencing system, deleting the labels of the files. Hence, the files themselves remain on the very same disk, unchanged yet forgotten by the system.
Therefore, should you be able to restore those labels, which provide the system with the links to the files stored on the hard drive, you would also recover the said files. Now we know that formatting does not always erase data from the disk: instead, it often merely destroys the map, which contains their exact coordinates, especially when we are talking about the quick formatting process.
However, we also delete such files by overwriting them with new ones. In this case, the system actively changes the configuration of the hard drive, creating new structures. Because of this, you should immediately stop using the hard drive with lost data. You may have a decent chance to restore valuable data but the complexity of such a task will increase manifold after overwriting old files with new ones.
Specialized Tools for Data Recovery
Hence, how to recover files after formatting hard drive? You will have to use a specialized software tool capable of scanning your hard drive and finding traces of seemingly destroyed files. Choosing such a program is quite a task, which requires careful planning and consideration. Here you can find a detailed guide on the best available programs that may help you recover files from a formatted hard drive. Some of these tools allow you to restore entire partitions whereas a few of them come with a targeted scanning option, which makes it possible to look for specified files while also ignoring all others.
However strange it may sound, it is typically easier and faster to restore an entire partition instead of one single small document. Why does it happen? When you are looking for specified files, the program has no other choice but to scan the entirety of the disk. On the contrary, when dealing with entire partitions the program may skip long scanning: it only has to find both the beginning and the end of the partition. By recovering it, the program will also restore all the files located inside. Of course, it works only if all the files remain on the disk.
Fortunately, the contemporary market can boast of a great number of various utilities for formatted hard drive recovery. Although such an abundance creates its problems (it may be difficult to find the best option among dozens), you may also look for guides on how to choose the most finely tuned hard drive data recovery software.
However, it also happens that the hard drive resists all attempts to obtain access to valuable data. Such situations are not as common as they may seem, and in most cases, powerful recovery tools provide users with optimistic chances of restoring their data. Regrettably, your chances to restore important files remain minimal in the following situations:
- Extensive damage. Hard drives, possibly except for specialized models, are very fragile. Extreme temperatures, hits and smashes — all these factors directly decrease the safety of your data. Once your disk has been physically damaged, your chances of restoring data from it drop to almost zero. If such is the case, you may want to contact experts in the sphere of data recovery. Nonetheless, the chances of successful restoration remain minimal.
- Secure formatting. Instead of the standard approach, this process not only erases the pointers, which tell your operating system about the location of all files in the partition but also overwrites the disk with new random data. Hence, it deletes files not only from the reference system but also from the disk.
- SSD TRIM. TRIM is a wonderfully convenient feature, which greatly increases the performance of your SSD. The sad reality is that it also wipes out all erased files from the disk. This option is active by design: once you enable TRIM, you can forget about all deleted files on the disk. As it often happens, better performance comes at the price of data safety.
As we can see, a patient user with a necessary set of software tools may successfully recover lost data from a formatted hard drive. However, it does not mean that you can forget about backups and diversification of crucially important materials. The best way to deal with a problem is to prevent it from happening. Advanced data security protocol requires a multilayered approach, which balances both the application of modern data recovery tools with regular backups.
Solutions Architect Data Analytics Core can seem overwhelming to newcomers, especially for those who are not familiar with the subject. To do the job right, you must be familiar with many terms, processes, standards, and concepts. This guide will assist you in solving problems and developing business cases to help improve your knowledge of this essential skill.
Solution architect – Data analytics – Core Review
The State of the CIO Report examines the current issues and concerns of CIOs. It also highlights the secrets and skills of highly innovative CIOs. Solution architects are experts in technology and business architecture. Solution architects usually have a defined project scope that defines the specific tasks and goals of the product.
A data analytics solution will only succeed if it has a good solution architect. The architect should be able to understand the company’s technological and business environments. He should be able to identify the problems facing the company and decide which solution is best. He should also be able design a solution that suits the needs of the company. Solution architects should be passionate about data analytics and be able explain the various concepts.
To create the best solution, a solution architect should be familiar with enterprise architecture. A company’s architecture is composed of several layers such as business processes, integration requirements and operating systems. An effective solution architect must also be familiar with the business model, operating system, and architectures. He must also have a good understanding of data models and business processes.
The solution architect must be able to identify the constraints and work with customers to resolve them. These credentials will enable the solution architect to prove that they have the qualifications to implement a specific technology.
Data Automation is key to modern business efficiency!
What is Data Automation?
Data automation is a process of using data to make decisions. It has the potential to increase productivity, reduce costs, and improve decision-making.
The concept of Data Automation was first introduced by Professor Thomas Davenport in his book “Only Humans Need Apply” in 2013. He defines Data Automation as “using data processing and analytical tools to replace repetitive tasks.”
Data automation can be used in many ways such as: improving customer service, reducing costs, generating insights from data, or improving efficiency and quality of products.
Automated data capture is on the rise
It is essential to have a simplified process for collecting business data as your industry becomes increasingly competitive. Low costs and maximum efficiency are key ingredients to competitive advantage.
Datatime will help you accomplish this by outsourcing your document data automation.
Data Automation: Why should you invest in 2022-2023?
We are closer to becoming a fully digital workforce as we reduce the amount of paper we use in the office each year. However, paper-based documents still have their place. You need to be confident that your data will be captured in a structured, efficient and error-free way.
A clean bill of data will help you remain competitive in any industry.
Data automation can make your organisation more efficient and ensure that you always have the most up-to-date information in real-time.
Automate your documents now and save!
How data automation works?
Efficiency is achieved through automation
The data automation process is used to extract, cleanse, and transform the data. It is a process that automates the extraction of information from various sources.
Data is extracted from a variety of sources such as databases, web services, files, and spreadsheets. This process can be done manually or with the help of a tool. After extracting the data, it needs to be cleansed or formatted in order to make it easier for analysis. The last step in this process is transforming the data into a format that makes it easy for analysis.
1 – Choose your documents
You can choose which type of business document to automate, including:
- Accounts payable
- Shipping documents
- HR / Legal Documents
- General Business Documents
2 – Redirect us
We can redirect your documents, including using one of the following methods:
- Daily Mail
- Email redirection
- Documents in hardcopy
Send them in bulk to us
3 – Conversion & verification
Multiple methods are used to verify and convert data.
4 – Send it to you
We will send the data to your accounting/ERP in the format you prefer.
TOP 4 Benefits of Data Automation
Multiple verification steps and the most recent technology are used to make sure that all data is accurate.
To ensure quick turnaround, we have invested heavily in the most recent technology and trained our staff extensively.
Do not invest in technology and employ multiple people to capture your data. Outsourcing is more cost-effective and reliable.
You can be sure that your data will always get returned to you in a consistent and high-quality manner because we use the same processes.
Find and hire good Data automation company
Data automation company can help you to automate your data entry tasks.
The world is moving towards automation and data automation is the future of business. It will not only save your time but also help you to improve the accuracy of data.
Across the globe, big data and AI have become central factors that drive business performance. In fact, almost 98% of all businesses are investing in big data to some extent, demonstrating the huge appeal this industry holds. From generating real-time analytics to helping create data-driven strategies, data in business is absolutely everywhere.
With the need for data at an all-time high, many companies are now using more than just one singular data platform to manage all of their data. Two common data architectures are data warehouses and customer data platforms. While both of these platforms are popular, people in business commonly mix them up – or simply think that they’re the same thing.
In this article, we’ll turn toward these data infrastructures. We’ll discuss what each one is, what they’re used for, and how data warehouses are distinct from customer data platforms.
Let’s get right into it.
What is a Data Warehouse?
Think of a data warehouse like a huge online storage facility. Data is a digital currency, and must be stored electronically. To house all of your company data, you’ll create a massive warehouse where each individual file can rest securely. This is what a data warehouse does, acting as an integrated source of information.
Alongside just storing data, employees are also able to query information within a warehouse to build up analysis, with this acting as one of the most fundamental business tools that companies will rely on in the modern age.
Commonly, businesses will partner with a leading cloud data warehouse to outsource this element of their company. Instead of having to construct the infrastructure themselves and pay the ongoing upkeep. If we look at a comparison of two of these services, Apache Pinot vs Druid, we can instantly see the range of benefits partnering with a cloud data warehouse offers.
With how accessible these businesses are, cloud data warehouses are now a go-to tool for companies across the globe.
What Is a Customer Data Platform?
On the other hand, a customer data platform (CDP) is a singular piece of software that pulls in data from many sources at once. A typical CDP will collect data from different areas of a business, focusing on any data that is directly to do with the customer. This could mean that it takes data from the company’s website, Facebook ads, and email marketing statistics.
Instead of having information about all three of these things in different areas, a customer data platform will integrate absolutely everything into a singular location. From there, data analysts are able to use this collected data to create insights about customers.
Whether it be finding what marketing works with your audience or segmenting users into different groups, a customer data platform will pave the way in terms of customer-related data insight.
What’s the main difference between a customer data platform and a data warehouse?
Looking at these two data infrastructures, it can be easy to assume that they both do the same thing. While they do indeed collect many forms of data into one location, they focus on slightly different things. A customer data platform even has a hint in its title, only wanting to collect customer data. It doesn’t care about other metrics, only collecting information that is directly related to the customers and how they relate to a business.
Data warehouses, on the other hand, collect absolutely everything. No matter whether the customer is involved or not, a data warehouse will contain everything a business comes across. As a catch-all service, a data warehouse could contain industry trends, business financial data, user information, and even customer data as well.
In short, customer data platforms are much more specific forms of data warehouses, focusing only on the customer-facing side of the business. Due to this, they have a limited functionality when compared to data warehouses. While an analyst could use a data warehouse to come up with the same insights as one using a customer data platform would generate, the opposite is not true,
With this in mind, it’s much more common to see businesses using data warehouses rather than customer data platforms. While customer data platforms are useful, their limited utility in comparison is slightly less impressive.
What are Customer Data Platforms typically used for?
With the limited utility of customer data platforms being restricted to customer-facing analysis, they can’t do nearly as much as a data warehouse. Yet, what they can do is actually incredibly impressive.
There are three main uses for a customer data platform:
- Unification – Customer data platforms are fantastic at incorporating many sets of data into one location. Considering customers could be accessing a business from mobile, desktop, or laptop, there are many different streams of information to manage. CDPs unify all this customer data into one place, making analysis much easier.
- User Segmentation – Marketing campaigns work much better when they’re personalized. In fact, 72% of customers say they want to see products that are specifically catered to their interests. By collecting customer data into a CDP, marketing teams can find user segments, then create individual groups that they can launch highly-targeted marketing on.
- Marketing Information – CDPs also provide a stable platform from which businesses can launch marketing efforts. With integrated data, this is the perfect place to conduct real-time outbound marketing, mostly in the form of email marketing. With all the customer data in one place, teams can create highly-specific marketing emails and send them directly from the CDP. This saves time and allows businesses to become even more efficient when it comes to interacting with their customers.
Across these areas, marketing teams can actually get a lot out of CDPs, making them far from a useless tool. As customers are the lifeblood of B2C businesses, tools to track engagement and interest are vital for ensuring a company’s long-term success.
While these data tools are both vital for businesses that want to collect, store, and engage with the data they generate, they are far from the same thing. Although both collect data, a customer data platform only focuses on data related to customers and their activity within a business.
A data warehouse will focus on every data type and set that you send its way, with this being a much more comprehensive form of data collection and storage. While both are popular, data warehouses have more utility and are therefore used more in modern business.
Consumers are constantly bombarded with advertisements for different types of goods and services. The variety of alternatives is overwhelming. But what exactly makes customers pause and take notice of it?
World brands grow more inventive as a result of trying to find a solution to this challenge. In reality, a lot of people are exploring the advantages of big data. For instance, Starbucks began utilizing AI in 2016 to contact consumers with personalized offers. The business utilizes its loyalty programs and applications to gather and analyze clients` data, including where and when transactions are made, in addition to tailoring beverages to suit individual tastes.
Big data analytics is not a new term. Although the idea has been around for a while, the initial big data analysts utilized spreadsheets that they manually entered and then examined. You can probably guess how much time that procedure used to take.
The standards surrounding big data have altered as a result of technological innovations. Modern software solutions significantly shorten the time required for analytics, enabling businesses to make choices quickly that boost growth, save expenses, and maximize revenue. This gives brands that can respond more quickly and effectively target their customers a competitive advantage.
Here are some advantages that a brand contemplating investing in big data analytics may experience:
1. Attracting and retaining customers
Organizations need a distinctive strategy for marketing their goods if they want to stand out. Big data allows businesses to determine precisely what their consumers are looking for. From the start, they build a strong consumer base.
The tendencies of customers are being observed by new big data techniques. By gathering more information to find new trends and methods to satisfy clients, they leverage those patterns to encourage brand loyalty. For example, by offering one of the most individualized purchasing experiences available on the internet right now, Amazon has nailed this strategy. In addition to prior purchases, suggestions are based on things that other customers have purchased, browsing habits, and a variety of other characteristics.
2. Targeted campaigns
Big data may be used by businesses to give customized products and services to their target markets. Stop wasting money on unsuccessful advertising strategies! Big data assists businesses in conducting extensive analyses of consumer behavior. This study often involves tracking internet purchases and keeping an eye on point-of-sale activity. Following the development of effective, targeted campaigns using these data, businesses are able to meet and exceed client expectations while fostering increased brand loyalty.
3. Identification Of Potential Risks
Today’s high-risk settings support the growth of enterprises, but they also necessitate risk management procedures. Big data has been crucial in the creation of new risk management solutions. Big data may make tactics more intelligent and risk management models more successful.
4. More innovative products
Big data keeps assisting businesses in both improving and developing new products. Organizations are able to determine what matches their consumer the most, based only on gathering a lot of data. A corporation can no longer rely on intuition if it wants to stay in today’s highly competitive market. With so much data available, businesses may now put mechanisms in place to monitor consumer feedback, product success, and rival activity.
5. Complex networks
Businesses may provide supplier networks, also known as B2B networks, with more accuracy and insight by employing big data. By using big data analytics, suppliers may avoid the limitations they usually experience. Big data is used by companies to increase their contextual intelligence, which is crucial for their performance.
The foundation of supplier networks has changed to include high-level cooperation, and supply chain executives increasingly view data analytics as a revolutionary innovation. Through cooperation, networks can apply new techniques to issues now being faced or to different situations.
How to launch a successful big data tool
Prior to using the data you have, you must decide what business challenges you are seeking to address. For example, are you attempting to identify the frequency and causes of shopping cart abandonment?
Secondly, simply having the information does not guarantee that you can utilize it to address your issue. The majority of businesses have been gathering data for ten years or even more. But it is “dirty data,” which is unorganized and chaotic. Before you can utilize information, you must organize it by putting it in a systematic manner.
Thirdly, the company you choose to cooperate with must be capable of more than just visualizing the data if you decide to hire them. It must be a company that really can model the data to generate insights that can aid in your business problem-solving. Before moving forward, it’s crucial to have a plan and budget in place because modeling data is neither simple nor inexpensive.
Big data analytics are helping the largest companies to keep expanding. More businesses than ever before have access to emerging technologies. Once brands have access to data, they may use the proper analysis methods to implement and address many of their issues.
Picture this: you’re a busy privacy professional who has been pulled in multiple directions over the last few weeks.
You’re finally sitting down to clear some of the backlog on your desk when you see an email from a customer with the subject line “DSAR.”
Your heart sinks as you think to yourself, ” not another one.”
Data subject access requests (DSAR) can be time-consuming and resource-intensive for organizations of all sizes.
They can also be a headache for privacy professionals who have to manage them.
However, there are some things you can do to make the process go more smoothly. In this article, we’ll share five tips on how to deal with DSARs.
What is a DSAR?
A DSAR is a request from an individual for information about themselves that an organization holds.
Under the EU General Data Protection Regulation (GDPR), individuals have the right to access their personal data, as well as the right to have that data erased or corrected.
Organizations must respond to DSARs within one month, unless they have a good reason to extend the timeline.
DSARs can be submitted in writing or orally, and they don’t have to include the term “DSAR.”
They can be submitted to any part of the organization, not just the privacy team.
Receiving a DSAR
The first thing you should do when you receive a DSAR is to confirm that the individual is who they say they are.
This can be done by asking for a copy of their ID or by using an identity verification service.
Once you’ve verified the individual’s identity, you’ll need to gather all of the information that they’re requesting.
This may require working with other teams in your organization, such as IT or HR.
It’s important to note that individuals can request any data that an organization holds about them, including data that is not related to their personal data.
For example, an individual could request information about how their data is being used, where it came from, or who it has been shared with.
They could also request information about an organization’s data retention policy or its procedures for handling data breaches.
Responding to a DSAR
Once you have all of the information that the individual has requested, you’ll need to put it together in a format that is easy to understand.
Remember, individuals have the right to receive their data in a “commonly used and machine-readable format.”
If you’re not sure how to do this, you can always ask for help from your IT team.
Once you’ve compiled all of the information, you’ll need to send it to the individual within one month.
If you can’t do this, you’ll need to provide a reason for the delay and let them know when they can expect to receive the information.
Dealing with DSARs can be time-consuming and resource-intensive.
To make the process go more smoothly, it’s important to have a plan in place for dealing with them.
Here are a few things to keep in mind:
- Establish a process for receiving and responding to DSARs.
- Train your team on how to handle DSARs.
- Keep track of all DSARs so you can identify patterns and trends.
- Use data from DSARs to improve your privacy program.
By following these tips, you can make the DSAR process less of a headache and more of an opportunity to improve your privacy program.
Five Tips for Dealing with DSARs
1. Get organized
The first step is to get organized.
Create a system for tracking and managing DSARs so you can keep track of which ones are still open, which ones are in progress, and which ones have been resolved.
This will help you avoid duplicating work and ensure that all DSARs are addressed in a timely manner.
2. Train your team
DSARs can be submitted to any part of the organization, so it’s important to train all team members on how to handle them.
This includes knowing who to forward the request to, how to collect the necessary information, and how to format the data for delivery.
3. Keep it confidential
DSARs are confidential by nature, so it’s important to keep all information related to them secure.
This includes both the data itself and any internal communications about the DSAR.
4. Take your time
Don’t rush through the process.
Organizations must respond to DSARs within one month, but that doesn’t mean you should wait until the last minute to start working on them.
If possible, start gathering information as soon as you receive the request so you can avoid delays later on.
5. Use it as an opportunity to improve
DSARs can be time-consuming and resource-intensive, but they can also be an opportunity to improve your privacy program.
Use the data from DSARs to identify gaps in your processes and make changes to improve the way you handle personal data.
DSARs may be a headache, but they help customers express their rights under GDPR. Data security and privacy is a top concern for customers, so it’s important to take DSARs seriously. Hopefully, if you follow these tips, you can make the DSAR process less of a hassle, and manage to comply with GDPR at the same time.
Outsourcing your data entry can be a great way to save time and energy while still getting the job done accurately and efficiently. But before you jump in and start working with a company, there are a few things you need to do to make sure the process goes as smoothly as possible.
In this guide, we’ll walk you through outsourcing your data entry, from finding the right company to work with to making sure everything runs smoothly once the transfer is complete.
So whether you’re just getting started or looking for ways to improve your current process, read on for helpful tips and advice!
What is Data Entry and Why Do it Yourself?
Data entry is the process of inputting data into a computer system or database. This can be done manually by typing information into a form or spreadsheet or automatically using scanners or other devices that read and convert physical data into digital form.
Data entry is often used to create records of customer transactions, inventory levels, employee information, and more.
It is vital to Record Keeping of Customer Identity and Transactions necessary to comply with the Anti-Money Laundering rules & regulations. Firms required to abide by the rules must keep records to help deter money laundering activities.
There are many reasons why you might choose to outsource your data entry rather than doing it yourself. Maybe you don’t have the time or staff to handle it all in-house. Or perhaps you want to free up your employees’ time so they can focus on other tasks.
Outsourcing can also save you money since you won’t need to purchase expensive software or hardware, and you won’t have to pay for employee training.
Whatever your reasons, there are a few things you need to keep in mind before you start working with a data entry outsourcing company. Below, we’ll walk you through the entire process, from finding the right company to work with to make sure your data is transferred safely and securely.
Step 1: Find the Right Company
When it comes to finding data entry outsourcing services, you have a few different options. You can search online, ask for recommendations from friends or business associates, or contact a professional association or trade group. Once you’ve compiled a list of potential companies, it’s time to start doing your research.
First, take a look at the company’s website. Does it look professional? Are there any spelling or grammatical errors? These may seem like small things, but they can indicate a company that doesn’t pay attention to detail – and that’s not what you want when it comes to your data.
Next, read through any online reviews you can find. What do other customers say about the company’s service? Are they happy with the results? If you can’t find any reviews, try reaching out to the company directly and asking for references.
Finally, make sure you understand the company’s pricing structure. Some data entry outsourcing companies charge by the hour, while others charge by the project. There may also be additional costs like rush orders or special formatting requests. Be sure you understand all the potential costs before making your final decision.
Step 2: Prepare Your Data
Once you’ve chosen a data entry outsourcing company, it’s time to start preparing your data for transfer. This process will vary depending on the type of data you’re working with and how it’s currently stored.
If you have paper records, start by scanning them into digital form. You can do this yourself with a scanner or all-in-one printer or hire a professional scanning service. Save your documents as PDFs or image files once your documents are in digital form.
If possible, use optical character recognition (OCR) software to convert the PDFs into text files that can be edited. This will make the data entry process much faster and easier.
If you already have digital records, look at how they’re currently stored. Are they in a database? A spreadsheet? Or are they just lost files on your computer? If they’re not already organized in an easily accessible format, take some time to do that now. It will make the data entry process much simpler.
Step 3: Transfer Your Data
Now it’s time to transfer your data to the data entry outsourcing company. This should be a relatively easy process if you’ve done your preparation work.
First, decide how you want to send the files – by email, through a file-sharing service, or on a physical storage device like a USB drive.
Once you’ve decided on a method, send the files to the company. If you’re sending sensitive or confidential data, encrypt the files before transferring them.
Step 4: Check-In With the Company
After you’ve transferred your data, it’s essential to stay in touch with the company to ensure everything is going smoothly. Ask for regular progress reports to see how much work has been completed and what remains to be done. If you have any questions or concerns, don’t hesitate to reach out to the company directly.
It would be best if you also planned to review the finished product before it’s delivered to you. This will allow you to catch any errors or ensure the data is formatted correctly.
You can outsource your data entry process with confidence by following these steps. With a bit of preparation and the right team in place, you can get the job done quickly and efficiently – freeing up your time to focus on more important things.
3 Tips for Making the Most of Your Data Entry Outsourcing Experience
When you outsource your data entry, you can do a few things to ensure the process goes as smoothly as possible.
First, be clear about your expectations from the start. What kind of turnaround time do you need? What format do you want the data in? Are there any special instructions or requirements you need the team to follow? By being clear about your expectations from the beginning, you can avoid misunderstandings and delays further down the line.
Next, make sure you provide high-quality data. This may seem obvious, but it’s crucial – if the data you provide is incomplete or inaccurate, it will only slow down the process and lead to errors. So take some time to double-check your records before you send them off.
Finally, stay in touch with the team throughout the process. Check-in regularly to see how things are going and answer any questions. By staying involved, you can help ensure a successful outcome.
Outsourcing your data entry can be a great way to save time and improve efficiency. By following the steps in this guide, you can find the right company to work with, prepare your data, and transfer it smoothly.
And by keeping a few things in mind, you can make sure the process goes as smoothly as possible. So if you’re looking for a way to streamline your workflows, outsourcing your data entry is worth considering.
As data becomes increasingly important in our digital age, the need for data centers is increasing. If you’re no longer using your data center or if it’s reached the end of its life cycle, you may be wondering how to sell it. This guide will walk you through selling your data center equipment, from preparing it for sale to finalizing the transaction.
Preparing Your Data Center Equipment for Sale
The first step in selling your data center equipment is to prepare it for sale. This means:
Ensure All Equipment is in Good Working Condition
You first need to ensure that all your data center equipment is in good working condition. This means testing and inspecting the equipment to ensure it is functioning correctly. If any pieces of equipment are not in good working condition, you will need to repair or replace them before you can sell them.
To properly check your equipment, you will need to do the following:
- Thoroughly inspect all of the equipment for any physical damage.
- Test all of the equipment to ensure that it is functioning properly.
- Make sure that all software and firmware are up to date.
If you find that any of your data center equipment is not in good working condition, you will need to repair or replace it before selling it.
Clean and Disinfect All Equipment
Once you’ve ensured that all the equipment is in good working condition, you must clean and disinfect it. This is important because you want to ensure that the equipment’s new owner will be safe from any harmful bacteria or viruses present on the equipment.
To safely clean your equipment, you will need to:
- Turn Off and Unplug All Equipment: You will need to turn off and unplug all of the equipment before you start cleaning it.
- Clean All Surfaces with a Mild Soap and Water Solution: Use a mild soap and water solution to clean all of the surfaces of the equipment. Do not soak the equipment in water.
- Disinfect All Surfaces with a Disinfectant: Use a disinfectant to kill any bacteria or viruses that may be present on the equipment. Do not soak the equipment in the disinfectant.
- Dry All Surfaces Completely: Make sure all surfaces are completely dry before turning the equipment back on.
Remove Any Personal or Sensitive Information
Before you list your data center equipment for sale, you need to remove any personal or sensitive information from the equipment. This includes passwords, user names, account numbers, and other sensitive information.
Once you’ve removed all of this information, you should also wipe the hard drives of the equipment to ensure that no trace of the information is left behind.
The best way to remove all sensitive information from your data center equipment is to use data destruction software. This software will allow you to securely delete all the information from the equipment so that it can not be recovered.
Once you’ve deleted all of the information from the equipment, you should also wipe the hard drives of the equipment to ensure that no trace of the information is left behind.
There are a few different ways that you can wipe the hard drives of your data center equipment:
- Use a Disk Wiping Software: You can use disk wiping software to overwrite all of the data on the hard drives. This will make it impossible to recover any of the information on the drives.
- Use a Disk Sanitizing Software: You can use disk sanitizing software to delete all of the data on the hard drives and then write over it with random data. This will make it impossible to recover any of the information on the drives.
Once you’ve removed all sensitive information from your data center equipment, you’re ready to list it for sale.
If you have any damaged or non-working equipment, you may want to consider repairing or refurbishing it before listing it for sale. This can help increase the value of your equipment and make it more appealing to potential buyers.
Listing Your Data Center Equipment for Sale
Once you’ve prepared your data center equipment for sale, it’s time to list it. You can list your equipment on online marketplaces or classified websites. When listing your equipment, be sure to include:
- A Description of the Equipment: Include a detailed description of the equipment, including its make, model, and special features.
- Photos of the Equipment: Include pictures of the equipment so that potential buyers can see what they’re buying.
- The Price of the Equipment: Be sure to list a fair price for the equipment. You can use online marketplaces or classified websites to find similar items for sale and use those prices as a guide.
- Your Contact Information: Include your contact information so that potential buyers can get in touch with you.
Once you’ve listed your data center equipment for sale, you should start receiving offers from interested buyers. Once you’ve received an offer, you can negotiate with the buyer to reach a final price.
When negotiating with buyers, remember that you’re not obligated to sell your equipment to the first buyer who makes an offer. If you receive multiple offers, you can take your time to compare them and choose the one that’s best for you. Or you also can find a company that specializes in buying and selling data center equipment for the best price.
Once you’ve reached an agreement with a buyer, it’s time to finalize the sale.
Finalizing the Sale of Your Data Center Equipment
When you’re ready to finalize the sale of your data center equipment, there are a few things that you need to do:
- Get a Bill of Sale: This document will serve as proof of purchase and help protect you and the buyer in case there are any issues with the equipment.
- Get Payment: Be sure to get payment from the buyer before releasing the equipment. You can use a secure payment method such as PayPal or an escrow service to protect yourself from fraud.
- Release the Equipment: Once you’ve received payment, you can release the equipment to the buyer.
It would be best if you kept all the documentation associated with the sale, including the bill of sale and any communication with the buyer. This documentation will come in handy if there are any issues with the equipment after the sale.
Selling your data center equipment can be a simple process if you take the time to prepare your equipment and list it for sale correctly. By following this guide, you can ensure that you get a fair price for your equipment and protect yourself from fraud.
A virtual private network (VPN) encrypts data as it travels across the internet, making it difficult for others to access it. A virtual private network (VPN) encrypts your online traffic, including your browsing history, downloaded files, and geolocation, so that no one else can see it.
However, you’ll need a virtual private network (VPN) if you care about internet privacy. It is like a digital cloak that shields you from prying eyes and keeps you safe from harm’s way. Then, why utilize a VPN? In order to prevent eavesdropping and provide unfettered access to blocked websites, a reliable VPN for windows encrypts your internet connection. This article will explain the benefits of using a VPN security system.
Why is a VPN security system good for you?
Is a VPN security system best for you? It is a question that everyone is asking, and the response is not complicated at all. Since VPN for Windows is an efficient solution for maintaining a VPN connection’s security and protecting data and internet browsing activities. The following are some of the most important reasons to utilize a VPN:
- Securing Your Network
Using a VPN has numerous advantages. The ability of companies to adequately secure their network is one of the most fundamental aspects. An application or website might keep track of your internet behavior without your awareness. Ads might be targeted to you based on the information they have gathered. Much pop-up advertising may interrupt your browsing if you don’t have a VPN set up. This can be both annoying and distracting to your online experience.
If you use a VPN for windows, you can prevent others from accessing your internet connection. This ensures the security and anonymity of any data you send or receive.
- Keep Your Personal Data Confidential
Virtual private networks (VPNs) are a great way to keep your personal information safe online. There are several ways hackers can steal your personal information when you visit a website. They can use that information to impersonate you, access your bank accounts, and more. High-level security, such as 256-bit encryption, is possible using a VPN. Anyone who can intercept your internet communications will see nothing.
- Control of the administration
Customers complain about a slow data network if they experience it for an extended period. The administration takes responsibility for resolving complaints. Data transmission may be controlled using virtual private networks and all obstacles eliminated. Unknown users cannot view site material if this system is in place.
Because of virtual private networks, many companies can now afford to utilize the internet. Access to the company’s network infrastructure and various applications is more accessible thanks to the VPN. To be successful in today’s technology world, you must protect all of your personal information from third parties.
- Avoid Data Slowdown
Your internet service provider (ISP) may slow down your connection once you’ve spent a certain amount of your allotted data. However, because your ISP cannot see how much data you are using when you use a VPN, you’ll soon discover that one of the VPN’s advantages is the ability to bypass a data cap. In particular, employees who are required to use data plans on their mobile devices to access the internet while on the go may benefit from this.
Conclusion: Why is a VPN security system good for you?
A VPN security system is a network of remote servers that you can use to disguise your IP address and encrypt your data.
What is tabular data
The term “tabular” refers to data that is displayed in columns or tables, which can be created by most BI tools. These tools find relationships between data entries in one or more database, then use those relationships to display the information in a table.
How Data Can Be Displayed in a Table
Data can be summarized in a tabular format in various ways for different use cases.
The most basic form of a table is one that just displays all the rows of a data set. This can be done without any BI tools, and often does not reveal much information. However, it is helpful when looking at specific data entries. In this type of table, there are multiple columns, and each row correlates to one data entry. For example, if a table has a column called “NAME” and a column called “GENDER,” then each of the rows would contain the name of a person and their gender.
Tables can become more intricate and detailed when BI tools get involved. In this case, data can be aggregated to show average, sum, count, max, or min, then displayed in a table with correlating variables. For example, without a BI tool you could have a simple table with columns called “NAME,” “GENDER,” and “SALARY,” but you would only be able to see the individual genders and salaries for each person. With data aggregation from using a BI tool, you would be able to see the average salary for each gender, the total salary for each gender, and even the total number of employees by gender. This allows the tables to become more versatile and display more useful information.
Preparing tabular data for description and archiving
These are general guidelines for preparing tabular data for inclusion in a repository or for sharing it with other researchers, in order to maximize the likelihood of long-term preservation and potential for reuse. Individual repositories may have different or more specific guidelines than those presented here.
- Only include data in a data file; do not include figures or analyses.
- Consider aggregating data into fewer, larger files, rather than many small ones. It is more difficult and time consuming to manage many small files and easier to maintain consistency across data sets with fewer, larger files. It is also more convenient for other users to select a subset from a larger data file than it is to combine and process several smaller files. Very large files, however, may exceed the capacity of some software packages. Some examples of ways to aggregate files include by data type, site, time period, measurement platform, investigator, method, or instrument.
- It is sometimes desirable to aggregate or compress individual files to a single file using a compression utility, although the advisability of this practice varies depending on the intended destination repository.
- Individual repositories may have specific requirements regarding file formats. If a repository has no file format requirements, we recommend tab- or comma-delimited text (*.txt or *.csv) for tabular data. This maximizes the potential for use across different software packages, as well as prospects for long-term preservation.
Data organization and formatting
Organize tabular data into rows and columns. Each row represents a single record or data point, while columns contain information pertaining to that record. Each record or row in the data set should be uniquely identified by one or more columns in combination.
Tabular data should be “rectangular” with each row having the same number of columns and each column the same number of rows. Fill every cell that could contain data; this is less important for cells used for comments. For missing data, use the conventions described below.
Column headings should be meaningful, but not overly long. Do not duplicate column headings within a file. Assume case-insensitivity when creating column headings. Use only alphanumeric characters, underscores, or hyphens in column headings. Some programs expect the first character to be a letter, so it is good practice to have column headings start with a letter. If possible, indicate units of measurement in the column headings and also specify measurement units in the metadata.
Use only the first row to identify a column heading. Data import utilities may not properly parse column headings that span more than one row.
Examples of good column headings:
max_temp_celsius – not max temp celsius (includes spaces)
airport_faa_code – not airport/faa code (includes special characters)
Data values and formatting
- Use standard codes or names when possible. Examples include using Federal Information Processing (FIPS) codes for geographic entities and the Integrated Taxonomic Information System (ITIS) for authoritative species names.
- When using non-standard codes, an alternative to defining the codes in the metadata is to create a supplemental table with code definitions.
- Avoid using special characters, such as commas, semicolons, or tabs, in the data itself if the data file is in (or will be exported to) a delimited format.
- Do not rely on special formatting that is available in spreadsheet programs, such as Excel. These programs may automatically format any data entered into a cell, which can include removing leading zeros or reformatting date and time cells; in some cases, this may alter the meaning of the data. Some of these changes revert the cell back to its original value when changing the cell type to a literal ‘text’ value and some do not. Changing cell types from “General” to “Text” before initial data input can prevent unintended reformatting issues.
Special types of data – Date/Time
- Indicate date information in an appropriate machine-readable format, such as yyyymmdd or yyyy-mm-dd (yyyy: four-digit year; mm: two-digit month; dd: two-digit date). Indicate time zone (including daylight savings, if relevant) and use of 12-hour or 24-hour notation in the metadata.
- Alternatively, use the ISO standard for formatting date and time strings. The standard accommodates time zone information and uses 24-hour notation:yyyymmdd or yyyy-mm-dd for date; hh:mmTZD for time (hh: two-digit hour, in number of hours since midnight; mm: two-digit minutes; ss: two-digit seconds; TZD: time zone designator, in the form +hh:mm or -hh:mm, or Z to designate UTC, Coordinated Universal Time).
Special types of data – Missing data
- Use a standard method to identify missing data.
- Do not use zeroes to represent missing data, and be cautious and consistent when leaving cells blank as this can easily be misinterpreted or cause processing errors.
- Depending on the analysis software used, one alternative is to select a code to identify missing data; using -999 or -9999 is a common convention.
- Indicate the code(s) for missing data in the metadata.
- When exporting data to another format, check to ensure that the missing data convention that you chose to use was consistently translated to the resulting file (e.g. be certain that blank cells were not inadvertently filled).
Data quality assurance
Consider performing basic data quality assurance to detect errors or inconsistencies in data. Here are some common techniques:
- Spot check some values in the data to ensure accuracy.
- If practical, consider entering data twice and comparing both versions to catch errors.
- Sort data by different fields to easily spot outliers and empty cells.
- Calculate summary statistics, or plot data to catch erroneous or extreme values.
Providing summary information about the data and including it in the metadata helps users verify they have an uncorrupted version of the data. This information might include number of columns; max, min, or mean of parameters in data; number of missing values; or total file size.
Tools to help clean up tabular data
OpenRefine (formerly GoogleRefine) is a very useful tool for exploring, cleaning, editing, and transforming data. Advanced operations can be performed on data using GREL (OpenRefine Expression Language).
The preceding guidelines have been adapted from several sources, including:
- Best Practices for Preparing Environmental Data Sets to Share and Archive. Hook, L.A., Beaty, T.W., Santhana-Vannan, S., Baskaran, L., & Cook, R.B. 2007. http://daac.ornl.gov/PI/bestprac.html
- Ecological Data: Design, Management and Processing. Michener, W.K. & Brunt, J.W. (Eds.). 2000.
- Guide to Social Science Data Preparation and Archiving. Inter-university Consortium for Political and Social Research. 2009. http://www.icpsr.umich.edu/files/deposit/dataprep.pdf
- Some Simple Guidelines for Effective Data Management. Borer, Elizabeth T., Eric W. Seabloom, Matthew B. Jones, and Mark Schildhauer. Bull. Ecol. Soc. Am. 90(2)205-214. 2009. https://esajournals.onlinelibrary.wiley.com/doi/full/10.1890/0012-9623-9…
What is Data Science?
Data science is a field that combines mathematics, statistics, and computer science. Data scientists use their skills to analyze data and extract meaningful insights.
There are three major steps in the data science process:
- 1) Data preparation: it includes cleansing, transforming, and loading the data into a usable form.
- 2) Modeling: it includes selecting the appropriate model for the given problem and using statistical methods to fit the model to the data.
- 3) Evaluation: it includes checking how well our model performs on new data that we haven’t seen before.
NIX United is a good data science consulting firm because they find the best solution for you according to your business goals.
Introduction to data science consultant vs data scientist
Data science consulting is an emerging profession in the data science field. Data science consultants are professionals who provide guidance to their clients on how to best use data and analytics, without having to go through the rigorous training that a data scientist does. They have a mastery of the skills required for data analysis, but lack a deep understanding of the underlying theories.
Data scientists are experts in analyzing large amounts of data and using this information to create models or insights that can be used for decision-making purposes. They have specialized knowledge about techniques and skills in statistics, machine learning and artificial intelligence, among other fields. Data scientists can also develop new algorithms or statistical methods for modeling purposes, as well as creating new predictive models from scratch.
What is a Data Science Consultant?
Data Science Consultants are a type of management consultant. They help businesses to identify and use data to create value.
Data Science Consultants are able to help companies in all sorts of ways, from identifying the best strategies for marketing campaigns to making sense of large datasets for better decision-making.
Data science consultants can be a valuable asset for any company looking to make the most out of their data, and they can be found in a variety of industries, including healthcare, financial services, and retail.
How big is the Market for Data Science Consultants?
Data science consultants are in high demand in the current market. They are required to create solutions that can help their clients solve complex data problems.
With the increasing use of data in various sectors, a lot of companies have been hiring data science consultants to help them make sense of their data. This is because they know that they can’t do it on their own and need an expert’s help.
Data science consulting costs vary depending on the type of consultancy service that is required and the duration for which it needs to be completed. But if we compare this cost with other types of consulting services, we will find out that this is much cheaper than other consulting services and hence, more affordable for most companies.
Comparing Consulting and Data Science Consulting Services
Consulting is the process of getting advice from a professional who has more experience in a particular field. Consultants are usually experts in their field and they will provide you with their opinion on your current situation.
Data Science Consulting Services are becoming more popular as the demand for data scientists is growing. Data Science Consultants will help you with your data-related problems such as data analysis, data integration, and predictive analytics. They have a lot of experience with these types of tasks and they can provide you with insight that might not be possible to get from other sources.
These two types of services are very different and it’s important to know what you need before choosing one over the other.
Data Science Consultant vs Data Scientist
Conclusion Summary: Which Career Path Should You Choose For Your Future?
The world of data is changing. Data scientists are in high demand and are considered to be the future of our society. But not everyone has the skillset or educational background to become a data scientist. So what should you do?
If you want a more stable career, then data consulting might be your best bet. Data consulting jobs are stable and provide a variety of work opportunities in different industries. A consultant can work with large companies or small startups, and they don’t need any specific skillset to do so.
Marketing data is an important asset of every business. Analyzing these statistics requires well-organized and structured insights for the best possible result. This is where data transformation comes in handy. It helps businesses to transform and format their data in a way that is more appealing to them. Simply put, it is a type of process that changes the form, structure, and value of data-driven insights.
There are two types of warehouses that organizations use to transform data: on-premises and cloud-based. This process can be constructive, destructive, aesthetic, or structural. People involved in this tend to use specific languages, like Python or SQL to finish the task.
Benefits and Challenges of data transformation
This process is very important for business for various reasons, like consolidating records, deleting duplicates, changing formatting, and a lot more. Marketing data and its transformation have various benefits. It helps companies stay better organized and is helpful for both sides, humans, and computers. It also improves the quality of the information. And most importantly, it makes it easier for applications, systems, and types of insights to be more compatible between them.
Like in every process, besides the benefits, there are also some challenges. This may include the fact that this process is very expensive. It all depends on the software and tools used for the data-driven insights. It can also be resource-intensive, and some businesses can use it for things that they do not need. Also, if it is done by analysts that do not have much experience it can bring problems for the company in the future.
How to Transform Marketing Data?
The first step in this process should always begin with extraction and parsing. Then it should follow with translation, mapping, filtering, and summarization. When there are different sources the insights can be merged to create enriched information. That should be split into multiple columns. The next step in the process is indexing and ordering, and then obviously encryption, which is a must. Finally, the process ends with formatting and renaming everything that needs to be done, to ensure clarity.
The Bottom Line Here
Data-driven insights are very important for every company. The transformation of these insights is what makes the analytics sector to be accurate, and that results in improving the business. So, it is very important to use the transformation tools correctly, and by people who know their way around them. Eventually, the success of the company depends on that.
SQL injection is a common cybersecurity issue used by attackers as an entry point to your database. It can be a precursor of many other attacks like credential stuffing, account takeovers, and other forms of fraud. Therefore, it is essential to understand how to protect the application’s database to avoid heavy losses from SQL injections. In this post, we will discuss various ways that you can use to prevent SQL injection attacks.
Ways to prevent SQL injection attacks
Among the most dangerous threats to web applications today are SQL injection attacks. All is not lost to a network or database admin because there are various ways to prevent them from ever happening or minimize their occurrence frequency.
As we will see below, you can take various steps to reduce the risk of exposure to SQL injection attacks.
Regular auditing and penetration testing
It is becoming increasingly necessary to perform regular application, database, and network audits nowadays. With regulations like GDPR, a company does not have the luxury of relaxing on matters of database security. In addition, auditing the database logs for suspicious activities, privilege escalation, and variable binding terms are necessary practices.
As crucial auditing, the system for malicious behavior is, it is equally essential to perform penetration testing of your database to gauge the readiness of your response mechanisms to potential attacks that include SQL injection. Penetration testing companies can find threats like cross-site scripting, unpatched vulnerabilities, retired software, insecure password, and various forms of SQL injection.
User Input Validation
Validating the user inputs is a common step to preventing SQL injection attacks. You have first to identify the essential SQL statements and make a whitelist containing all valid SQL statements. This leaves out the invalidated statements. We refer to this process as query redesign or input validation.
Ensure you configure inputs for user data by context. For instance, you can filter email addresses to ensure that only strings that contain specific characters such as “@” are allowed. In a similar fashion. Ensure that you filter the social security and phone numbers using regular expressions to allow a specific format and number of digits in each of them.
Sanitization of data through special character limitations
You can safeguard your database against SQL injection attacks through adequate sanitization of user data. SQL injection attackers use specific character sequences that are unique to exploit a database. Therefore, sanitizing your data not to allow concatenation of strings is a critical measure.
You can achieve this by configuring the inputs from a user to a function. It ensures that an attacker does not pass characters like quotes in an SQL query as they might be dangerous. Various administrators use prepared statements to avoid unauthenticated queries.
Parameterization and enforcing prepared statements.
Input validation and data sanitization do not fix all SQL injection-related issues. Therefore, organizations must use prepared statements containing queries that are parameterized to write database queries. We also call this variable binding. Distinguishing user input and code is made easy to define the SQL code used in a query or a parameter.
Although dynamic SQL as a programming method allows more flexibility in developing an application, it has the drawback of allowing SQL injection vulnerabilities as instructions. In addition, sticking to the standard SQL means malicious SQL inputs will be treated as data but not as a potential command.
Enforcing stored procedures in the database
Stored procedures use variable binding like parameterization. Unlike mitigating SQL injections using prepared statements, when you implement stored procedures, they are resident to the database and are only called from an application. If you use dynamic SQL generation, they minimize the effectiveness of stored procedures. According to OWASP (The Open Web Application Security Project®), only one parameterized approach is required, but neither is enough to guarantee optimal security.
Increasing the capability of the virtual and physical firewalls
To help fight malicious SQL queries, we recommend using software or appliance-based web application firewalls. Both NFGW and FWAAS firewall offerings are easy to configure and have a comprehensive set of rules. If a software security patch is yet to be released, you can find WAFs to be useful. One popular firewall is ModSecurity. It is available in Microsoft IIS, Apache, and Nginx servers. It has ever-developing and sophisticated rules to help filter potentially dangerous requests from the web. Its defenses for SQL injection can catch many attempts to sneak in malicious SQL queries from the web.
Reducing the attack surface
An attack surface is an array of vulnerabilities that an attacker can use as an entry point. Therefore, in the SQL injection context, it means that you do away with any functionalities in the database that you do not require or ensure further safety.
A good example is the xp_cmdshell extended storing procedure for the Microsoft SQL Server. It can spawn a command shell and pass a string for execution in windows. Since the process started by the xp_cmdshell has similar security privileges as the SQL Server service account, severe damage from the attacker can befall the database.
One rule should always reign when dealing with matters on the internet. No connected application is secure. Therefore, ensure that you hash and encrypt your connection strings and confidential data. There are many encryptions and hashing tools that are cheap, easily accessible, or even open source. Today we must universally adopt encryption as a data protection mechanism. It is for a good reason. Without encrypting your data using appropriate hashing and encryption policies, when it falls in the hands of a malicious actor, all the data is in plain sight. There are various hashing mechanisms like SHA, LANNAN, and NTLM. Encryption algorithms in the market today are bcrypt, DES, RSA, TripleDES, among many others. According to Microsoft, through encryption, we transform the problem of protecting the data protecting cryptographic keys.
Monitoring the SQL statements continuously
Third-party vendors and organizations should ensure continuous monitoring of all SQL statements within an application or database-connected applications. They should also document the prepared statements, database accounts, and stored procedures. It is easier to identify SQL statements that are rogue and various vulnerabilities when you scrutinize the functioning of the SQL statements. Therefore, a database admin can disable or delete unnecessary accounts, the stored procedure, and prepared statements.
There are monitoring tools that use technologies like behavioral analysis and machine learning. They include tools like SIEM and PAM and are an excellent addition to an organization’s network security.
Take away about prevent SQL injection
It is essential to conduct regular penetration testing to evaluate how you have implemented measures to prevent SQL injection attack responses. Through this option, you can stay ahead of the attacker and prevent lawsuits and hefty fines from coming your way. Besides the above measures, you can implement other safeguards like limiting access, denying extended URLs from your application, not divulging error messages, among many others.
PDF is a format that is extensively used in legal, academic, real estate, medical, and other industries. Small business uses PDF for storing, and sarong business-critical information as these files are highly secure for saving sensitive data.
Apart from the businesses, these files are widely utilized by students at various levels. Whether the academic departments want to share the assignments with students or academic transcripts, PDF is the best format as it is possible to add different content types such as text, images, or even QR codes.
Why PDFs for Data Storage & Transfer?
PDFs Are Portable
PDF stands for Portable Document Format; so, as the name suggests, these files are highly portable. It means that you can move these files, and they will appear the same on all the digital devices without any dependencies.
Once these files are created and stored as PDF, they remain the same, no matter how you use them. No compromise will be made to the integrity of the integrated contents even if you move them across operating systems.
PDFs Are Compatible
One of the best things about these documents is that they are compatible with running over all the operating systems. So, whether you are using macOS, Windows, Linux, or any other operating system, you can create, download, edit, share, or even merge pdfs into one for extensive usage.
If we talk about mobile devices, PDFs keep all the data intact, no matter if you are viewing them on Android, iOS, Windows, or other operating systems. The files adapt themselves to the screen size to ensure that all the information is displayed correctly on the small screen.
PDFs Are Reliable
Reliability in PDF is the mixture of both portability and compatibility. So, reliability here refers to the fact that when you open a PDF on a computer, laptop, tablet, or smartphone, you will not see any change in the paragraph, vector graphics, images, tables, graphs, or other content.
Not even a minor change is made to any of the data types when you export the document to another computer or other continent. One of the reasons for the immense popularity of PDFs is that they help convey information in the original format. Organizing work or exchanging information is easy thanks to PDFs.
Let’s now discuss some facts related to PDFs in the upcoming section.
Facts About PDFs You Must Know
Most Popular File Format
If you have ever used PDF, you must be aware of the fact that it is the most widely used file format on the internet, and the credit goes to the reliability and security parameters. These documents meet high standards of portability and compatibility aspects, which add to their popularity.
Encapsulate Robust Security
You can encrypt the document with a password to ensure that only authorized users can view it. One protected document can only be accessed by entering the right password key. This way, it controls unauthorized access. That’s why the banks are using PDF files to share account statements and other confidential details with users over email.
Integrate Extensive Features
Those who have been using PDF for a couple of years now must know that the earlier versions used to be bulky, storage-consuming, and lacked support for hyperlinks. Today’s PDFs are packed with powerful features that make them lighter, allow for faster downloads, are more versatile, and support hyperlinks.
Accessible to Persons with Disabilities
The PDF/UA (Universal Access) version makes these documents more accessible to persons with disabilities with the use of assistive technology. Accessible PDFs make use of software such as screen magnifiers, alternative-input devices, text-to-speech software, speech-recognition software, screen readers, and similar technologies.
Supports Interactive 3D Models
With the release of PDF 1.6 back in the year 2004, users were given the flexibility to embed 3D models and the Product Representation Compact (PRC). It is possible to zoom and rotate the 3D models integrated into PDF. For the uninitiated, the PRC is a file format that supports an equivalent display of geometrical or physical structure for 3D graphics.
Incorporates Multiple Layers
PDF files have different layers that users can easily view and edit as per business or personal preferences. Users can change the properties of each individual layer, merge them, rearrange them, or lock them on their computers. To view the layered PDF feature, you must use PDF 1.5 or higher version.
Convert Images to PDF
If you have created a digital print and want to share it with someone over email or chat, you can convert the image to PDF. Similarly, you can also convert a Word file PowerPoint presentation, JPEG file, Excel document, or even a Paint file to a PDF without compromising the quality of content or changing its actual structure.
PDFs come with numerous advantages, and there are some facts related to them that most users are not aware of. The more you use PDFs, the more you become used to them. Not only are they good for sharing data over email, but they are ideal for saving data on a computer, external storage, or on the Cloud.
If you’re starting a business or already halfway there, you need to listen to what your business data has to say. Data is more than numbers, graphs, pies, or percentages. They are essential information pieces to your business. Also, they offer insights into the status of different systems, departments, and locations.
Apart from that, they are also critical parts of systems advocacy. If you wish to change or improve something in the system, you need data. You can present facts and figures to support your claim. The same goes when you’re asking for more funding or lobbying for reforms.
There’s no guessing game with data. It doesn’t matter if your output is what you expected. With data, you can justify your business decisions and strategies. Yet, raw data won’t be of so much help.
With more than 2.5 quintillion bytes of data received per day, data shortage is not the issue. Instead, it’s how humans make sense of them. This is where analytics comes in.
What is Data Analytics?
The process of examining, cleaning up, and transforming data is what makes up data analytics. It also includes posturing data to discover insightful information. This is where businesses could use it to draw conclusions and support decision-making. With data analytics, you stop guessing. Instead, you start sticking to facts when making business decisions.
Must-Read Books on Data Analysis
If you’re unsure how to leverage data analytics for your business, you need to understand how it works. Below are the top five books you can start reading. With these, you’ll understand data analytics. You can also get ideas that can impact your business.
1. Wayne L. Winston’s Microsoft Excel Data Analysis and Business Modeling
If you want to learn Excel from the ground up, this is the perfect book for you. Excel has been every beginner’s favorite for statistical analysis within the last 35 years. If you master this, you can launch a career in analytics. More so, you can help your business grow.
Many consider this book as one of the best in data analysis. Why? Because it uses Excel for probability analysis and basic statistics. Aside from that, the author also filled it with many practical applications. These applications are for technical topics like forecasting, multiple regression, and more. The content is also extensive because it can help you become an expert in the topic, with its many exercises.
2. Phil Simon’s Too Big to Ignore: The Business Case for Big Data
If you’re still unsure whether big data is helpful for businesses, then you have to read this book. The author showed how institutions leverage data. They used the government, private sector, and big corporations to explain this point.
Phil Simons also included several lessons from big data experts. Also, he added case studies from across the globe. Anyone who wants to dabble in data analytics must read this book. It can give valuable insight on how to turn data into intelligence. It would also teach you how to turn intelligence into actionable information.
3. Cole Nussbaumer Knaflic’s Storytelling with Data: A Data Visualization Guide for Business Professionals
If you want to learn how to communicate efficiently with data, you need to read this book. This shows you how to leverage the knowledge of visualization for your business. The book also offers pragmatic guidance to business analytics experts. Through this, they can present data in a more palatable and understandable manner.
You can master data analytic skills with this book. It shared insights and information to achieve this. The book challenges you to go beyond your comfort zone. You can do this by using conventional data visualization tools. As such, you can create a more compelling, informative, and engaging story.
4. Gohar Khan’s Creating Value With Social Media Analytics
This book will assist in your learning further about data analytics. It will also teach you how to apply big data to various social media strategies. Through this, you can drive engagement and value. You need this if you want to improve your conversion rate and increase market traction.
If you delve deeper into the principles shared in this book, you will better understand them. It discusses resources, techniques, strategies, concepts, and theories to enjoy social media. With this, you can increase website traffic and generate high-quality leads. You can also improve buyer patronage and make better business decisions.
5. Andriy Burkov’s The Hundred-Page Machine Learning Book
This book is best for data science beginners. If you want to get acquainted with machine learning, read this book. It talks about technicalities and mathematical concepts in simple terms. As such, it doesn’t sound intimidating or overwhelming to novice data analysts.
The value of big data and big data analysis for a business is undeniable. Regardless of their size and experience, companies should learn how to leverage data. With this, they can improve their working methods and increase customer satisfaction. They can also improve their business bottom line.
Read these books and jumpstart your business’ data analytics journey. With the valuable data analytics insights in these books, you could never go wrong. And, if you’re ready to scale your business, contact Thematic. They can help you make sense of your big business data.
I have two points for which I occasionally try to be persecuted:
- If you don’t know which tool to choose, then choose any tool. And this is most likely Google Optimize.
- You don’t need to know the under-the-hood math of A/B testing. Look at the tool’s “Win/Lose” messages.
So why do I think that?
- If you don’t know which tool to choose, chances are you have no or very little experience in A/B testing. To get that same experience, it’s important to start as soon as possible.
- If you don’t know which tool to choose, it means you have no requirements for the tool. If there were real requirements, a simple comparison of functionality would quickly solve the problem. And if there are no requirements, then anything will do.
- Knowing the under-the-hood math will help if you write your tool from scratch. In other cases, it’s practically useless: you can’t change the Google Optimize algorithm(s) and/or other tools.
It seems like picking the best tool and figuring out the under-the-hood math is very, very right. The devil lies in the definition of “right.” I insist that speed of motion (start early) is more important in most cases. In that time, you will have time to do more and get better results than that gain from a better tool and knowledge of math.
Run experiments whose wins will be reported to you by accounting, not by the analytics system.
Data collection for most qualitative research relies primarily on face-to-face interaction. It is due to the nature of the data itself, taking the form of concepts, words, and ideas.
But pandemic restrictions have affected all aspects of our lives, including research pursuits. This has significantly affected data collection for qualitative studies. Due to this, many researchers have resorted to remote methods of data collection. This shift brings up some new challenges unique to remote interactions.
We’ll go through some popular remote data collection methods for qualitative research. What are their advantages and characteristics? What factors do we have to consider with the use of online/electronic means for data collection?
Remote Data Collection Methods
Popular pre-pandemic methods of data collection were face-to-face interviews and focus group discussions. Aside from transcribing the words of the participants, researchers also made field notes. These notes consisted of the researchers’ observations on the environment and nonverbal cues, among others.
However, you cannot access all this extra information through remote interactions. Alongside other factors, most people are more comfortable with in-person conversations.
Researchers have decided to make do and glean whatever information is available. Remote methods also present some unique advantages.
Video conferencing experienced a surge in popularity during this pandemic. Platforms such as Zoom and Google Meet became popular choices for communication.
Video conferencing is a popular alternative due to it being an audiovisual experience. It’s the closest we can get to face-to-face interaction without having to be near each other.
Depending on factors such as internet speed and access, discussions can flow naturally.
Most video conferencing platforms also have built-in recording tools. These leave you with a video file you can review and transcribe when needed.
Phone calls are also a popular remote interview choice. They are relatively inexpensive and accessible, especially for respondents in more remote areas.
Phone interviews are an excellent way to collect verbal testimonies and interviews. It may also help respondents feel more comfortable since they won’t feel as observed. Since you cannot see each other, there is less pressure to ‘perform’.
However, this lack of visual cues can also be a disadvantage. It makes building a rapport more of a challenge. It also makes it more challenging to pick up on non-verbal cues like body language.
Organizing a group discussion over a phone call could also be quite a challenge. Some older phone models might not support the feature, and it would be harder to keep track of each speaker.
You can also conduct interviews or discussions via text message or online chat. There are a lot of messaging apps available right now, with both mobile and desktop versions.
Depending on your research design, these discussions can be synchronous or asynchronous. You can send your questions and have the respondents answer them. You can also have everyone go online at the same time and have a live discussion.
Like phone calls, respondents could feel more comfortable communicating via chat. Without visual cues, they can feel more at ease with expressing themselves. It’s also easier to record conversations since the medium is text-based.
Its weakness, however, is the lack of visual and auditory cues. The medium limits you to communicating via words on a screen. This risks misinterpretation. You can use emojis, slang, or tone indicators, but it is not the same as face-to-face interaction.
Remote data gathering is not a new phenomenon, but it hasn’t been the norm until recently. Due to its differences with more traditional methods, it also poses unique considerations.
Most remote data gathering methods need to use third-party programs or applications. You may reassure them that you will keep their data confidential, but these apps may not.
It’s best to be open about this to your respondents. For some, it might not be a huge concern, but it’s best to be safe. Better yet, you can find messaging or conferencing programs that guarantee your privacy.
This concern can depend on who you are writing for or your research question itself.
Some studies have also questioned the validity of qualitative interviews. They cite that the researcher’s personal biases could influence their line of questioning. The additional barriers innate in remote methods could add to the method’s limitations.
Either way, it is best to consult with fellow researchers or your higher-ups. Getting feedback helps you ensure the clarity of your methods and the validity of your data.
Unique circumstances require unique solutions. There is no question that researchers will continually adapt and overcome. Pandemic or not, we remain committed to the pursuit of knowledge to help our institutions.
Once you have your data together, you’re ready to move on to analysis. Thematic has a quick yet comprehensive guide to help you through the process.
While it is true that online dating has the edge over traditional dating, there is a downside that involves sharing your intimate information with the matchmaking services you use. Not all, but most Matching & dating sites and apps rely on the information you provide to run their business. Some use it to suggest the best matches, while others use it for targeted advertising and to make money. Ultimately, the choice of the dating site will matter a lot in determining how safe and secure your data is.
Which Groups of Daters Are Most Concerned about Collecting Data?
Everyone seems worried about sharing their location and intimate personal details with online dating sites, but married people looking for affair dating are usually more concerned about how their information is stored and protected. Similarly, mature singles who are returning to the dating scene are often skeptical about dating collection by these online services. That is why platforms targeting affair dating keep a close eye on their privacy policies so that users can feel their data is secure. As most sites are now moving away from advertising and relying more on paid upgrades and subscription revenue, wives seeking married dating or single parents returning to dating should know their data is safer than it used to be.
How Much Data Dating Services Collect about Their Users?
It usually depends on the quality and reputation of a dating site, but most of them collect a variety of highly personal data and often retain it indefinitely. It may include text conversations with other members, photos, videos, and info on sexual orientation, gender, religion, political affiliation, location, ethnicity, body type, desire to have children, and beyond. Some platforms also collect data related to preferences in a partner – they achieve it with the help of filters or by utilizing powerful algorithms that keep an eye on users’ every swipe.
Today, an increasingly large number of dating sites encourage you to join through Facebook, Instagram, or other social media sites. This option allows those sites to access thousands of additional data points, including who your friends are, what you like online, and what kind of content you have been viewing. Speaking of sensitive information, the top on the list is your location. Dating sites collect your location data because they claim to need it for recommending relevant matches nearby.
This means that whether you use an app-based platform or you are on a website-based service, know that dating services will have a bunch of your data. Plus, a website data tracker can help reveal the URLs you visit while exploring a dating site. The information is then used for a positive result from the matching service, which is harmless as long as the site that is used has a high trustworthy reputation.
How Dating Sites Use the Collected Data?
Providing better services for finding partners is a key point, exactly for which the entire data collection is started. That is why choosing a reliable, authentic, and reputable dating site becomes even more important. An authentic site would use it for the following purposes:
- To Improve Customer Experience
A good dating site uses your info to improve customer experience. For instance, they access your location data to help you find someone in your local area. How precise the data tracking is, varies from site to site, with the opportunity for members to share or indicate themselves their current city and country.
Some dating apps would show even more granular location info – they allow you to find out users who may be only a few feet away from where you are. It means you can find people in the same town or even on the same floor of your apartment building. And with data about your preferences in a partner, such matches will inevitably lead to a real date.
- To Update Algorithms
Dating sites work on algorithms to fetch you the most relevant matches, and the data you share will have a huge impact. They constantly upgrade algorithms considering your personal information as well as who has liked you on the platform and how you use the service. The algorithm is updated considering why your profile is “Liked” or “Not.”
They also consider preferences you share with them, which allow the sites to introduce new filters and help you find accurate matches. That is why some sites allow you to filter results based on body type, ethnicity, and religious background, and others do not.
- To Secure Users
Security is an important concern for online daters, and these sites and apps augment using your data. For instance, they use your photos and screen your data using AI to ensure better security. Some sites preemptively screen images and block everything that might be considered lewd. Such steps help increase customer satisfaction and allow them to browse a site with confidence.
Profile identification, implementing fraud and spammer tracking systems, blocking inappropriate content – all this is possible thanks to preliminary data collection and previous user experience.
Today, people do not mind mining dating sites and apps for love, and they even do not mind becoming a premium member to enjoy additional matchmaking services. Over 30% of US adults are using those online platforms and would continue to do so until they find a partner. Being on an authentic dating platform would help keep things safe and secure with the most positive outcomes possible.
Data-driven storytelling is based on the great allure of stories. With this in mind, more and more businesses are adopting a narrative approach to internal and external communication in the effort to convey abstract data in vivid ways.
In times of big data: Presenting complex information in an understandable way
Business intelligence tools, CRM software and the use of artificial intelligence all give marketing and sales departments a wide range of options for data collection and analysis to choose from. But the crux actually lies in the wealth of information available and in the complexity of that information: The mere generation of numbers and data is largely pointless if it does not succeed in communicating the meaning of these numbers and data and putting them in a context people can understand.
Data-driven storytelling, on the other hand, prepares naked figures in such a way that stakeholders and customers experience them as understandable, interesting and appealing.
“Sometimes reality is too complex. Stories give it form.”Jean-Luc Godard
Storytelling with data as a communication strategy
Basically, data storytelling is not new. For instance, a trend toward data-driven journalism has been emerging for several years now. The term describes not only a certain type of information acquisition but also a particular form of presentation. These aspects are also true of data-driven storytelling in in-house corporate communications and in marketing.
In essence, data-driven storytelling comprises three areas:
- the analysis of the data
- the narrative
- the visualization of the data
Thanks to narrative as well as visual and interactive elements, abstract data sets take shape, and this contributes to greater reach.
Data processing: Here’s how to turn data into a good story
But how do you proceed if you want to illustrate the latest sales figures, or user interactions in the last quarter? First of all, there should be careful consideration of which topic is to be prepared for whom, and on the basis of which data:
- What point do my data illustrate? How meaningful and representative are they?
- What target group do I want to address?
- Which aspects of my data evaluation should be conveyed to the target group?
- What prior knowledge does the target group have?
- What misconceptions does the target group possibly assume?
7 typical storylines in data-driven storytelling
Only once the above aspects have been isolated does it make sense to think about what the narrative should look like. First of all, a storyline should be considered that is appropriate to the question at hand and the existing pool of data. According to marketing manager Ben Jones, 7 basic types can be distinguished here:
- Change over time: A story is told about a process or transformation.
- Drill down: The narrative begins with an overall view and leads to a concrete example.
- Zoom out: Over the course of the narrative, a tiny focus is extended to taken in the big picture.
- Contrast: Different protagonists, data or issues are compared.
- Intersection: At the heart of the narrative lies a crossroads where two or more questions or data points intersect.
- Dissection of factors: Data and storylines are interrogated for correlations and causalities. Unclear records are “dissected,” so to speak.
- Profile of outliers: The story is dedicated to special cases and statistical outliers.
A meaningful structure of a data story
Like any good story, a data story should captivate its readers or listeners. Taking a cue from the dramaturgy of classic feature films, a structure in at least three parts is recommended:
- Exposition: Presentation of the topic and context of the data analysis; what is the occasion for broaching the question?
- Confrontation: Presentation of the central question and the challenges involved; what are interesting observations and problems?
- Resolution: Concluding wrap-up with recommendation for action; what insights does the data analysis yield, and what things might need to be changed?
Data visualization: Preparing numbers for visual effect
In addition to the actual narration, visual elements also play a decisive role in data-driven storytelling. Infographics, diagrams, animation and highlights make the world of numbers tangible, even for the untrained beholder. The presentation should be as clear and simple as it is precise. The accompanying narrative can pick up on and explain any relationships that cannot be conveyed visually. Combining textual and visual elements makes data stories easy to understand and internalize. This not only bolsters in-house communication processes but can also contribute to improved customer loyalty.
About the author: Cora Eißfeller
Cora Eißfeller works as an online editor at content marketing agency textbest in Berlin. After working for several years in publishing, the literary scholar now devotes herself entirely to digital marketing. Her focuses are e-commerce, new work, and urbanisation trends.