Tabular Data: What is and How Preparing

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.

General guidelines

  • 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

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).

References

The preceding guidelines have been adapted from several sources, including:

Data Science Consultant vs Data Scientist: What are the Similarities and Differences?

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.

What is Data Transformation and why does it matter for your marketing?

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.

Ways to Prevent SQL Injection Attacks

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.

typical eStore’s SQL database query

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.

Encryption

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.

New to PDF? Explore 10 Unknown Benefits & Facts

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. 

The Conclusion

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.

Top 5 Books to Help You Master Business Analytics

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 Takeaway 

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.

The best tool for A/B tests for UI or CRO

I have two points for which I occasionally try to be persecuted:

  1. If you don’t know which tool to choose, then choose any tool. And this is most likely Google Optimize.
  2. You don’t need to know the under-the-hood math of A/B testing. Look at the tool’s “Win/Lose” messages.

https://optimize.google.com/optimize/home/

So why do I think that?

  1. 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.
  2. 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.
  3. 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.

Qualitative Data Collection During the Pandemic

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

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

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.

Text/Online Messaging

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.

Considerations

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.

Privacy

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.

Professional Standards

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.

Wrapping Up

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.

Data Collection on Dating Sites: What You Need to Know

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.

Summarizing

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: How numbers are transformed into stories

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:

  1. Change over time: A story is told about a process or transformation.
  2. Drill down: The narrative begins with an overall view and leads to a concrete example.
  3. Zoom out: Over the course of the narrative, a tiny focus is extended to taken in the big picture.
  4. Contrast: Different protagonists, data or issues are compared.
  5. Intersection: At the heart of the narrative lies a crossroads where two or more questions or data points intersect.
  6. Dissection of factors: Data and storylines are interrogated for correlations and causalities. Unclear records are “dissected,” so to speak.
  7. 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:

  1. Exposition: Presentation of the topic and context of the data analysis; what is the occasion for broaching the question?
  2. Confrontation: Presentation of the central question and the challenges involved; what are interesting observations and problems?
  3. 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.