Five Main Benefits Of Big Data Analytics

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.

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.

The Role of Big Data in Mobile App Development in 2022

The digital world is similar to our ecosystem – there is always room for evolution. Thousands of unparalleled development opportunities are introduced day-in and day-out, and there’s no stopping it!

When it comes to growth opportunities, the mobile application development industry is at its apex.

With around 4.57 billion people hooked to their phones, more businesses are building mobile applications to attend to both internal and external customers.

This competition has created a pressing need to stay updated with the customer needs, swiftly altering market trends, and advances in technology.

As a business, to achieve success and out-perform your competitors, let data propel your decisions. Big Data lets you uncover hidden customer preferences and use them to build high-tech mobile apps.

What exactly is Big Data?

To understand what exactly Big Data is, here is its definition:

Big Data is information that has more variety coming in huge volumes and with a higher velocity and veracity. These are also known as the four Vs of Big Data.

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. 


Big Data is a bigger, more intricate set of information derived from the latest data resources.

These data collections are extremely voluminous and cannot be handled by conventional data processing software. But these data sets can be used to solve business issues that were initially difficult to tackle.

With Big Data technology, it is easy to process information gathered to obtain rich and real-time company insights. These are related to product management, profit, risk, users, augmented shareholder value, and performance.

In the world of Big Data, the key aspect is speed. Conventional analytics focused on scrutiny of old data, while Big Data analytics focuses on real-time data.

The Four V`s of Big Data

  • Variety

This refers to various types of data derived from different resources, developed by people and machines. These are available as structured, semi-structured and unstructured data types.

  • Volume

The amount of information that’s developed is huge when compared to traditional data. While for some it could be tens of terabytes, for others it might be hundreds of petabytes.

  • Velocity

This is the rate at which the data is created. It is a never-ending process, even though you’ve fallen asleep.

  • Veracity

Data is a valuable source, but it is of no use if it isn’t true. Big Data is derived from several sources; therefore, you must verify the veracity/quality of data.

The role of Big Data in application development

The significance of Big Data is huge since it compresses data derived from video and voice recordings, social media, unstructured and structured data, and machine data. 

When it comes to mobile app development, Big Data gives developers more leverage over essential external factors. It also lets companies compare, understand, and evaluate the app’s usage so that they can head up to future objectives.

With Big Data, businesses can extract comprehensive insights and information regarding user experience. This helps them build mobile apps that are in line with the customers’ demand.

TOP 5 Benefits of Big Data in mobile app development

For a mobile application to be downloaded frequently, it needs to be engaging, quick and easy to use.

The most essential aspect of it, however, is to meet the needs and expectations of a user. Using Big Data to develop mobile applications helps in that context. Here are some of its benefits:

  • Understands users expectations

An application is known to be the best when it meets the customers’ needs. But this increases the expenses of maintaining the app.

When you use Big Data, you can easily evaluate the flow of information produced by users daily. You will also be provided with useful insights as Big Data constantly garners the users’ needs.

With this technology, a company can understand the interaction and reaction of users from various demographics. This further will help you come up with quality and innovative mobile application ideas.

  • Analyzes the user experience

The realm of mobile app development is growing at a faster pace. Due to this, developers must understand the way users use the app along with their needs.

By using Big Data technology for app development, you can thoroughly analyze the users’ experience. This will provide you with a detailed analysis of their engagement for every feature and page.

With this analysis, you can create a list of things that users want or demand to be changed or improvised.

  • Offers access to real-time data

In today’s time, companies have to constantly keep updating their business tactics to stay ahead of the curve!

Big Data, in this aspect, offers promising end-results with full access to real-time data. It also provides real-time analysis of information.

This helps you make real-time and data-driven decisions to enhance the user experience and customer satisfaction. It also assists to make more profits.

  • Improves the performance of the app

Developers can now track the traffic generated by the application with complete ease – all thanks to Big Data technology.

They can also analyze the user engagement for specific pages and features, and gather data regarding factors that affect the performance of the app. This allows the developers to enhance the performance and increase the number of users.

  • Personalizes the User Experience 

With Big Data, you can optimize the search, make it less taxing, and more intuitive for users.

You can evaluate the users purchase patterns, social behavior, and demographic data to alter your business tactics as per their needs. You can even add features in the data to offer fast and smarter self-service.

Personalize the user experience as much as you can, as this will increase user interaction.

Using Big Data, you can nurture the features via various formats and structures offered by app development companies.

Final Thoughts

With cut-throat competition in the industry, it is essential to invest in quality hours. This is because the influence of Big Data on companies building mobile apps is ready to alter the pattern of operations.

You will also have to hire mobile application developers with a complete understanding of Big Data to stay ahead in the competition. For example, to hire flutter developers with great experience might take you a while. 

In the future, with advancements in technology, meaningful mechanism, excellent digital media channels, and innovations, the phase of development is going to be more efficient.

Big data for small businesses

Big Data is now becoming a marketing tool available not only to big business, but also to retailers, services, and more. Instead of advertising for everyone, analyzing specific data makes it easier to target your customers, optimize costs, and drive sales.

Big Data is a large unstructured amount of information that is produced and stored in the digital world: where and how many people are there, what actions they take, what sites they visit… Retail, banks, mobile operators, insurance companies have huge amounts of data about each of us. All of this data can be used to evaluate consumer behavior and to generate analytical reports, build forecasts for business development. The analysis of this data is used in the field of finance, logistics, marketing to predict trends in consumer behavior. Your “individual” offers are formed by retailers precisely through the processing of information about previous purchases.

They use technologies such as cloud computing, mobile and social technologies, machine learning, the Internet of Things. Business analysts analyze large amounts of data, interpret it for a deeper understanding of customer needs and improve business processes in real time.

Of course, it is either too difficult or impossible for ordinary entrepreneurs to do it on their own. However, their services were offered to them by mobile operators. Kyivstar and Vodafone have a wealth of information about their customers and do not hide that they are using Big Data to create their own services. And recently, big data products have been offered to entrepreneurs and other business segments.

We are looking for a client

With big data, knowing your customer’s habits and preferences, you can best identify locations for new stores, business units, and more. The report will show you where these people are most often. If the business is important to the maximum permeability of the place, then it will help Heatmap (“heat map”) – areas where the number of people, judging by the number of smartphones, is the largest. You can view the current locations of branches, points of sale or ATMs on the map, and add and save new objects. By the way, such reports will be useful in planning promotions. For retailers, these cards are a way to determine the best location for new points of sale. When they open, they will be relevant to analyze the potential customers and targeted SMS-mailing.

Good offer

If an entrepreneur is planning to grow their business and reach new customers, Big Data will allow them to find their target audience and make a relevant offer to them. This requires the mobile carrier to have an existing mobile number database for today’s business customers. An analyst can process big data about their behavior in the company and create a consumer portrait. For example, an operator can analyze the journeys and movements of subscribers, their age, gender, income, the presence of children and a car locally (home, work, and weekend), what sites, locations, or locations they visit (here you can add competitors’ addresses, for example) whether it has a competitor discount, receives SMS from them, or dials their numbers. The following is an automatic sampling from its subscriber base, which are similar behaviors (so-called Look-alike audiences) who are not yet customers of the company.

The next step is to make these people fit the needs of the offer via SMS.

Having a portrait of a client, an entrepreneur can apply the aforementioned reports to target advertising across different channels of communication. For e-commerce, the chain can be: customer segmentation by specific attributes – target segment analysis – search for a target-like segment – communication with potential customers.

Examples of technology use

Operators talk about successful cases of using big data for business. For example, a network of home goods and cosmetics stores wanted to determine the number of potential buyers who live and work within a 3km radius of the stores, to identify their portrait for further attraction. Having analyzed more than 200 parameters, including distance from the store, socio-demographic characteristics, smartphone availability, online behavior, they built the model and identified 4 segments for further targeting. Conducted promotional campaigns with individual offers for each segment. Analysis of the results showed a significant increase in conversion. Becoming aware of discounts, sales, discounts, or special offers for a specific CA with Big Data becomes much more effective.

The filling station was tasked with finding new customers. The owners of the car were found and informed about the opportunity to receive cashback and become a member of the loyalty program. A similar target audience searched for and found a auto parts store.

Among the regular users of the service are coffee shops that send invitations to middle-income people who