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Data Analytics
17 min read

Data analytics - Bring trust with precision

Data Analytics is a branch of STEM (Science, Technology, Engineering, Mathematics) that studies raw data and identifies meaningful conclusions like trends, valuable metrics, and other information to interpret the data. The setup requires database servers to hold the statistics and software applications with specified algorithms to run the data and form meaningful conclusions to make more informed decisions. 

Any data can be subjected to analysis and will help you to get a better insight of the presented information, but data analysis techniques are generally used in businesses to make vital customer decisions. It is also widely utilized by scientists and students to verify or disprove the experiment's hypothesis.

Data vs Big Data 

Data is a collection of qualitative or quantitative variables that can be structured or random, theoretical or numeric, but provide us with information.

It's pretty straightforward to conclude big data contains a large amount of data. Big data has a greater variety and volume of valuable information, with more complex data sets collected frequently from new data sources. To put it simply, we can comprise big data as the three 3V's: Volume, Variety, and Velocity.

Classes of Analytics

Data Analytics

Data analytics is a broad term covering all types of data analysis to transform raw data into useful information for some conclusion, but it's not oriented to reach any immediate goals. Data analysts primarily utilize statistical modeling and predictive algorithms to create reports and visualizations that are further used to make required diagnoses and conclusions.

Business Analytics

According to a Forbes study, at least 53% percent of companies use data analytics in their company presently. The primary aim of business analytics is to examine and analyze the data and then transform their results into trends and insights that ultimately help executives, managers, and operational employees make better deals and plan the immediate business goal more appropriately.

Business analytics examples include managing patient information and insurance database systems in the healthcare industry. Or to streamline fast-food restaurants by monitoring peak customer hours and making orders in advance and ready to go, to ensure customer satisfaction and avoid the peak hour rush.

Advanced Analytics 

Advanced analytics refers to sophisticated methods and tools that can help you get the most of the given raw data. Advanced analytics techniques can forecast trends, analyze customer behaviors, and set up sales objectives accordingly.

Big eCommerce giants like Amazon and Flipkart rely on advanced big data analytics. They data mine sales from their website and rely on text mining to analyze documents, emails, and other text-based content using automated machine learning techniques and predictive analysis. This ultimately leads them closer to fine-tune cart recommendations which will increase the chances of customer purchases. With many other similar websites available, personalization and knowing the customer channel better has helped them remain on top of the game.

Evolution of Analytics

Data Analytics is not entirely a fresh concept; back in the day, businesses collected customer information from reward program registrations, sales reports, advertisement returns, and other information. They manually entered the data and organized them to understand the customer trends and devise future business strategies. 

Business Intelligence

Business intelligence offers a way to examine the existing data to understand trends and then decide which factor streamlines the effort. It also allows you to create reports and dashboards comprising the data. It provides descriptive information about the situation enabling the members in positions to make informed business decisions.

Business intelligence doesn't make any prediction, it just informs about the metrics, but these insights allow the officials to work smarter and make appropriate decisions.

Big Data 

Big data is a recent concept that came into play because of the boom in technology and the internet with smartphones, mobile applications, social media, the cloud, and IoT(Internet of things). Big data is the essential tool that drives large IT industries and businesses of this decade. 

The scope is unlimited to various sectors like travel and tourism, finance, telecommunication, virtual assistant, machine learning, and many more. With these broad sectors, the big data market is expected to have annual revenue of  $274 billion US dollars in the upcoming year 2022.

Government agencies can utilize the big data collected from Aadhar cards and ration cards to segment the population and develop beneficial schemes and work opportunities based on age, demographics, and income.

Data Science

Data Science initially started with statistics for businesses and has evolved to include machine learning and artificial intelligence applications. It was purely mathematical, but now data science can run algorithms for different forms of data, from infographics to text. Data scientists run algorithms on systems to extract information and insights from structured and unstructured data and apply that knowledge into actions across various application domains. They also design new algorithms and procedures for processing data in new ways and get further analysis. 


Automation has become part and parcel of big data analysis, increasing the amount of information to process. Automation has come to the rescue and made the process efficient and agile. 

Both the data and the procedure being complex, it's not suitable for a data scientist to work manually and run the algorithm. Automation has not only reduced the work, but it can also generate reports efficiently and visualization and graphs. It reduces human error and precisely interprets the current data, so there is no benefit of the doubt and that teams can make accurate business decisions. 

Types of data analytics based upon the results 

1. Descriptive Analytics

Descriptive analytics summarizes data points that happened previously so that trends emerge and helps the team get insights. For example, has the sales improved? Or statistics of female customers buying a particular product. 

It gives you a conclusion about the distribution of your customer data and helps in identifying errors and outliers. 

2. Diagnostic Analytics 

Diagnostic analytics investigates the root of a particular problem or case. Why did this happen? So it involves comprehensive data about the specific scene and hypothesizing to find the root cause. 

For example, in health care, symptoms correlate to a particular condition, and without knowing their medical history, they can't be fully diagnosed. Or in business, like how the new advertisement affected the sales. 

3. Predictive Analytics

Predictive analytics takes previous data and feeds it into a Learning model or algorithm that traces key trends and patterns. Then, use the data, interpolate the graphs, and predict what will happen in the immediate future.

If you take the case of predicting stock market trends and the future price, that will allow the investor to yield significant profit.

4. Prescriptive Analytics 

Predictive analytics gave you an idea of what will likely happen in the coming weeks. Prescriptive analytics offers you the next course of action, suggests various possible outlines, and the potential implications for each scenario. 

If you are sensing a falling trend in the stock price, will you pull out from the company or sell the stocks to someone else, choose profitable solutions, and allow the investor to make sound decisions.

Big data is stored in two different forms as data lake and data warehouse. The data lake is a wide body of raw data, which doesn't have any targeted purpose. Whereas on the other hand, the data warehouse is a structured and well-organized database and has been processed to serve a specific goal or vision.

Data lakes primarily store raw, unprocessed data, while data warehouses store processed and refined data. Data lakes are in complete disarray, so they typically require a much larger computational space. It requires a data scientist and specialized tools to understand and translate it for any specific business use.

Data warehouses are processed and have specific uses like any business or organization to collect information, so it has to be reliable and easily understood by all the employees by just looking at the charts, spreadsheets, and tables. The data warehouse is easier to decipher, but the organized architecture limits the flexibility and makes data warehouses difficult and costly to manipulate.

Organizations might use both data lakes and data warehouses to harness their big data. Let's look into how various industries exploit the Big Data benefits and how it has reformed their business strategies.

Telecommunication and Media

Telecommunications and the multimedia sector are the early and most prominent users of data analytics. There are zettabytes of data generated every day and handled swiftly using big data technologies.

Travel and Tourism

Travel and tourism also use this technology to forecast travel requirements and facilities for a particular venue from the reviews on google and other sites. And can optimize the business through dynamic pricing and seasonal offers to attract more customers.

Finance and Banking

The banking sectors use big data analytics to determine customer behavior based on investment patterns, shopping, insurance, bonds, and loans. These obtained financial backgrounds allow them to target the right customers and enhance branch productivity; tools like TCS Optix offer contextual insights to a bank, rendered via enterprise applications and embedded analytics, and enable quicker adoption.


Big data has started to impact the healthcare sector with the help of predictive analytics and leveraging real-time data streams. Penn Medicine, a multi-specialty hospital-based in Pennsylvania, developed a dashboard that displays real-time data streams of electronic health records (EHR). 

There is an inbuilt system to alert respiratory and nursing staff when interventions are needed. That can improve personalized healthcare and provide each patient with required visiting and care from the medical professionals. 

Data analytics has multi-purposes across various sectors and plays a crucial role in tech companies, government offices, and finance. It is used to build features like machine translation robotics, speech recognition used in smartphones, and these analytical tools are running the digital e-commerce economy with optimization strategies.

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