Data Analytics Applications: Types, Tools & Working Methodology

According to a survey, 89% of organizations use data analytics applications to improve decisions. Another one points to the fact that companies using data analytics are 2.5 times more likely to make better decisions than those who do not.

Sooner rather than later, businesses are realizing the true importance of data analytics. Data analytics is significant because it allows decision-makers to create an evidence-based strategy, understand customer behavior, and increase overall productivity. Businesses that have embraced it are bound to reap the benefits as it gives a clear insight and a competitive edge to their business.

This blog discusses the diverse aspects of data analytics and its applications across various sectors.

What is Data Analytics?

Data Analytics, simply put, is analyzing raw chunks of data to get meaningful and actionable insights. Think of it as a form of business intelligence used to solve specific challenges.

Say you run an e-commerce company. You might want to understand your customer behavior and patterns. Using such insights, you can elevate the entire customer experience. Well, data analytics lets you do just that. Data Analytics tools evaluate past trends to predict future ones. In the process, you get better and more informed choices instead of guesswork.

What are Data Analytics Types?

Chiefly, data analytics are of 4 types. Let us discuss them one by one.

Descriptive Analytics

This type of data analytics evaluates past data and trends. It gives a snapshot of the past by summarizing and interpreting data. This also involves data aggregation, mining, and visualization.

Diagnostic Analytics

As the name suggests, diagnostic analytics diagnoses past events. It goes a step further and looks at the "why" behind the events and trends. Consequently, it analyzes data and finds out the causes, letting businesses understand why it happened.

Predictive Analytics

Using statistical models and machine learning algorithms, Predictive Analytics analyzes historical data and identifies patterns to predict future events and outcomes. Predictive analytics is well-poised to manage trends and probabilities.

Prescriptive Analytics

Once you are aware of future possibilities, you want to know the best possible actions. Prescriptive Analytics combines big data insights with optimization techniques to recommend the best course of action.

What are Various Data Analytics Tools?

Big data implementation utilizes various data analytics tools. Let's talk about some of these widely used tools.

Python

One of the majorly used languages for data analysis, Python is an object-oriented open-source programming language. Through its built-in data analytics tools, Python is used to streamline, analyze, and visualize data.

R

R is primarily used for statistical computing and graphics. This open-source programming language is well-suited for statistical analysis, data visualization, and manipulation.

Tableau

Tableau is a widely used tool in the application of business analytics. It is one of the most popular data visualization platforms out there. It is known for its easy user interface and seamless data-set conversion into graphics.

Power BI

A business intelligence tool that supports different data sources. It provides immediate insights and answers your data analytics questions.

SAS

SAS (Statistical Analysis System) is used for business intelligence, advanced analytics, and predictive analytics. It finds application in several uses, including managing, cleaning, and modeling data. SAS is also used to retrieve, report, analyze, and visualize data.

How Does Data Analytics Work?

Data Analytics' importance owes to the fact that there are multiple benefits of data analytics. Let's first understand how the entire process of data analytics works. The data analytics process involves a series of steps.

Data Collection

The first step is identifying the data you need and assembling it. The data could be from a single source or multiple sources. If the data is from many sources, the analyst must combine it using data integration.

Regulating Data Quality

The next step is to adjust the quality of the data collected. This is done using data profiling and cleansing. Apart from maintaining quality, one must ensure the implementation of data governance policies. This means that data is correctly used and is according to the corporate standards.

Building an Analytical Model

Once the data is cleaned, the data analyst and the data scientist build an analytical model. This is done using analytical software and predictive modeling tools. The model is then tested with an experimental data set. It is done until one receives the intended outcome. Lastly, the model is run against the intended data set.

Presentation

Finally, the results are presented before the end-users and business team in an easy-to-understand manner. To do this, tools such as charts, bar graphs, and infographics are used.

What are Various Data Analytics Applications in Different Sectors?

Healthcare

Data Analytics ensures optimum resource utilization, diagnosis, and better patient care. By analyzing patient data, hospitals can identify high-risk individuals. They can also personalize treatments for every single patient.

Finance

There are multiple uses of data analytics in finance. From fraud detection to risk assessment to making investment strategies, data analytics can help you with anything. It also enables personalized advice for professionals analyzing their investment goals.

e-commerce

One major application of business analytics is in e-commerce. Through data analytics, customer behavior and their shopping patterns and preferences are identified. E-commerce companies can offer personalized recommendations to their users, thereby improving customer segmentation, satisfaction, and retention.

Banking

Data Analytics in banking can be used to track customer behavior, mitigate risks, and personalize banking services. Banks can also avoid potential frauds and recognize creditworthiness by running analytics on their banking data.

Manufacturing

Data Analytics enables predictive maintenance, optimizing production, and refining product quality in manufacturing. It also enables manufacturers to have real-time monitoring of production, which often translates into higher productivity and lowered costs.

Internet Searching

Data Analytics almost powers the entire internet searching industry. It sifts through vast amounts of data to give relevant search results. The data analytics algorithm continuously learns and adapts to user behavior, leading to more personalized results.

Is Data Analytics the Next Big Thing?

The future, they say, is exciting. Cliched as that may sound, it holds true for data analytics. Big data management is something to reckon with today.

Useful insights by data analysts can make a significant difference, influencing the growth of your business. Even small and mid-sized enterprises are warming up to the idea of analytics.

Once, somebody had remarked that we are creating more data every two days than we have done since the dawn of civilization.

Since so much data is being collected, it would be prudent to act upon it. Data Analytics is like looking for your target in high-beam light. Not using it is searching randomly with a blindfold in the dark.

How Can BluEnt Help You with Your Data Analytics Journey?

Launching a business and sustaining it requires equal amounts of energy and effort. While there is no shortcut to success, there is always an intelligent way of analyzing markets. Judging consumer patterns is easier when the needle is in the right direction.

Here is how we learned that important lesson. In October 2019, BluEnt assisted Gearbox during the launch of its currency gift card, Fortnite. Our teams managed data, analytical reports, and server maintenance on their behalf. What does that mean? Well, we gleaned activation and redemption data from several sources, consolidated it for 'big picture' ideas, and visualized it in a manner that allowed decision-makers to move on a real-time basis.

BluEnt believes in responsibility to our customers – for every action, outcome, and consequence. Our certified and experienced resources are dedicated to assisting you every step of the way.

Get in touch now!

Frequently Asked Questions

What is Data Analytics, and why is it important?

Data Analytics is the process of analyzing and interpreting big chunks of data to derive meaningful and actionable insights for business. It is important because using it enables businesses to make data-driven decisions, analyze customer trends, and improve operational efficiency.

Which industries find data analytics applications?

There are several industries that benefit from data analytics. Such industries include healthcare, finance, banking, manufacturing, transportation, e-commerce.