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Types of Data Analytics

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Types of Data Analytics Types of Data Analytics

Understanding Data Analytics

Data analytics can be defined as the science of analyzing unprocessed data to get conclusions from it. Data analytic strategies help you collect raw data and identify patterns to draw practical insights. Data analytics is now a standard of the primary research of data experts. Many businesses also employ data analytics to help them make wise choices.

The phrase “data analytics” is very broad and encompasses many different types of data analysis. This can be applied to any form of data to gain information that can be utilized to improve things. For instance, gaming companies employ data analytics to create prize schedules for players which keep the majority of players active in the game. Similarly, various sorts of businesses use data analytics to meet their specific demands.
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History of Data Analytics

Spreadsheets were traditionally the preferred tool for manually comparing statistics and evaluating data for business insights. Beginning in the 1970s, organizations started utilizing electronic technologies, such as relational databases, data warehouses, machine learning (ML) algorithms, web search engines, data visualization, and other tools with the ability to facilitate, speed, and automate the analytics process.

Modern data sources have also put a load on traditional relational databases and other tools’ abilities to input, search, and modify enormous amounts of data. These tools were created to manage structured data like names, dates, and addresses. Modern data sources that produce unstructured data include email, text, video, audio, word processing, and satellite imagery. These types of data cannot be handled and evaluated using traditional methods.

With advancements in technology, new tools started getting into the picture and the whole process of Data Analytics now is fairly simplified.

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Types of Data Analytics

Data Analytics is generally of four types. In this article we’ll be broadly discussing all four of them:

Descriptive Analytics

Descriptive Analytics

Suppose you want to find out yearly cost changes, monthly sales growth, the total number of customers, and revenue generated per customer. These all measure what your business has faced in the past, to prepare a report for all of them you’ll be using Descriptive Analytics. Descriptive analytics is the use of various types of past data to make comparisons.

Let’s discuss a few cases where you can apply descriptive analytics:

These reports are developed by comparing current metrics to previous metrics and visualizing trends using raw data generated when people visit your website, adverts, or social media content.

Music streaming giant Spotify is a good example of how descriptive analytics can be used by a business. Spotify analysts track user behavior and their streaming patterns to determine which tracks are in high demand and accordingly use that data to prepare their trending list.

For instance, you might carry out a poll and discover that as users’ ages rise, so does their propensity to buy your goods. If you have repeated this survey over several years, descriptive analytics would reveal if the age-purchase connection has always existed or whether it was a trend that only happened this year.

Diagnostic Analytics

Diagnostic Analytics

Diagnostic analytics is a subset of analytics that seeks to answer the question, “Why did this happen?” Diagnostic analytics could also be used for data drilling and data mining. Companies may need to analyze various data sources, maybe including external data, to understand the core cause of trends.

Let’s discuss a few of the examples:

Predictive Analytics

Predictive Analytics

As the name suggests, Predictive analytics alludes to a future prediction. It combines diagnostic and descriptive analytics for identifying special cases and predicting future trends, making it an important device for estimation. Predictive analytics sits alongside advanced analytics types, bringing several benefits such as complicated analysis based on machine or deep learning.

Predictive analytics examples include:

Prescriptive Analytics

Prescriptive Analytics

The goal of prescriptive analytics is to advise on how to avoid a future problem or benefit from a potential trend. Prescriptive analytics uses cutting-edge tools and technology like machine learning, and algorithms, making it easy to implement and administer.

Examples of prescriptive analytics:

Data Analytics Technologies

Analyzing various kinds of data sets to derive useful information is known as data analytics. For organizational decision-making, data analytics is utilized to find hidden patterns, market trends, and consumer preferences. Data analytics involves some technologies such as:


Data is critical for any organization because it allows them to better understand their customers, improve their advertising strategies, and extend their bottom lines. There are many benefits to data, but you can’t make use of them without the right tools, therefore data analytics procedures and tools are very important.

While raw data is quite powerful, data analytics is what unlocks the potential to grow your business. As a result, we can state that data analytics is highly crucial in the growth of any business because it assists the organization in maximizing its performance.

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