What is Data Analytics?

Data analytics is an inclusive term in the world of data science. It is the analysis of data collected from one or different sources. Trends, patterns, and correlations emerge from the analytics, which remains valuable for business practices. 
Firstly, data analytics allows data scientists to gain insights through careful examination of datasets. Management teams use these insights to make informed and strategic business decisions towards future activities. Without data analytics, all data, no matter the size and derived source, is useless.
Secondly, when companies apply data analytics to any big data collected, they can efficiently improve customer service and increase sales turnover. Companies use this strategy to boost competitive performance.

Two Components of Data Analytics

Big Data Analytics

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Processing data analytics needs two critical elements for any data initiative to succeed. These are:

Descriptive analytics

Descriptive analytics is the starting phase. It helps in describing historical and relevant trends found in the data. This analytics aims to answer the question of “what happened?
It’s both a component and a type of analytics too.
As a component, descriptive analytics measures conventional indicators like Return on Investment (ROI). Such an indicator employed differs from industry to industry. Although descriptive analytics doesn’t make predictions or informed actionable decisions, it summarizes datasets in an expressive and meaningful way.

Advanced analytics

Here, the process uses advanced and innovative tools to extract relevant data, make appropriate predictions, and uncover trends.
Tools used in advanced analytics include, but are not limited to, machine learning and classical statistics. Machine learning tools like natural language conversion/processing, sentiment analysis, and neural networks, and more, generate new insight and info from data.
Advanced analytics focuses on the question of “What if?
With the increasing popularity and use of large data sets, machine learning skills, and affordable computing power have made it easier to use these two data analytics techniques in different industries.

Four types of Data Analytics

Descriptive analytics

Types of Data Analytics

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What the analytics seeks is the answer  to the question, “what happened?” Under this technique, summarizing large datasets explains findings to all stakeholders. Descriptive analytics strategies help businesses to track successful performance or failures using KPI (key performance indicators) and metrics like ROI to gauge past performance.

Diagnostic analytics

Diagnostic analytics is the technique that seeks to answer why certain things happened. The process takes information from the descriptive-analytical level and further studies to unravel the root of the problem. Any performance indicator calls for more investigation to know why they got worse or better.

Predictive analytics

The predictive analytics technique focuses on events that would happen in the future.  Using historical data, machine learning, and statistical methods, the study of patterns and trends tracks the possibility of reoccurrence. The outcome will be very valuable.

Prescriptive analytics

The presence of this technique offers solutions to the problem. By employing insights gotten from descriptive analytics, we gain inspired decisions. Also, it relies on machine-learning tactics capable of finding patterns in massive data sets.


Data analytics is a critical aspect of data science that data scientists should understand. Its application in everyday activities is almost endless. New business opportunities arise as a result of the continuous generation of data.

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