The Reason Why Study Data Analytics in This Day and Age

With the presence of humongous data around us, it is obvious that we need to analyze this data for our benefits, either for gathering hidden insights or generating reports. The term that covers this process is called Data Analytics. As the name suggests it refers to the method of collecting both qualitative and quantitative information about a digital property and therefore allowing marketers or product managers or anyone in a digital company to make informed decisions about their customers.

So far we have used data analytics as a blanket term to describe how enterprises glean insight from their data to solve real problems and carry out their mission but there are actually four distinct types of data analytics that help enterprises answer different questions.

1. Descriptive Analytics - answers what happened
2. Diagnostic Analytics – answers why did it happen
3. Predictive Analytics – answers what will happen
4. Prescriptive Analytics – answers how can we make it happen

To better understand these terms, let us picture a hypothetical scenario where your company produces a survey with high hopes that over a thousand individuals will take it. However, you find out that only a hundred people took it and therefore you want to know the reason behind it. First, you will check exactly who did and did not take the survey, which is descriptive analytics. Next, you might look at other factors in the data such as the location of participants, the email platforms they used and the time of the day they took the survey, to determine why only a few people took the survey; this process is called diagnostic analytics. Then, if you want to do another survey you can compare the data from the first survey with what you know about potential respondents in your next test to predict how people will answer it, based on similarities and differences in demographics and processes; when you do this you are figuring out what will happen and that is predictive analytics. Finally, you will want to know how to increase the number of respondents on your next survey so you will use the past data to hypothesize ways to change behavior and reach more individuals who are more likely to respond, which is prescriptive analysis.

Now that we know what data analytics is, let us look at the steps involved in the process of data analytics.

1. Data Collection – Collect data points from multiple sources and store them in a tabular format so that it becomes easier to exploit this data and find insights.
2. Data Cleaning – In this step missing values or some unstructured data are either replaced with a number or that row is deleted to clean the data to be used for analysis.Data Manipulation – Manipulate data to create required features and variables.
3. Data Analysis – Logical methods and analytical techniques are used to segment the data into different groups and based on current and historical data to predict future business decisions.
4. Data visualization – Present data with insights after listing final outputs for given problem statements in visual or graphical form for easy interpretation.

Lastly, in the early days of data analysis, the tools used to perform analytics were only accessible to a limited number of employees working in large corporations, as licensing was required and companies had to pay hefty sums to acquire them. However, things have changed now, and there are quite a few open source solutions that data enthusiasts can use for their work. The two most widely used languages in data analytics are Python and R as they offer comprehensive suites of software facilities for calculation, data manipulation and graphical display.