Data Analytics Explained in 4 Categories!
The world of technology is well known of his complex and difficult terms. Many people especially from the “outside” have a hard time to understand what you mean. The world of data is likewise. The combination of technology, science and mathematics makes it sometimes even harder to get it straight. I already wrote an article about how analytics can drive you crazy. However let us start at the beginning by explaining different possibilities. In this case about data analytics explained I will try to give a clear view about the varieties. For this article I have to say a special thank you to Ramesh Donta who has written an earlier post about “Big Data terms” on LinkedIn in which I found the inspiration to do this.
The definition of Analytics
The field of data analysis. Analytics often involves studying past historical data to research potential trends, to analyze the effects of certain decisions or events, or to evaluate the performance of a given tool or scenario. The goal of analytics is to improve the business by gaining knowledge which can be used to make improvements or changes. By Business Dictionary
There are 4 different categories known in the world of analytics. Let explain them in the most simple way we can.
Is the basic of all analytics. For example if you tell about your spending of your income. Let’s say 50% is on rent, 20% on food, 10% on clothing, 10% on healthcare and the rest on other items, that is descriptive analytics. Just describing history in the form of data and numbers.
If you want to go deeper into the data you have collected from users in order to understand “Why some things happened”. However this is very laborious work that has limited ability to give you actionable insights. It basically provides a very good understanding of a limited piece of the problem you want to solve.
If you analyzed your income spending for the past 10 years. You can safely forecast with high probability that next year will be similar to past years. The fine print here is that this is not about ‘predicting the future’ rather ‘forecasting with probabilities’ of what might happen.
If we look at prescriptive analytics we can say that we use predictive analytics and descriptive analytics as the base line. Prescriptive analytics are build on predictive analytics by including ‘actions’ and analyzing the resulting outcomes to ‘prescribe’ the best option to improve. You can even extend this to big data sources such as weather and other factors such as inflation. Imagine how you can make data-driven decisions by looking at the impacts of various actions.
I hope you will have a clear view about analytics now. The different categories of “data analytics explained” all use the same basic sets of data and add some other sources to it if necessary. If I missed anything please do not hesitate to add.