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Data Analytics: A brief overview

Analytics!!


There has been a lot of talk about data and analytics in the past decade. But what exactly is data analytics? Why has it become a buzz word in recent times? If data can help companies make decisions, how can organizations take this approach? Why was this domain not prominent earlier?

Being a data science enthusiast and practitioner myself, I wanted to try answering these questions which I have been frequently hearing from people around me. This post is a compilation of my understanding of what is analytics along with my take on the questions in a brief and simple context.

WHY Data Analytics??


In the past 10 to 20 years, we have witnessed a tremendous growth in Digitization. Everything around us has been digitized in some form or the other. All these digital transformations have led to a humongous digital imprint in the world which can be used to harness a lot of insights for businesses ranging from retail, banking, manufacturing, healthcare and so on. This was not possible few years ago as there was no sophisticated framework for collecting, maintaining, processing and analyzing data. In the recent years, with the advancement in technology, this availability of data combined with exponential increase in computing power and affordable storage devices has paved way for an approach to decision making based on data. This way of decision making has also become widely accepted as it is more representative of the world around. 

WHAT is Data Analytics?


Analytics is the art and science of solving business problems using data. The process of data driven decision making is termed as data analytics. As in every scientific experiment, data analytics also involves steps where in a statement is put forth and an analysis is performed using statistical techniques to either prove or disprove the statement proposed. If we dig deep, anyone would be forced to think that data should be the protagonist in an analysis. But in reality, it is actually the business problem in hand which is the most important component in data science. Once we have a clear business problem or the right question to be answered, it becomes much easier to narrow down on the factors to be considered for the analysis and then to look out for relevant data and perform the analysis. An analysis is only as good as the relevance of data to the problem being considered.

HOW of Data Analytics:


Analytics in a broader sense can be classified into four categories i.e we can bucket every analytics problem into one of these four categories:

  • Descriptive: Involves understanding the data using descriptive statistics majorly to understand what has happened. Consider a dashboard that keeps updating real-time on the usage of resources at a job site. This majorly involves presenting the data in the best possible way to generate insights for business. Visualization tools play an important role in this space.
  • Inferential: Involves deciphering inferences based on the data by performing hypothesis testing. Consider a pharmaceutical company that is planning on introducing a new drug to the market. It is important for the company to make sure that the new drug has better results than the previous one already available in the market. A test of significance will be performed to validate if there will be a significant difference between the effects of the drugs. Statistical tools like R, Python, SAS, SPSS are commonly used to solve these type of problems.
  • Predictive: Involves the concept of Machine Learning and Deep Learning to make predictions for unknown inputs majorly to understand what could happen. Consider a real estate company that would want to fix the price of a new house in the optimal way so that the company can make the maximum revenue out of a venture. This would involve considering several factors that would affect the price of a house and then developing a predictive model for predicting the house price. Techniques like Machine Learning, Data Mining and Deep Learning comes into the picture in these type of problem statements.
  • Prescriptive: Involves the Subject matter expertise to generate actionable insights from the analysis performed. A market research analysis and recommendation can be considered as an example. This majorly involves answering the question of what can we potentially do if something happens or is about to happen.

There are several use cases for each of these types of analytics. But for now, this article presented a bird's eye view of why, what and how of data analytics. Hope it was helpful. Kindly leave your comments below and suggestions. Happy learning!

Cheers!
Renga



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