Everywhere we look today there is data. From a small business start-up to huge industrial settlements, data is generated in huge amounts. Analysis of this data can give very important outcomes. Analysis of this data, however, is broad and can be done in countless different ways. Business analytics involves using tools and techniques to turn data into useful business insights. This data can be in the form of spreadsheets, traditional business data sources, or in general large amount of data generated on all the social media platforms like Twitter, Facebook, or from other platforms.
Data is being generated steadily on all such platforms. We use several tools and techniques like statistical modelling and machine learning, with the hope that this will help us gain meaningful insights and draw the required inference from the data. There are a lot of buzzwords these days that closely resemble the meaning of the term business analytics – like Business Intelligence, Decision Science and Data Science but they all ultimately convey the same idea, that is, turn data into meaningful business insights. However, this data that is obtained cannot often be used directly for our analysis. There is a lot of noise in the data that we get. For the purpose of analysis, we need to clean that data before any technique of data analysis can be applied to it.
There are many types of analytics. Descriptive analytics deals with what happened. We look at historical accounts and information to analyze what happened. For instance, historical sales data of a sick company to figure out why the company became sick. Then there is predictive analytics which deals with what might happen. It essentially deals with the idea of forecasting and policy-making where we build statistical models to be able to analyse the future course of events. Lastly, there is prescriptive analytics. This gives solutions or future course of action. It uses certain descriptive and predictive analytics tools to identify and model the future. We might recommend decisions based on certain optimisation models and/or simulation. Each of these three types of analytics is important, but it gets more value-added as we go down the list.
The most important step in analytics is, to begin with a business problem that we are trying to solve. Like, the optimum price to boost revenue, or how to gain more customers by customising the product, or how to solve certain infrastructural bottlenecks in the supply chain, etc. The next step is to plan the work. Estimation of the effort that will be required to analyse the data obtained is important. Then we build a business case to put all this together, to get the support of the leadership. After this part of the business problem is sorted, we need to begin by asking questions about the data we want.
First, is it possible to obtain the data, because if there no data availability, then there is no point even thinking of analysis. Second, if the data is available, then what is the quality of the data, whether it is very noisy and beyond cleaning or can it be cleaned and made fit for statistical modelling. Most data obtained from real-world scenarios are often very noisy and need immense cleaning. Next, we also need to worry about the type and level of data – whether it is an individual basis or macro-level data. There are a lot of other problem-specific and method-specific points that we need to keep in mind before we can start the analysis.
Thus, once we have figured out the problems of the way to solve it and the possible sources of data and estimated the cost of analysis and interpretation, we can begin with the actual exercise. The first and most important thing is to get the data. Once we get the data, we need to store it safely and clean it thoroughly to get proper and unbiased results from our fitted models. After that is the actual process of analysis, model fitting, visualising the fitted models and finally, forecasting from these models.
There can be no scenario imagined which doesn’t involve the analysis of data in its different forms. A huge amount of data is being created at every juncture every single day. The mere size of this data makes it impossible for us to be able to handle or analyze and make sense of the data manually without the use of proper data analysis tools and techniques. Thus, it wouldn’t be wrong to say that analytics is our present, and our future as well.
Picture Courtesy- Imarticus