Thanks to Nirav Dalal for his guest post this week. Nirav is currently working with a large KPO and has close to 10 years of overall experience in the analytics and modeling space. In his role he is responsible for delivery, solution development, business development, research and innovation. He has worked extensively in the areas of sales and marketing analytics, consumer analytics and predictive analytics in the CPG / Retail space. He has a master degree in Statistics and PhD in applied mathematics. Nirav writes about the retail analytics industry covering topics on specific solutions, to generic trends in the industry. In this post Nirav talks about how to make business sense out of data.

If a seasoned analytics professional is asked about what he understands by analytics or what does analytics stand for, the probable answer would be Insights. Analytics is all about providing insights to all kinds of business that employs analytics or analytics professionals. The primary aim of any analytics activity would be to help the business make better decisions through critical insights coming out of models, analysis etc.

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Analytics is widely used now in various fields ranging from retail, banking, telecom, automobile, medicine etc. Businesses in retail use analytics to drive sales, improve their margins, playing in new markets etc. An interesting application in medicine is about predicting serious diseases in infants based on their vital parameters during the first few days or weeks.

 There are varieties of methods applied to achieve the goal of deriving insights from data. These include different modelling techniques, software’s like SAS etc. People working in the analytics field often get attracted to sophisticated models, software’s and techniques to achieve their end result. There is nothing wrong in using these if need be, but the critical question to ask is whether the business question(s) is answered or not. Econometric and statistical models, tools like SAS, R are all means to achieve the goal of business insights through analytical techniques. These exercises are not an end in themselves; they form a part of the larger exercise to manage business better.

 Suppose a manufacturer wants to enter a new market and would like analytics to help him have a smooth landing. A very obvious analytical solution would be to build a forecasting model with drivers of sales. This would achieve two things from the manufacturer’s point of view: they get a view of how the category / market would behave in the foreseeable future and tell them the key drivers that affect the category sales. If the model throws accurate forecasts but is unable to retain any drives of sales, it is not fully helping the manufacturer. This is a classic case of having a great model but one that is of little use to the business. So the client knows that yes this is a good market in which to play but does not know what are the critical aspects which affect sales and he cannot tune his offerings accordingly.

 If an analyst builds an almost perfect statistical model which is technically brilliant but provides no business insights, you might as well throw the model in the bin.

If you enjoyed this article of Nirav’s please do take a look at his other blog post on what an analyst’s job really entails. http://analyticstraining.com/2013/getting-your-hands-dirty-with-data/

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