Analytics is simply the use of numbers to decide on business problems / situations.Thus, in a world where there are huge ERP systems, Internet information, Mobile apps etc. there is a large volume of data that is created and stored by an organisation . The old way of work was – if you need to make a decision, call the person who has experience in that area and take his advice. Was it the best way? Perhaps not, because human beings develop biases basis the atmosphere / situations / education they have been subject to. Also, it has been found that though a human being can effectively judge the effect of one factor on an outcome, he /she finds it difficult when the number of factors are many and the data is huge. Better decisions are made with the use of statistical techniques which allow us to work on the data and come to a conclusion.
The next question often is – So what is the type of ‘use of numbers’ that we are talking about? Will I have to sit and do maths again?
The last decade has seen the advent of SaaS (Software as a service) in all walks of Information gathering and manipulation . Thus, Analytics systems now are button driven systems which do the calculations and throw up the results . An Analyst or Data Scientist has to look at these results and conclude / make recommendations for the business to implement. For example, an ICICI bank wants to sell loans in the market. It has data of all customers who have taken loans from it over the last 20 years . The portfolio is of , say , 1 crore loans . It now wants to understand which customers should it give a pre-approved loan offer.
The simplest answer may be – all the customers who paid up on time every time in the earlier loans. Let us call this set of customers Segment A .But on analysis you may find that customers who defaulted but paid up after default actually made more money for the bank because they paid Interest + Late payment charges. Let us call this set Segment B .
Hence, you can now say that you want to send out the offer to Customer A + B.
However, within Segment B there was a set of customers who you had to send Collections teams to their house to collect the money. So they paid Interest + Late payment charges- Collection cost . This set is Segment C.
So you may then decide to target Customers A+B –C.
You could do this exercise using Decision Tree software which cut your data into segments for you .
The last question that we will tackle in this article is – What does the work day of an Analytics professional look like ?
A typical work day may look like the following :-
He will walk into the office and be told about the problem that the business needs his inputs on
He will determine which is the best way to solve the problem
He will then gather the relevant data from the large datasets stored in the server
Next, he will import the data into the analytics system
He will run the technique thru the software (SAS, SPSS, XLSTAT etc.)
The software will produce the relevant output
He will study the output and prepare a report with his recommendations
This will be discussed with the business
The companies which recruit large teams in Analytics include TCS, Accenture (Mumbai), McKinsey Knowledge Centre (Gurgaon) , Genpact (Bangalore and Gurgaon) , Novartis (Hyderabad) , Dell (Bangalore), Capital One (Bangalore ), Capgemini (Mumbai) etc. It is expected that there will be a shortage of Analytics resources in the world (and India) in the next decade.
Subhashini Sharma Tripathi
Subhashini has a decade of experience across roles in Analytics in Retail Finance and Banking. These roles have been across Risk Management , Collections strategy , Fraud Control and Marketing in GE Money, Standard Chartered Bank, Tata Motors Finance and Citi GDM . Her area of interest is the integration of results / outputs of Analytics with Business Decisions – Tactics and Strategy.
She is currently active in the Analytics Training and Consulting arena.