Your Order is Here! - Delivering Data

 So, we have so far explored data acquisition, data management, data security, data governance and data quality good practices. Again, ask Why? The answer is Analytics. This is the reason why we did it all. Because, the leaders of an organization will make decisions based on the analytics we provide. Their asks will vary, as I had mentioned earlier in my posts. Let's explore a little deeper.

One interesting thing that happened after I moved back to India after 25 years abroad, is when I got my Indian SIM card. A few days had passed and nothing out of the ordinary. Then suddenly, the calls started coming... "Sir, do you want a personal loan?". First, it was from non banking financial institutions (NBFCs) and then even from some major banks. The calls continue to this day, but I have better spam protection tools, no thanks to my phone provider!

Whoa! Wait a minute?! How did they get my phone number that I had registered only a few days ago? Before I go into the violation of privacy and data sharing laws aspects of this, the first thought that occurred to me was, "their analytics is not very good, is it?". 

Let me explain. Basically, a call center employee has been tasked with calling as many phone numbers as possible in a day to see if any one of them will say, "yes, I need a personal loan". This is the "spray and pray" method, if you ask me. Let me tell you why. First of all, the person who is calling me has no idea who I am. They don't know if I fit the financial profile of a person who needs a personal loan. They know nothing about me, other than that I am male ( they always address me Sir, so at least I know that they know my gender) and my registered phone number is xxx-xxx-xxxx. Depending on how much my phone company sold my data (without my consent), they might even know my age group. But other than that, they know nothing about me and the poor call center employee is filling a quota, with little to no financial gain.

Now, consider this scenario. This is very basic, but a possible scenario, in my opinion.

Now, consider how that phone call would go, if the call center employee had this information at her fingertips. She now knows that John Smith is married to Sally Smith and they recently had a baby. Looking at their account balances, she knows that the likelihood of either of them opting for a Personal loan is not very good. Your AI has already calculated all the permutations and combinations of their purchases and has come up with a recommendation of offering John a Credit Card and Sally a College Education Fund. The likelihood of both of them taking up the offer is high. 

Basically, the more "relevant and contextual" information we can put in the hands of our departments, the better off they are, in being able to run their business successfully.