Do you (really) unlock the value from your Big Data?

Last Wednesday I was trying to look for a specific slide in one of the many PowerPoint presentations I have on my laptop, and kept stumbling across new presentations that I had completely forgotten I had there…well, there are tactics that I developed in the past years on how to organize my documents better, however I still from time to time get lost and frustrated when I can’t find what I am looking for, although I’m sure I have it there… somewhere...

If it’s any consolation, none of us can really handle the data assets that we collect, edit and create on our personal laptops, so imagine how troublesome this is for a large Enterprise.

We find that enterprises have several ‘blind spots’ - not knowing what they should already know - because they don’t know what they have. They have the data; they just can’t easily get to it. This is the main pain point with unlocking value from Big Data.

In my mind, the situation that organizations are facing today with having access to multiple sources and touch-points of data will just go and worsen over time. As the offering of data platforms out there is very wide and probably will expand even further, the need for analytical models to gain insights from these data platforms will also increase. Yet, is having a Big Data platform enough to unlock the hidden value of information?

Who can ask the right question?

There is another dimension of complexity that organizations are bound to encounter, even after they manage to connect all their data into a central location and extract insight from it.   The next stage will be “tell me something I didn’t know I should look for, and that I can actually do something about…”; or in other words, the rising challenge will be about “who can ask the right question?”

In essence, the queries you will run on the data (that you have access to), the conclusions you will draw based on the answers you got, and the action you take upon them - will serve as the key elements to differentiate the level of service and experience your customers will be getting.

The common (and conservative) analysis that is used today is mostly at the level of running queries on the data and scheduling operational reports for management. At this level of “business intelligence (BI) world” - you have the ‘glasses’ to see what happened, yet, you are not clear on why it happened and what you should do next to improve it / avoid this from happening again. In order to tackle the ‘why’ and ‘how to improve’ questions, you will need to ask different and better questions that are looking at other data elements. These are data elements that can help you get the right conclusion fast; rather than ‘noise’ - adding mystery and confusion to the ‘why’ and ‘how to improve’.

I believe you should be asking different type of questions on relevant customer data (i.e. Customer retail visits, CRM agent notes, billing information, Web logs, Voice data, business data, IVR logs, emails, mobile app and more). Then, you ought to form a customer profile based on customer resolution across these multiple touch points and build automatic sequences of the data based on customer events and intent.

These sequences should be assembled in multiple layers to address multiple business scenarios. The idea is to sequence these large amounts of data in order to find the intent in these behavioral patterns which eventually can assist the business to take next best action in offline- and real-time. For example, sequence the data according to customer events timeline so you can understand the entire customer journey, sequence by repeat callers, sequence by repeat topics, by intent, by propensity to complain, by propensity to call, to buy and more.

I couldn’t find the exact PowerPoint presentation I was looking for, but I thought that this type of implementation (asking what slide I was looking for, rather than search by the file name) can yield significant business value, from reducing the manual effort to search and retrieve data.

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