The business case for Big Data – Part 3: Mind the Gap!

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Today, an organisations ability to continuously blend external sentiment against internal operational efficiency is tantamount to survival. Your big data business case is nearly complete having focused on the revenue potential at your organisations busiest points of interaction, then the cost potential, through on-going reduction of your back-office structured IT interfaces. Now it’s time to focus on the […]

Today, an organisations ability to continuously blend external sentiment against internal operational efficiency is tantamount to survival.

Your big data business case is nearly complete having focused on the revenue potential at your organisations busiest points of interaction, then the cost potential, through on-going reduction of your back-office structured IT interfaces.

Now it’s time to focus on the risk aspects by applying three little words heard daily when travelling across subterreanean London.

Mind the Gap!

I am sure that those familiar with traveling on the London Underground will have heard this phase numerous times on arrival at a station (It reminds individuals not to fall in between the train and platform and, should only need common sense rather than the application of data science!).

This metaphor is extremely useful however, in looking at the current operational risks across your organisation which if managed and subsequently intelligently analysed, further augment your business case from a benefits perspective.

Figure 1:  Understanding your operational gaps leads to further big data opportunities

Taking the example in Figure 1 for the COO and CMO, which depicts two of the organisational quadrants illustrated, and, is the classic scenario where organisations that continue to build from an inside-out COO-perspective (in this case building products that we are good at building), may well be devoid of the changing needs of consumers as the market evolves (CMO-perspective).

A big data business case here, could be built on the following hypothesis:

‘Are we building the right products to meet the current market demand and if not, do the required products match our product development capabilities?’

Finding ‘unknown unknowns’ is art not science

Donald Rumsfeld’s infamous phase leads us on a journey that will require a data science solution that blends market sentiment data (what consumers think of our products) against, an analysis of what we can and cannot build with our current processes and in-house expertise. Equally, we would also need to consider the potential revenue opportunities of adapting our products against the operational costs required to effect this change.

This would lead to a business case galvanised against a key business vision and decision at the CxO level; If we adapt our products to meet anticipated demand, our revenue uplift would be X as opposed to ‘staying as is’ where it would be Y. Job done right?

Wrong! This would be a perpetual cycle based on the application of numerous data science algorithms, consistent and actionable metrics and finally, continuous business activity monitoring (and benefits realisation calculations) to continuously to stay ahead of the market.

Similarly, the more gaps you combine into a single improvement process, the more points of business interactions you positively influence and, the more back-office interface savings you can drive. In short, although our business case has been constructed in 3 sequential steps, it is in fact the risk component that leads to revenue and cost opportunities further down the line.

Your big data start-up begins here!

If your organisation mitigates its key risks intelligently, it stands to reason that this will improve new customer closure, reduce existing customer churn, optimise the costs associated with the provision of service and in time, with continuous application of an evolving concept I am calling ‘Data Art‘, will make your culture more customer focused than ever.

You now have the basis of your big data business case for cost, revenue and risk, have adopted the ideals of the ‘Big Data Start-up‘, are focusing on ‘Right Data rather than Big Data‘ and should now be able to progress your initiative. Remember though,

Even the best algorithms, people and the tightest statistics mean NOTHING unless, you can make a story our of your findings.

I will move on in my next blog to discuss the emergence of ‘Data Art which alongside a sound foundation of  data science will increasingly enable organisations to make big data meaningful, tangible and actionable.

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