There is no doubt as to the potential value that big data can bring enterprises but some are still grappling with the business case to justify the investment. Calculating the return on investment (ROI) on big data can be likened to sticking a finger in the air to determine which way the wind is blowing!
Experts speaking at conferences, writers producing white papers, journalists writing news and suppliers offering software and hardware all agree that big data is here now and needs to be taken advantage of to help deliver a plethora of benefits.
Some have worked out how they see it being monetized, particularly those offering cloud-based services to manage big data and suppliers of supporting hardware and software - but the art of calculating how much revenue can be generated off the back of big data is still, to a degree, in the area of the ‘dark arts’!
A survey released late last year by Wikibon, an open-source research firm, found that the ROI of big-data projects is proving to be a big let-down for most enterprises with the average company getting a mere 55¢ return on every dollar it spent on big data when expectations on ROI are at around the $3 to $4 mark!
It also reported that 46 percent of big data practitioners report that they have only realized partial value from their big data deployments. An unfortunate 2 percent declared their big data deployments total failures, with no value achieved. It found there were three compelling reasons for this struggle to achieve maximum business value from big data. These were:
- a lack of skilled Big Data practitioners;
- ‘raw’ and relatively immature technology.;
- a lack of compelling business use cases.
Wikibon’s analysis indicates that enterprises that have achieved significant value from big data are those that address these issues at the onset of new big data projects. These projects are generally not initiated by IT but driven by line-of-business departments, often marketing, and focus on small but strategic use cases.
So it would seem that marketing, often with big budgets to play with, might be in the best position to justify and implement big data projects, and other departments might be advised to support marketing in its endeavours.
Now that focus has swung squarely back to the customer in almost all enterprises, especially those addressing the digital services space, big data has become all encompassing in utilizing any data that touches the customer – and that doesn’t leave much.
The much discussed ‘customer experience’ is affected by service levels, ease of purchasing, the complete delivery cycle, loyalty points, easy payments, access to customer service reps, online self-care, etc. The list goes on. If it affects the customer or the customer’s experience it will be found in the most basic big data implementations, almost as a matter of course.
For marketing, there exist many use cases that support big data investments for campaign design and management, customer profiling and management, churn analysis, risk modelling, sentiment analysis, social graph analysis push-marketing, personalized offers, and much more.
It is also markedly easier for marketing departments to analyse revenue gains from any activity because most are related directly or indirectly to revenue generation. And marketing departments are usually adept at making the figures look good, after all, isn’t that a form of marketing in itself?