Do you have bigger concerns than big data?

If you read much of the fluctuating hype around Big Data there’s a common theme in the negative camp that questions how much real, tangible value there is in Big Data analytics. Sceptics question the challenges in putting an ROI on something which by nature is exploratory. A key premise of Big Data’s distinction from traditional analytics is that whilst the latter is about providing answers to known questions, Big Data is about finding the questions you hadn’t thought of. With such ethereal qualities it’s not entirely surprising that it’s easy for the media and analysts to whip up some copy that creates fear amongst those embarking on a big data mission. And just like traditional analytical projects, tales abound of huge investments into projects which were culled after sinking significant amounts of cash, time and people with no significant return. Not surprising then that it’s easy to find nebulous articles, and research into business perception (that's been fuelled by such articles) that make vague claims about the disappointment of big data, without much tangible evidence.

Not surprising then that any board contemplating a big data project will be caught between the rock of ‘we have to do this Big Data thing, because everyone else is’ and the hard-place that demands that every investment satisfies the corporate ROI evaluation hurdles. The “Build it and they will come” philosophy never worked for Data Warehousing, and the “trust me, there’s value in the data lake” won’t for big data.

Many organisations have rushed too quickly into Big Data technology evaluation and got fingers burnt, satisfying the geeks desire for hot technologies to feature on their CV, but not providing business value.

The most viable approach is one of focus; starting with a clear use case, thinking about specific priority business challenges, that would show real-value ‘if’ data could help to resolve, or at least provide greater clarity or even marginal improvement. Then based on a short-list of these ideas develop a feasible set (small in number) that can be trialled. You may call this a pilot, a proof-of concept, a proof-of-value, but it means a fairly rapid, low cost, rapid approach to exploring the data and identifying if there is some potential value.

As the examples below highlight (from this article), there are plenty of tangible examples of where big data provides real business value, and there are few ideas in this list that don’t fulfil the above criteria:

• a focus on a known, significant business problem, that has tangible financial implication
• an exploration of the data (and that’s all possible data) to see which elements add value, and to discard from the solution, those that don’t

There’s a fair chance that if you don’t find any value in the potential data sets to help resolve one of your most critical business issues, then you’re not going to have a business to worry about for much longer. Your worries are bigger than Big Data.

AIG (Insurance): using a wider data set (including ‘unstructured’ like handwritten claims notes, to enhance fraud identification
AMEX (credit cards): enhanced predictive models – identifying ¼ of accounts that will close within four months
Delta (Airlines): tackling the frustration of lost baggage – and expense to the airline – by providing customers with access to baggage tracking data via a mobile app

FT.com (media): analyse content preferences to improve personalisation

Huffington Post (Media): use real-time analysis of social media trends, recommendation and moderation to enhance personalisation

Kroger (Retail): understand customer behaviour – to drive loyalty and profitability

Southwest (Airline): using speech analytics to understand (and improve) interactions between customers and personnel.Understanding online behaviours and actions, to improve offers and increase loyalty, revenue and profit.
Red Roof Inn (Hotel): using a range of data to pinpoint travel hot-spots (bad weather, cancelled flights etc) and enhance targeting of 'stranded travelers' with hotel offers

Sprint (Telco): analysing network traffic to improve quality and customer experience
Tesla (Automotive): collecting sensor data, increasingly in near-real-time, to identify performance issues, recommend maintenance schedules and enhance R&D; all to improve customer satisfaction.

UPS (logistics): using data (telematics, routes, idle time) to enhance fleet optimisation – fewer miles, less fuel, lower costs

Wallgreens (Healthcare): ensuring patients collect their prescriptions – to help them stay on their medication – and prevent future illnesses