Big Data has its chips stacked against it; a fear of numbers, lack of available skills, and simple confusion are numbered among the reasons marketers are weary of the concept. But as outlined at the Being Cool With Data event, it’s worth pushing through the teething problems.
Main barriers to Big Data in retail:
49% lack of relevant skills
39% lack of tools and technologies
36% cost of implementing
Main drivers to Big Data in retail:
64% new insight into customers
57% more targeted marketing campaigns
53% better planning and decision making
With a topic as ubiquitous as data, those who are championing it need to make it seem more appealing, or at least worth noting. Brand-e.’s event hosted at The Hospital Club aimed to enlighten the crowd on how to be Cool With Data.
A range of speakers from We R Interactive, Squawka, Adconian and BrainJuicer assembled to discuss the ways in which their companies make good use of data, offering some real-world applicable case studies. But undoubtedly the highlight, in terms of understanding the phenomenon, came from The Big Data Insight Group, whose most recent report findings on Big Data in retail are detailed above.
We understand the term Big Data to represent the burgeoning torrent of information that is now being recorded at such a rate that companies are left lost at how to suitably harness it.
In fact, what Mark Young and Dom Pollard – speakers from The Big Data Insight Group – suggest is that companies can realistically tackle it by considering the three Vs; Volume, Variety and Velocity. To state the case, they quote IBM, reminding us that “90% of the world’s data was generated in the last two years and 80% of that data is unstructured.”
Understandably alarming, the pair point to Gartner’s 2012 Hype Cycle (pictured) and show us that Big Data is only just entering the “peak of inflated expectation”, making it between two and five years from reaching its “plateau of productivity” where businesses will be competent and proficient in understanding it. In other words, that’s how long we must wait before seeing the positive implications of all this data in sectors such as healthcare and education.
One of the team’s most recent cases exemplifies how the data captured can add to swift and effective real-world application. Shazam, it has been found, is an accurate pre-cursor to the US Billboard chart. “We provide regular updates to record labels on what’s being tagged and at the end of the year we do a report to give our predictions for up and coming bands,” comments Jason Titus CTO of Shazam. “These are commercial arrangements that we have instigated. For the partner it’s about seeing who’s had a trajectory which would suggest they are going on to big things,” he tells The Big Data Insight Group.
There’s no doubt that it is still a numbers game, and this in itself presents another problem for businesses who are reluctant to take it up. The key to the progression of the trend is increased availability of data visualisation; more than just aesthetically pleasing, this space includes data visualisation company Bloom, who is extracting useful data displaying it in increasingly creative ways.
Young and Pollard suggest affiliates and partnerships with other companies as ways to attain more data. As if to accentuate this, Telefónica’s latest announcement states that the company is selling data insight to retail companies, where footfall, age and gender is offered on where the user goes once they have left the store, offering a competitive edge to those companies who are prepared to take action.
This in part uncovers the reasons for Big Data’s emergence in Gartner’s Hype Cycle and as the latest preoccupation with marketers. For one it is basic human nature to observe and understand habitual human behaviour (as well as being lucrative). Secondly the data itself is not constrained to large banking and financial organisations as it once was; capturing data is nothing new, but its democratisation means it extends beyond the hands of Mastercard and Visa.
Big Data itself can make companies more efficient in driving conversions through understanding human behaviour. But crucially it can be used for more anthropological matters, which is what we will spend two to five years waiting for; today’s business models and industries are waiting to be disrupted, which can only happen once we get more granular with data.