C-levels want big data efforts to produce results and ROI. Here are three key points to keep in mind to fulfill these requests.
IDC forecasts that the global big data technology and service market will grow at a compound annual growth rate (CAGR) of 31.7 percent, with projected revenue at $23.8 billion by 2016. With so many companies entering the big data arena, the looming question is: How do you make big data actionable?
More specifically, C-level executives will want to know soon if the organization is getting to the "gold nuggets" of big data buried in those mammoth big data repositories. Big data workers don't want to be in the position of not striking gold. (For historical perspective, geologists estimate that 80 percent of the gold that prospectors pursued in the 1849 California gold rush was never found.) Here are three ways to avoid it. (Note: Points one and two should be considered before a big data implementation, if possible.)
1: Know your destination
Matt Ariker, Chief Operating Officer of the Consumer Marketing Analytics Center at IT consultancy and research firm McKinsey & Company, writes in an article on his employer's site that "...the promise of Big Data is so seductive that it often sends people scrambling after ever more petabytes or exabytes in the hopes of unearthing that golden insight that will allow them to grow and beat competition. This process untethers many a marketer as one question leads to another and to another….Tremendous insights do exist in Big Data. Companies that use it well are leaping ahead of their competitors. One of the big reasons for that, however, is that they have a very clear sense of what they want to do with all that data before they start."
Ariker urges companies to begin their big data mining processes with what he calls "destination thinking." In other words, marketing and any other departments planning to use big data should sit down with IT and identify the key performance indicators (KPIs) that it expects to improve in with intelligence gleaned from big data (e.g., building market share in underserved communities). Big data questions should be carefully articulated with the intent to uncover valuable insights that can immediately be put into action and to see if pre-defined KPIs are met.
2: Check your data samples
Princeton University's Center for Information Technology Policy (CITP) and the University of North Carolina (UNC) at Chapel Hill recently published research that warned marketers against placing absolute faith in queries of consumer data that they collect from social media channels such as Twitter and Facebook.
In UNC professor and Princeton CITP fellow Zeynep Tufekci's draft paper, Big Data: Pitfalls, Methods and Concepts for an Emergent Field, she challenges a big data methodology that heavily relies on social media for insights and compares it to biological testing on fruit flies, which are easy to use and breed in labs and can yield quick results. Tufekci says the problem with fruit fly testing is that it isn't necessarily representative of real-life scenarios, so it can skew research. Likewise, findings from Twitter, where only 10 percent of a typical marketing audience participates, or Facebook, which represents some market segments more than others, can skew results.
The lesson here is you can't dependably act on data that doesn't accurately reflect your market. You need to verify that it does first by confirming that it is truly representative of your entire market.
3: Find ways to create quick successes
One way to see a tangible ROI within 12 months of installing a new big data solution is to harvest the low-hanging fruit first. Most of us who have run marketing departments know that when projects pile up, promotional, product launch, and brand-building campaigns come first, and exploratory research comes later; this might make it possible for big data to make an early splash with contributions that there have never been enough time before to exploit.
There are also a growing number of cloud-based analytics providers that can help companies gain experience and confidence in finding gold nuggets in that big data ore. Several options include IBM's InfoSphere BigInsights, Google's BigQuery, and Microsoft's Windows Azure.
Marketing departments seem to understand the urgency of making big data actionable. This was recently reflected in a June 2013 Economist Intelligence Unit research survey, where marketers listed the "ability to use data analysis to extract predictive findings from big data" as their highest priority.
Now that the investment gauntlet has been thrown down, marketing and IT must find ways to reduce time-to-insight to keep fidgety C-level executives happy and to meet their own expectations.