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.)

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.

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.

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.