Big Data. Everyone’s paying for it, collecting it, and talking about it, but what are companies actually doing
with it? There’s a lot of data out there, but not too much actionable insight – yet. And that’s the point, isn’t
it? Shouldn’t we be using this seemingly unlimited stream of consumer and user data to drive smarter business
Many companies have turned into pathological data hoarders
without much to show for it.
Even in 2015, Forbes
foresaw the average investment for data-related initiatives to cost an average of $7.4 million. That amount
is likely higher now – and nearly 60% of these
projects fail according to Gartner. That is a LOT of wasted dollars. And now nay-sayers are
chirping about “the death of big data.”
How did this happen? Theory vs. execution. Instead of idyllic “data lakes” – curated, centrally connected and
ripe for pinpoint, targeted analysis, we’ve ended up with “data swamps,” filled to the edges, and too muddy to
offer any visibility into the insights that we’re all fishing for. But big data isn’t dead – most organizations
just haven’t figured out how to manage it.
If you don’t know what you want to get out of the data,
how can you know what data you need –
and what insight you’re looking for?
I even wrote
on LinkedIn that the “hoard it all and sort it later” approach is a bit backwards. So, I recommend the “Data
Tree” approach (not to be confused with Decision Tree, of course). It starts with a thoughtful look at the
questions/issues that are keeping you up at night – what are the business problems you’re trying to solve? Next,
- Prioritize your business issues
- Prioritize difficulty of assembling the first data breadcrumbs
- Identify highest value issue that can be solved with the least amount of effort
This approach gives you a path to a “win,” and a better understanding of how to collect and analyze your data.
Once you’ve selected a specific target issue, investigate all factors that could have an impact on it. For
example, if your big issue is shipping/delivery delays, those factors can be anything from tired drivers and
speed limits to weather and traffic patterns. Data for each of those should be relatively easy to assemble, and
Using the same example, you then focus on the factor with the highest correlation (let’s say traffic), and branch
out from there – what contributes to that factor? In this case, weather, time of day, location,
etc. And again, you assemble another data breadcrumb, or discard it. And just like you would be growing and
grooming a tree, you continue to break down each factor and sub-factor.
This will ultimately provide a holistic view of every data point that contributes to your main issue – and more
importantly, which ones you can control or impact. Best of all, it eliminates the muddiness of data noise –
extraneous information that doesn’t have an impact on your key business issues.
Once your data tree grows, you’ll end up with
big, smart, healthy data.
This is the approach we take at EastBanc Technologies, putting the problem before the tactic. This enables our
customers to save time and money, and see results sooner. With an investment of ~$50K, one of our clients with a
huge database, increased their frequency of customer wins by 25% within three months. In fact, the average size
of their deals went up 94%!
Putting your issues first is the best way to mine your data for actionable insight – and to get the best return
on your investment in Big Data. Ready to branch out from your current data approach? Take a look at my LinkedIn
Viable Prediction – Is Big Data Dead? or contact
us for a consultation on how to get your data tree growing.