The Race to Keep Up with the Analytics Evolution

If you thought analytics was a competitive advantage for your company, think again.  Doing analytics the right way is a standard competency for most businesses, today.  While some companies are still banging rocks together, hoping for a spark of enlightenment, others are using science to leverage predicted outcomes.  Let’s take a closer look at this evolution in analytics.

Reporting analytics has three evolutionary stages:
Stage 1 – Tabular Reporting
Stage 2 – Data Visualization
Stage 3 – Actionable Insights
Alternatively, we might restate these stages as What Happened?, What’s Happening? and What Will Happen?

The universe of analytics is about to have its own big bang – will CIOs make the right decisions to keep up, or leave their people feeling like cave men meeting the first Martians?

Stage 1 is the stuff of stone tablets.  When Moses came down from Mount Sinai, he was holding God’s P&L, which roughly amounted to a net profit as long as everyone would agree to stop killing each other.  There was a pretty slow uptake because of all that clunky reading.  I get the same sinking feeling today, when I walk into a client and find them pouring over 100 column report in Access, with that dull look on their face, like a caveman staring into a stream waiting for a fish tAnalyticsEv_Graphic1o show up.
Then God hired a new VP of sales, Jesus, who saw how inefficient this old system was.  Shrewdly, he simplified the rules down to just two, in effect, inventing the first KPIs.  Though, no one was quite sure what it meant if you were in the green with God, but searing into a burnt orange with your fellow man.  For a time, KPIs were all the fad – some clever professor at Harvard said “scorecard” and suddenly every business was grading themselves and their customers with a dizzying matrix of stoplights.  After a great class action lawsuit, led by the group, Citizens for Color Blind Awareness (who always wore mismatching ties), we all agreed a change would be for the better.
Stage 2 launches with the heralding of science, understanding how the human mind works in visual perception.  Edward Tufte, a grumpy old Galileo, pushed forth the notion that if a picture is worth 1000 words, then a chart is certainly worth 100 stone tablet reports.  Screens go aflame with pie charts, the Neanderthals huddle back under the safety of their animal skins and wonder what all the fuss is with this fire thing, anyway.
The tooling around visualization becomes so dead simple to use, that virtually anyone with a mouse could create an incoherent dashboard.  The manufacturers of the software call this Self-Service BI, but what they really mean is Un-Serviced BI – a proliferation of 3D charts and other fantastic visual anomalies, resulting in a pile of chart junk.  This is when we start to feel the limits of our 2 dimensional world.
Then we brinAnalyticsEv_Graphic2g in the “experts” who show us how we can see in other dimensions.  With miniaturization, we hang 12 perspectives of the same clean chart on a trellis and create a digestible comparison across 12 dimensions – a true multi-dimensional world.  Charting soon becomes storytelling as a cohesive message is wri
tten to the page.  We can easily monitor any business process even though the hills of data have grown into mountains.  All this can make you feel like a Data Viz Ninja, until you realize that you actually are a Ninja and your competitor is staring down at their smart phone about to mount a missile strike.
Like so many patterns in the universe, these stages expand and contract.  Once you’ve squeezed out every last insight from your charts, it comes time to blow it all up with the next big bang.  Stage 3 analytics is made of science that no one ever heard of before, even though John in app dev has been studying it for the last decade and brings it up at every performance review.  It’s the recommendation engine at Netflix, the speech recognition on a smart phone, spell check, auto correct, threat detection in antivirus software.  It has become so ubiquitous, that you just expect it.  You get a little bit annoyed when it’s not there, when that website doesn’t have a search bar or that IVR system taunts you with 8 menu options that may have changed since you last hung up in despair.
Some CIOs have been scratching their heads about this machine learning thing, wondering if they really need the hype, or if it will at least amount to a nice yearend bonus.  And if all that Random Forest, Naïve Bayes stuff doesn’t pan out, what then?  Will there still be enough budget left to implement a quick little data lake?  Finally, they call in a sales guy who begins telling them that they will have the ability to forecast sales and proceeds to tell a story about Target predicting a teenage pregnancy, as a most pertinent example.  Luckily, everyone in the meeting has an Apple watch to pass the time, without looking like their passing the time.
The trouble then, like all the other stages, is imaging the impossible.  What would you do if you could profile your customers and know what they needed before they did?  What if you could predict customer churn or employee turnover?  Or knew the best customers to offer a loan to?  Or could detect theft or tampering with your systems?  These questions lead us into the arena of actionable insights – they might be recommendation engines, optimization suggestions through clustering or classification algorithms.  The focus shifts into this final goal of how to improve outcomes.
Businesses will fall into many different places across the spectrum in analytics.  The question now is where do you fall and are you ready for the next big bang?

About Anexinet

Anexinet is a leading professional consulting and services company, providing a broad range of services and solutions around digital disruption, analytics (and big data), and hybrid and private cloud strategies. Anexinet brings insight into how technology will impact how business decisions will be made and how our clients interact with their customers in the future.
Mark Wojciehowicz, BI Solutions,

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