my big Data Donut

Two days in a row I managed to catch very different talks about big data, but came away with one big duh and several new insights.  In short, my prior training and experience using analytics to drive strategic decision-making placed me comfortably up the curve.  In return for my limited investment of time and attention, I gained a few new ideas, collected some cogent descriptors to share with clients and reawakened  elements in my strategic thinking process.

Big DATA , just a conjunction 

We all know Big because we know small. Everything classifies as one, when we decide it’s not the other. Big is also a euphemism for many.  Statistically, the bigger the sample, the greater it’s  significance. Bigness insures enough cases to draw general conclusions about a population.  Most of the time we don’t care about the population but we do care that a sample represents the population we care about.  An “Everyman” should be average and appear at the top of the bell curve, or normal distribution, right? Will being average, change the odds of being big or small? hold that thought.

We recognize data when we see it too. In excel, Big spreadsheets contain many rows and or many columns of stuff that we call data.

Changes in technology bring more data, we record and keep records of events that previously were not possible to record. More data gets created when instruments simplify its recording over ever smaller intervals.  For example, satellite data records and transmits continuously atmospheric particle movements,  Nike’s Fuel metrics measured by its band can provide streaming location data of people’s changing heart rate.

Put the Big together with Data along with the ease of access and you find yourself understanding Big Data coincident with the cultural shift  Big Data’s wider access produces.

If you build it they will come

In Big Data’s case, technology shifts made lots of data more accessible which increased people’s application in their decision-making.  At this hour, I can hear the helicopters hovering over the major highway junctions nearby to monitor traffic and issue the reports broadcast over radio and TV.  Everyone wants to avoid sitting in traffic, and their consumption of this information and decisions of when and which route they drive naturally impacts the pattern.  The widespread availability of GPS and map services rely on alternative information sources to generate traffic congestion maps , and influence consumer travel decisions as well.  Don’t you rely on one or more of these information sources? Why? few of us know the details behind the projection.  Instead,  we feel better with more information available, after all,  traffic information helps us avoid the inevitable–the likelihood of being stuck and delayed in rush hour.

Bottom line, consumption makes Big Data valuable. Its availability  raises questions, but we often skip the critical ones.  We ponder its use, before questioning its reliability as in what do I do with it? How can and should it impact my decisions?  

Why?

Humans’ daily actions rely on the process of cause and effect.  I turn on the faucet to make water come out.  I say “please,” you say “thank you.”  How many miles must I run to burn off the Fat calories I consumed eating a donut for breakfast?   Hmm, can I measure my fat burn rate? If I work for the donut producer, I may focus on the sales effects that result from posting this information.

These sets of  reactionary questions miss the opportunity set that Subway anticipated and took to the bank.  I don’t know the story behind Subway’s marketing strategy , haven’t looked into the chain’s profitability, but they clearly seized advantage of a trend fueling both  awareness and their revenue. They twisted the cause effect to create a successful Cause marketing campaign.

Worry about Bad not Big Data

In the second talk, Casey Winters, the head of digital marketing for a growing web-based start-up called Grub Hub spoke about the poor decisions being made using vanity metrics.  Traffic isn’t a new metric for retailers or commuters.  In business, Cost per Acquisition, Lifetime Value and Conversion rates represent a few key performance metrics that when properly calculated, effectively drive strategic investment decisions.

The challenge today isn’t their availability as much as their reliability.  More sources  of information reflect the ease with which some data can be measured.  For example, Google Analytics offers the basic traffic stats freely to any website who embeds their code.  Advertising agencies spent a decade redefining themselves to be digitally capable, and help their clients use these new tools to distribute their marketing dollars to physical and virtual locations.  The result, more data and Data Scientists emerging as guides through the complexity associated with Big Data.

STOP making Data into donuts

More data spread around doesn’t make anyone smarter, especially when not all available measurements of existing data prove trustworthy. Standards help a lot, but they may not  sufficiently help separate the noise from the signal. Don’t just use the data that’s available but be sure you understand its creation.  Take the case of the glazed donut comparisons shown above between Krispy Kreme’s Famous calculated calories to Dunkin’s Glazed donut figures.  The fact that they appear together in one chart doesn’t mean their calculations used the same computation process.  The information on its face lead to one conclusion, which may or may not support your own experience of these donuts.  Haven’t you already  put that experience to use and attributed  the observed differences’ cause to something other than the method of calculation?   In short, you used cause and effect favoring intuition over critical thinking.

When it comes to talking about strategy,  we often forget to ask the questions before we pull the data.  ROI may justify one investment choice over another and then again it may merely be used to confirm the value of your investment decisions after the fact.  Data should move you from insight to reality.  Remember a dot in one dimension is a line in another, the value of the era of big data increases our opportunity to capture more dimensions.  The challenge is using data to gain more perspective and beware of our biases.

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