Big 3I competencies: Why are they so darn hard to acquire?

Creating value and organic growth opportunities requires uncovering opportunities often hiding in plain sight. Innovations challenge expectations including possible returns on the effort.  We take for granted what’s under our noses even though it may be exactly where we need to pay closer attention. Understanding how perception affects our preferences makes compensation possible. Vigilance helps,  especially awareness of value on multiple dimensions. There’s a monetary aspect and there are ideas we hold near and dear.  Both values motivate human behavior and that’s what makes life interesting.  Let’s begin our exploration  looking at traditional expressions of value  after an introduction to the concept of “fundamental attribution,” or first perceptions.

Prior knowledge separates surprise from distraction.  A sudden unanticipated event will jolt our senses. Our sudden vigilant state will recede when we recognize familiar people, or cues, associated with things we know make us happy. Surprise includes circumstances or context that make us expect what comes next and so we relax our guard. The fundamental attribution idea literally draws on internal experience. Stored knowledge takes care of us, finding a fit to situations and environments we meet. That doesn’t mean we pick the best fit. Often familiar,  frequently used ideas come to mind faster. Logical or rational alternatives follow, too late to be useful. That’s where intention, pausing before reacting, offers the pre-frontal cortex time to process. This internal tradeoff makes humans wonderfully complex and predictably irrational.

The trick is to understand how circumstances get people to do what you want and avoid them blowing up in your face.  Psst, the answer goes beyond data analytic competencies, though that’s important.

Perception and preference the Big What?

Data comes in one flavor, but tastes differently to consumers than it does to product and service providers.  Everyday, more code and identifiers amplify specific and ambient details associated with activities such as tracking goods, service use etc. The convenience, cost and time savings provided by standard identifiers like bar codes, account numbers, social security numbers, email addresses and phone numbers also simplify providers, up and down the supply chain, catering to our unconscious preferences. Every day, we compromise a little more of our privacy and anonymity in the process.

The sheer volume, veracity and velocity of all this raw, “Big” data makes navigating the future possible. The tricks require exploring past and present relationships between variables. Predictive Models use that deeper understanding of variable relationships  and their interactions to create opportunities, control risk producing conditions and optimize sources of marginal profit. The results enrich our lives and few of us feel oppressed by this Business Intelligence (BI).  Big Brother does exist, but so does Big Sister, Big Doctor, Best Friend, Old Roommate, Big Pen Pal etc. In other words, government  surveillance creating the old FBI style dossiers, pales to the knowledge stored about you by your bank, Google, Facebook, Amazon and other retailers. Healthcare regulations and practices preserved the privacy of your information, and their slowed migration to electronic medical records. Their failure to keep up with the wider digital data practices have also slowed  diagnostic advances and cost saving opportunities.

Real innovations begin with insight, once the province of small tests and strictly the domain of human intelligence.  Today Big Insight crowds out the spotlight occupied by BI. Cheap storage and faster processing makes data mining possible for anyone, but it is the strategic opportunists  with the foresight to be serious players and accumulators that continue to change the world.  Recently, GigaOM  identified several use cases  while highlighting Terradata, the makers of the first terabyte scaled database. The full list is worth reading, as I mention only a few.

  1. Steve Jobs infamous statement that Apple doesn’t do customer research no longer holds true.  Terradata named Apple as its first customer to exceed a petabyte of storage. Apple rapidly accumulates  transactional information on their customers to understand customers across product groups.
  2. WalMart’s data processing and analytic capabilities go beyond simple sales efficiency. The data helps instruct and educate its suppliers with insights about packaging dimensions as well as shelf space location etc.

Intelligence to Influence requires insight

The ongoing arrival of new technologies and embedded tracking codes continue to fuel the race to understand and use real-time ambient data to influence transactions. More data makes it easier to see deeper underlying patterns more clearly.  With greater awareness, trends can be spotted and tracked more readily and the impact of different interventions tested simply and more thoroughly.

Understanding the data requires more than iterative recombination, it takes expertise. With knowledge and experience patterns can be understood by both people and machines (see Earlier post: understanding-aint-believing-and-yes-there-are-economic-consequences).  But it takes  curiosity to explore different dimensions and generate insights.  Here are two different takes:

Luis Arnal of InSitum explains what holds back many of us. Please listen to his Design Research Conference in 2011 complete  presentation, absent the charming slides. This summary doesn’t do justice to his talk, but  I wanted to share some of his key reflections and lessons on the steps to developing insights

Begin with data, or information records that represent your observations from field research. After collection, the data needs to be categorized, clustered.  Begin the analysis process using a simple scatter plot to understand the landscape or context of observations relative to the categories selected.  Using  intuition and prior knowledge, the dimensions you choose to contrast also leads to the direction in which you develop associations between the data points.  What, if any, possible connections exist?  Using imagination and creativity  lines of connection appear as  part of an effort to FIT the dots to a model.  Of course the interpretations vary. Time and patience make possible “a fidelity of meaning” and the underlying pattern comes into focus. The data’s added value  suggest patterns that slowly develop into solutions. Insights, Luis explains contain  30% Data, 30% inspiration, 30% perspiration and 10% luck.   Insights facilitate the transition from confusion to help resolve the initial problem. They are the links between what Is and What If, they help us imagine how when we don’t or can’t know.

Recent article in HBR by Thomas Davenport,  another worthwhile read, emphasizes a different set of talents and experiences.  Particularly helpful for positioning your firm is one of the closing observations about the capabilities housed within your organization and the opportunities they present.

“….their greatest opportunity to add value is not in creating reports or presentations for senior executives but in innovating with customer-facing products and processes….

LinkedIn isn’t the only company to use data scientists to generate ideas for products, features, and value-adding services. At Intuit data scientists are asked to develop insights for small-business customers and consumers and report to a new senior vice president of big data, social design, and marketing. GE is already using data science to optimize the service contracts and maintenance intervals for industrial products. Google, of course, uses data scientists to refine its core search and ad-serving algorithms. Zynga uses data scientists to optimize the game experience for both long-term engagement and revenue. Netflix created the well-known Netflix Prize, given to the data science team that developed the best way to improve the company’s movie recommendation system. The test-preparation firm Kaplan uses its data scientists to uncover effective learning strategies.”

What’s the common denominator linking Davenport and Arnal?  Both reference visual thinking or the conceptual translation of ideas into tangible representations.  Again,a  mastery difficult to acquire and beyond the bounds of computers, even those as powerful as IBM Watson. I don’t think Siri creates flow charts, but she might learn.

I did and so can and do others. When hiring for analytics teams I managed, three criteria or competencies were essential: SAS skills—statistical coding; knowledge of the business; and an ability to think through new problems. i never thought to ask someone if they could draw.  One of my teams pioneered new strategies to improve profitability.  Initially, that meant differentiating credit worthiness.  Managing the portfolio however required alternative methods to promote profitability by optimizing costs and simultaneously minimize risks.  At the time, combination of competencies we needed were rare. Above all we needed flexible thinkers to tackle complex problems  and create more sustainable solutions. We learned to bet on those who offered two of the three. In time, we came to realize that the third criteria, thinking, was one we couldn’t teach.  It became the minimum requirement. In the late 80’s, we sought out academics with  conceptual modeling experience and bypassed MBAs.  Banking wasn’t the only employers seeking these skills but we were much more flexible in hiring them.

Today, the combination of technical skills proving most valuable continue to be found among individuals who have studied complex data and demonstrate visual thinking, again not MBAs. Not all designers capabilities include assembly of a sophisticated social network analysis model, but they sure do a great job of communicating conceptual ideas tangibly.

This post began talking about value.  Should the value consumers derive match the value producers derive? Absolutely not. In business the preoccupation with return on investment makes sense for private equity focused on upside and early exit. This contrasts with Warren Buffet, who grew wealthy ” thanks to his ability to learn the value of various securities and then buy them for less, a concept at the core of value investing. “Price,” he has said, “is what you pay. Value is what you get.”

Remember the fundamental attribution concept?  Buffet’s remarks on value and his actions show how easily we mistake motive and behavior.  Companies that obsess about cost risk missing key insights.  Case in point, the recent rise and fall of JCPenney’s CEO, a man clearly familiar with the power of BIs (insight and intelligence analytics) to achieve innovation. How people interpret observed behavior matter. The more detail and the more attention to context , increases chances to uncover key actionable insights.  James Surowiecki, a notable observer of the slippery slope of over reliance on analytics, recent New Yorker column , shared comments on the widely touted and now vilified  Ron Johnson, by Mark Cohen, a former C.E.O. of Sears Canada, and now a professor at Columbia”

“In most of the retail universe, price is the most powerful motivator,” Cohen said. “This game of cat and mouse with regular, ever-changing discounts is illogical, but it’s one that lots of consumers like to play. Johnson just ignored all that.”


Playing effectively with Big Data analytics requires an unusual mix of capabilities. More than sheer brute processing power, modeling, imagining and speculating requires artistic license.  Machines will find patterns of relationship quickly, but not clear they will find the direct relationship between cause and effect. The reasons and thought processes that drive the behavior, remain domains where humans excel.

Its’ hard to believe that the same analysis that led Johnson and his team to create the square fair pricing missed recognizing coupons significance to their customers. I agree with  Surowiecki, who  suggests the impact of one  fundamental attribution created a rippling effect producing one error after another. The first error made by the board in selecting Johnson, created further error by  decision-makers and Johnson himself  in choosing  to push their half-baked strategy forward prematurely.

What do you think?



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