Possibility, are you asking how or what?

Polarity, the idea of opposites, turns thoughts and possibilities around like  a pendulum  always moving from one extreme to the other. That is until the thought runs out of energy or momentum and stops. It rests until some force displaces it.

Recently, a client asked for help explaining the difference between planning for a transition and planning a transformation. Since Transformation seems to be one of the buzz words of the moment, I began to wonder what made the two thinking processes different, and what did they really mean. My polarity thinking friend suggested that transitions plan for certainty, or near certainty and transformation plan for uncertainty.  I disagreed.

I think of a transition as the pause between takes, what happens between two clearly defined states.  It’s when we assess, evaluate or figure out our position, how close or far. Transformation, that’s the feeling we have on arrival, we made it so now what.

In other words, if the client has a clear objective as in to take a specific distant hill, then transition plans incorporate the certainty that elevations will be changing on route and insures the team’s prepared for the journey. When it knows  what changes to expect along the way, then it’s transition ready. Transformation focuses on arrival, different conditions and challenges it doesn’t know, but can imagine arrival makes possible.  After all, isn’t that why the objective was to take the hill?  Wasn’t it about the advantage that being on top offers?

Put another way, imagine what you want to do is known, like traveling to another location.  Transitions focus on the journey, how long will it take, buying tickets or planning the route. Transformation planning asks how the change in location affects your current activities.  Transitions are more whole body time shifts, where as transformation puts your head in the future while the rest of your physical body remains grounded in the present.

November’s topic for the monthly strategy discussion focused on Transformation Readiness. Before I managed to summarize the conversation and post notes,news about the sale of Mariano’s to Kroger caught my eye. and then I also spotted  an interview with CEO, Bob Mariano on the Chicago Booth website.

If you are not familiar with Chicago, then let me explain that Mariano’s was a new entry into the grocery store business. By coincident, just as they had opened a few stores one of Chicago’s main competitors –Safeway decided to close all of its Dominick’s stores. This meant Mariano’s acquired 10 of the closed stores and their debt fueled expansion took off.  That’s when Kroger came calling.

Since I had already been thinking about  transformation questions , as in how do you get transformation ready, I thought it worth sharing these responses.  Take a peek, and let me know what you think, are the example transitions or evidence of transformation readiness?

Scenario A: I think that Mariano’s namesake, CEO and founder, Bob nailed it when he said:

“At Mariano’s, we tried to push further. We continue to push.  What I mean by push is to expose the customer to different and unique things and allow them the opportunity to tell you, ‘No, I don’t like that,’ or ‘Yes, I like that.’”

Scenario B: Or maybe you prefer the spin by CEO of Shazam when asked about the increasing gap between growth in the amount of information and its utilization. ” …How do you improve data intelligence?”

“That’s definitely the case [that there is a data knowledge gap] and for years we have been talking about data warehousing, or capturing that data, but turning information into data intelligence is a new journey for many companies…”

Or, how about the Gambling industry insiders view who characterizes difference between digitizing or converting your industry to the reality post conversion this way:

Advancements in technology has brought about a rapid digitization of gambling and almost every other industry. Some have managed to exploit these developments more than others and I think that the gambling industry is at the forefront of how well technology can be applied to a domain.

As an industry we must be open to change and pro-actively look at how we can exploit such technologies to provide a better and more entertaining experience to our customers. For example, the progress in Touch ID has enabled us to allow LeoVegas iPhone app customers to log in to the casino using only their fingerprint.

Are you wondering why distinguishing between transitions and transformations matter? Or, even better, how your business can take greater advantage of the widespread availability, access and flexibility that a fully digitized world creates?

Great, now you are thinking strategically.


Sports analogies for business – how effectively do their impact and influence extend beyond the game ?

George Toma Readies Superbowl 48 Giants Stadium Field.
Superbowl XLVIII, Giants Stadium NJ readied by George Toma, official groundskeeper for all 47 preceding superbowls and his crew.

On the day of  Super Bowl XLVIII, it’s impossible to ignore the power of sports to captivate the attention of the world. Few events compare historically to viewership levels and  calls for  the all-consuming air time devoted by the media to a singular recurring event. Think about it, other than religious or national holidays, only acts of pure terror or wonder attract this much attention.

Irrespective of the team match ups, the pinnacle represented by the Super Bowl offers several compelling lessons to further Business Strategy, though we will overlook the obvious promotional marketing option.

Successful business strategy defines itself as a quest for clear advantage.  Sports stories typify this quest and that’s why we wondered about the value of the analogies, given their abundance and frequent invocation.  Do they help business leverage competency, inspire confidence and deliver positive outcomes?  As usual, participants reviewed in advance a few selected articles (linked at the bottom of this post). These thought starters warmed up participants reflective abilities and helped us frame the conversation as follows:

  • A battle of objectives-Winning vs. Value Creation
  • Numbering Success
  • Beyond the action of “the game”
  • Game Changers

The topic was not intended to prove conclusive, and we offer the following post-discussion elaboration of hasty notes for those of you who missed out.  Do feel free to add your thoughts and share the responses with others.

Winning Objectives

Undeniably there is a cross-relevance between many sports philosophies and business. Competition and Sports go hand in hand. Opposition, rivalry and contest make up the elements necessary to motivate both players and observers. The experiences of playing or watching sports evoke great passion and focus, behaviors which naturally pervade our lives and make the contests universally appealing. No wonder, sport stories succeed as shorthand references connecting experiences across a series of interpersonal activities including business.

How and do sports’ stories help or hurt business strategy? The context of a game doesn’t exactly mirror the ongoing demands of business.  A specific sport’s rules, frequency of play and consistency of  playing conditions (e.g. the field, the season, the equipment, ethics etc) make sport’s winners easy to name and likewise test the merits of a particular set of plays or strategy.  Business by contrast strives to eliminate ambiguity and standardize its rules and apply them consistently.  The result is the frequent restatement of results which makes the parallels to sport that much more divergent.

The context of game doesn’t exactly mirror the ongoing demands of business.  For example, is the business of business to win, or create value?  Does strategy offer greater value when it focuses on generating general or more specific outcomes?

The delimiters present in sports—the field, the game, the season –all make possible two fundamental self-sustaining attributes:

  • Emotional fervor, and sheer adrenaline fuels high performance in individuals, teams and fans but is impossible to sustain naturally.
  • Measurable relationships easily established between actions and rewards—both tangible and intangible .

Business strategy in contrast struggles with these issues and looks to sports for inspiration while persistently under playing the value and resists imposing similar clear and consistent delimiters.

For example, the value of a win, though always significant accumulates in sports but often dissipates in business.  To get the points, make the down or win the game rarely engages and rewards  employees directly as  it does  players.  To deliver success, employees need more sustainable incentives.

How a business measures or tracks a win or a loss differs dramatically from sports.  It also differs within industries or within an organization based on the business unit.  Are forecasters held accountable for their targets in manners equal to sales professionals?  What about wins in customer service versus IT?

Clear outcomes generate stories that rouse emotions to a degree that no numerical analysis of performance musters. Engagement in a given task or play and the survival lesson it produces can motivate both employees and players to focus, as well as extol the importance of each effort.  The analogy of surviving a skirmish makes clear connections between the single event’s outcome and its impact on the larger mission.

War analogies and larger battles such as the Super Bowl share common language but differ drastically in participant experience. Isolating a single event and elevating its importance can be an effective motivator, but in business performance goals need to be sustained and ever-increasing.

Sports analogies dominate conversation today may be due to the increasing prominence of the business of sports and the remoteness of common war experiences in many American’s lives.  To be successful understood, metaphors need to be familiar to your audience which increases its authenticity and your credibility.  These conditions make it easy to turn to sports for help.  In a single game, the event stakes don’t carry the same consequences as being a general or platoon leader in battle.

The idea of surviving the skirmish and larger battles may be proper in sports but many business problems are often ongoing.  To call for action, any insight the analogy inspires must ring true.

Beyond the action of “the game”

Before any game, a coach and the players generally agree on a strategy.

Coaches help their players gain the knowledge they need to be effective and win. Regular practice, drilled instruction, and constant review raise the level of experience and consistency of individual performance.  These factors help coaches recognize the readiness of a player and affect whether to play them in a given game and situation. Since players once they possess the ball often have wide discretion for their actions, the coach’s decisions to play them matters.  In business, there’s no overt practice, and few managers and leaders prove effective as coaches, though they remain accountable. In business proven performers get more freedom than in many team sports.  For example, players may blame their poor collective performance on decisions made by others, as reported by Darin Gannt.

‘‘That was a changeup,’’ the Chicago Sun-Times reported Brian Urlacher said. ‘‘I don’t like coming out of the game. But he’s the head coach. So I do what he says.’’

Few people close to the details willingly write or disclose these facts, or their reflections in a timely manner for fear of repercussions.  There’s greater value to silence and avoids becoming the subject of wider scrutiny.  Sure, there are some wonderful tell-all stories even if they tend to be extreme– great failures and great successes.  It’s no wonder that everyone confuses tactics and strategy. the failure of planned plays in both business and sport always depend on both the player capabilities and circumstances.  The difference isn’t just whose calling the shots, or is it?

Admittedly different sports offer different learning opportunities for business, but in general business could benefit from more post-mortem reflections on the game and analysis of play.

Organizations that do demand routine project reflection, write and publish them at least internally demonstrably outperform their peers.  Still, the number of organizations who have embraced this practice exemplifies the larger ambiguities that are rampant in business.  Of course, sports isn’t immune to the fall out that comes to their industry from these stories but not to the extreme and speed with which the market punishes business.

Numbering Success

Numerous performance indicators exist in both business and sports.  In sports, wins and losses are common denominators of success for both players and teams.  Regularly, new scorecards and metrics attempt to track and match up players within an organization or rank the organization in an industry.  The quest for more data and more metrics has led to the rise of numerous successful businesses.

Numbers try to provide great performance inspirations but fail to rouse people at the level of a great story.  Using alternative statistics to recruit and balance a team ‘s capabilities and diversity of skills plays out in both arenas.  Michael Lewis and his compelling story Moneyball heralding the quants’ efforts in baseball to change the dynamic of the game, proved to be an ineffective strategy.  Sports management quickly learned that an over-reliance on more statistical data missed the things that trained scouts or observers of real behavior historically captured.  Situational competence, summarized in statistical measures still requires a great deal of domain knowledge to properly interpret and produce winning combinations.  Either may be sufficient to generate a strategy, but both are necessary to create effective strategies.

These lessons, business sadly discovers slowly and fails to fully embrace even in the wake of the colossal market disruptions caused by internet and mortgage back investing bubbles. Moneyball profiled the underdogs and arguably changed the consciousness of average America about the power of data analysis, a welcome message to Chicago quants.

Another key lesson was the use of underutilized information to plea the case for alternative strategies.

The NY Times story that second-guessed the NFL coaches we read illustrates the common decision-making tension data analysis presents to both sports and business leaders.  The wide availability of data made it possible for the NY Times to create an accurate predictive model and demonstrate punting’s value punting in the fourth quarter, an impulse Coaches rarely heed.  A response by Ditka in the comments that follow reminds modelers that the model fails to consider what people in the game may know about the particular event that may be missing from the analysis–may being the operative phrase and tension point.

Clearly, quants have their place but good decisions always include some domain knowledge.  The access to information makes it easy for competitors to rely on the same information and in fact, that’s part of why the law of averages continue to pan out.  Statistics are wonderful reference points but one still needs domain knowledge to recognize what made the outlier an outlier.

Important to understand boundaries or the difference between situational competence and domain Knowledge.  Sports metaphors can help more people think differently when used effectively.

Game changers

The business of sports creates value regularly, but what about sport itself?

George Steinbrenner

George Steinbrenner purchased the Yankees at the low point of their competitiveness in 1973 for $8.7 million. According to Forbes, the teams’ worth was $1.7 billion in 2010, at the time of his death.  Steinbrenner was the first to grasp the handle of the free-agent market and he made it work for him. In effect he made the rules they all had to live by and he dominated the game.  He made offers to players they couldn’t refuse. Mike DeGiovanna writing for the LA Times explained that lavish spending on free agents became investments that helped fuel five world Series Championships since 1996.  His willingness to pay the premium for exceeding the league’s salary caps allowed them to keep a winning streak.  Under his tenure, the Yankees started a television network and built a new stadium.

“The bottom line is he put great players on the field, and he delivered championships,” Cashman said. “He built something the fans can be proud of, and that’s what a great owner does.” (see )

Discussion Takeaways

In business turnarounds, surely the numbers matter but they are often not the deciding factor.

Steinbrenner’s leadership style and strategies show how it’s possible for a single individual to usher in sweeping changes in an industry.

When it comes to Big Data, it’s important to differentiate situational vs. statistical know how.  The premium value attached to Baseball statistics could easily explain why Alex Rodriquez got kicked out,  because his drug enhanced performance was screwing up the stats.

Business can learn a lot from Sports.  The ready availability of numerous statistics offers a baseline and yet one still needs to overlay good judgment.  Efforts to track results may bring you closer understanding the true nature of a problem’s structure.  Numbers  balance your  intuition and know how. They  may clarify your findings, but in the end performance is a blend of art and science.

Steinbrenner’s decision to reward players so heavily for performance may have changed the game but it also made it more lucrative for players to juice.  Be careful what lesson you take.

Sports analogies can be intimidating, to those who don’t fully understand the sport or make the connections storytellers perceive as relevant.

Increasingly Sports has taken to applying business analogies to improve its own game, its own business performance.  None the less some of the greatest quotes come from sports players, e.g  Yogi Berra. “I like the moment when I break a man’s ego.

Business is a long-term effort toward an ever elusive goal, while sports  and sports management operate in a much more limited, shorter term horizon. The lessons don’t offer much assistance or inspiration for sustaining success over  longer horizons.

Sports has its Black Monday, the day in January when poor performing coaches often get cut.  No singular day of reckoning in business though the swift reaction after annual earnings may produce a similar response.


Journalism second guessing NFL coaches


be sure to check out the comments and remark by Ditka.

NASCAR second guessing


Adopting analytics


Think Tank: sport and business are not a good fit 


Communicating your strategy


Focus on Game Changer—short video for investors in sports



A guest post by:   Willard Zangwill, Ph.D., Professor, University of Chicago, Booth School of Business

Rachel Kaberon, in preparation for the Strategy Management Practices Issues Group discussion of the Chicago Booth Alumni Club, asked me to put together a page or two of thoughts about uncertainty in decision making.  Since she had helped me with software I have developed to assist in complex decision making, this was my chance to return the favor.  Hence, here are some thoughts that strongly influenced my thinking about uncertainty and how I have tried to suggest how people might better predict the future and make better decisions.


First is that uncertainty is remarkably uncertain, and our efforts to predict it are likely worse than we often assume.   Overconfidence bias is indeed strong.   What demonstrated this to me was the outstanding work of Philip Tetlock[i]. He studied how accurate were the predictions of experts and pundits in the political or economic  areas. These people were similar to the prognosticators we see on television or other experts discussing what might happen to events in the future.  Tetlock examined such predictions for years and studied tens of thousands of them, which was a huge undertaking.

What Tetlock discovered was how bad the predictions were.   They were only slightly better than chance.  Not the result one might expect, but worse.  Too many events seem to unexpectedly occur in the future.

Interestingly, the prognosticators that were most confident and sure of themselves, were wrong more than the more cautious forecasters who hedged and added conditional statements.    The confident experts tended to gain more support and attention, as their confidence convinced others, but that did not make them more right.

How could predictions be so faulty?  By and large, we tend to think we predict better than we do because if we are wrong, we give ourselves excuses.  We suggest that no one could forecast what really happened, or that events no one could have foreseen occurred. That process absolves us of blame and provides exoneration.   The net result, however, is that the future is harder to predict than most of us are likely to believe.


Given this conundrum that we have to predict events, but are probably not that good at it, what can be done.  Here are a couple of experiments that I have found useful to try to build upon.

As Gary Klein[ii] has noted, Research conducted in 1989 by Deborah J. Mitchell, of the Wharton School; Jay Russo, of Cornell; and Nancy Pennington, of the University of Colorado, found that prospective hindsight—imagining that an event has already occurred—increases the ability to correctly identify reasons for future outcomes by 30%.

The concept  is illustrated by the following.  Consider some upcoming event, say a presidential election.  Then think of reasons why a particular candidate might win.

Now do the following.  Assume it is now after the election. And assume it has just been announced that candidate has won by a solid margin.   Now think of reasons that triumph occurred.   You will likely think of  more reasons.  In essence, assuming an outcome and carefully imagining it, helps you think of reasons why that outcome might occur.   Perceiving those additional reasons then helps as you proceed to analyze the situation.

A much different approach in a study by Armstrong and Green[iii], was also quite helpful for forecasting the future.  In brief, they had subjects predict the outcome of past situations that were unknown to the subjects.    Since these were past situations, the actual outcome was known, although the subjects did not know those outcomes.  After the subjects made their predictions about the outcome of these situations, the accuracy of the predictions were then determined.

At this juncture, the experimenters then changed the situation.  They required that the subjects first consider several situations analogous to the one they had to predict; these were analogous situations where the subjects knew the outcomes.   Once they considered those several analogous situations, now the subjects were told to predict the situation in question.  The success rate went up substantially.  In fact, when a group of subjects were involved and they carefully compared analogous situations, the accuracy of the prediction roughly doubled.

The message seems to be this.  When we forecast an event, we tend to do that by thinking of some similar event that we know.  That similar event we know, gives us ideas about the outcome of the event we are trying to predict.  Now take this one step further.  If you consider several events roughly similar to the one you are trying to predict, it is like increasing the sample size. The accuracy of your prediction should rise.    Moreover, just examining how several situations similar to the one you are considering turned out, is illuminating, and by exposing the complexities of the situation,  provides useful insights.


Given the difficulty of predicting the future and the challenges thereof, it might help to broaden our decision-making framework and, in particular, to do more breakthrough thinking as that might provide us with an advantage.  Considering breakthrough thinking, as least for most people, good breakthrough ideas seem to occur almost randomly, as we tend to think about an issue and the exciting idea somehow jumps into our minds.    But there do seem to be procedures that help them occur more frequently and more when needed.   The key insight is to look and examine where the breakthrough idea is more likely to occur.

To illustrate, suppose you cannot find your car keys and have searched all over the house.  In frustration, you ask your spouse.   He/she replies that they are on your dresser.  Despite the mess on your dresser ( not necessarily yours, but certainly mine) you dash over to your dresser and with only a little rummaging, quickly find your keys.

As another example, when they search for oil, they do not put the exploratory well anywhere. But they first conduct detailed geological and seismologic examinations to locate where the oil find is more probable.

The concept for breakthrough ideas is the same.  Suppose you have one million possible ideas to search through in order to discover your breakthrough idea.  Finding that breakthrough idea from among the million possibilities,  is not likely to be easy.  This explains why getting breakthrough ideas is usually a challenge, as it required a quite large search to uncover it.

On the other hand, now suppose you obtain some clues as to where that exciting idea might be found that narrows your search down to ten possibilities.   You can easily search the ten and, in all likelihood, uncover the breakthrough idea.

The insight is to examine where the breakthrough idea is likely.   It is like drilling where the oil is likely, and you will more easily find it.   One of the concepts behind the software for decision making I developed takes advantage of this and seeks to suggest where the breakthroughs will be more likely, helping you to more easily discover it.


The uncertainty of the future is probably far greater than most of us assume. Here I have tried to suggest some means that might help reduce that uncertainty and improve decision-making.  There are other ways as well, and they should help as you proceed to make difficult decisions for the future.

[i] Philip Tetlock, “Expert Political Judgment: How Good Is It? How Can We Know?” (Princeton)

[ii] Klein, Gary, “Performing a Project Premortem,”  Harvard Business Review, Sept.2007

[iii] Kesten C. Green and J. Scott Armstrong   “Structured analogies in forecasting”, University of Pennsylvania, 9-10-2004

BIG DATA: Big Deal or Just Big Business?

Technology evolves and for those of us who spend their lives adapting and endeavoring to keep up with the advancements it’s hard  not to notice a curious underlying dynamic.   Data and our ability to calculate or manipulate it for greater meaning is a little like resolving the chicken and egg paradox.  More of one begs more of the other, and yet we continue to ask which came first as if that question were important.  For many of us, our interest in  closing the uncertainty gap wishes for more data. We expect it will  help minimize the error or noise because the present picture of relationships remains a little too ambiguous. The constraint in this case is often our own experience and knowledge.

Professionally,  my own work warns against this unconscious bias.  I simply ask people to imagine three dots and ask that they line them up.  I then remind them that all three dots are coincident data points in time, and ask whether this new piece of information has changed their vision of the dots?  I then ask them to place the dots on an axis of time, and tell them that the dots now represent demand, growth or performance like ROI.  Does the way you’ve visualized the dots changed again?  I explain that the context I’ve added snapped into their own experience to create an image that creates a new puzzle as what they see fights with their expectation and they need more data to explain it.

The Economist in revisiting the Growth Matrix in 2009, put it another way. Bruce Henderson, credited with originating this framework reportedly believed  “while most people understand first-order effects, few deal well with second-and third-order effects. Unfortunately, virtually everything interesting in business lies in fourth-order effects and beyond”.

Big Data and the volume variety and velocity of its availability now has several partners,  real time processing power and plummeting data storage costs and lest we forget, simple access and manipulation tools  placing the data in an ever increasing number of users’ hands.  It is the number of people who now want to use the power of analytics that lends Big Data its influence, or at least that’s what several Chicago Booth alums who shared their thoughts last week recognized.

On May 18, 2012 Chicago was busy preparing for the arrival of NATO delegates and support.  The result was many businesses strongly encouraged their employees to work from home, leaving the monthly strategy discussion homeless.  We took advantage of the opportunity to launch our first virtual discussion combining a real-time interaction platform  (Group Systems Thinktank) and conference call (freeconferencecall.com).  Interest in the topic proved overwhelming prompting us to open up a second lunch interaction following  our usual early morning time.  The comments that follow represent a condensed version of the conversation.  Note, links to the discussion prep video and articles we encouraged participants to review in advance can be found at the bottom of this post. Also, a full transcripts are available to those who interested in seeing the automated output from ThinkTank, just drop me a note.

What’s the deal

Its a toss up whether mobility or big data has captured the imagination of business media more. The duel isn’t the point. Other driving forces and a growing need for critical thinking skills that were already in short supply.  Data reduction may be an emerging competency.  As the earlier references to Henderson point out, the questions you are trying to answer don’t get any easier just because you suddenly have access to more data. What to do with this new wealth of rich information are the bigger questions and challenges not merely for business but for consumers as well.

In the process of  generating the following list of examples, the interaction on Thinktank let participants also provide some links, raise new questions and add additional comments.

Twitter ,Telephone call records, Smart Grid, Real time Electricity meter  data , Nike + ,Scanner data,  Comments from Call Centers,  Providers’ case and disease management notes, EMR records, Geospatial (GoogleEarth, Navistar, etc), Mobile and GPS,  Gov’t DBs (big-data in an unstructured/non-uniform sense) , The quantified self, Output or processed Data from SAS, Salesforce.com, other enterprise databases, Loyalty program, Amazon purchase history, Mint.com, Moneyball (Big data in baseball), the new NSA data warehouse in Utah, QR, The internet of things, Data.gov, The London Datastore, created by the Greater London Authority (GLA–Chicago has similar initiative) offers citizens open access , Netflix movie recommendations, SAP’s HANA usage (profiled in the report on their Sapphire Conference )

Twitter for example has evolved in ways that surprised their founders and also launched a number of new businesses with very unusual purposes. As one article pointed out routing the data can be equally important as tallying it, as illustrated by Procter and Gamble’s practice of funneling social media conversation/data to the appropriate person’s screen for monitoring and response.  In other words to be meaningful, sometimes just knowing something happened is enough, it doesn’t necessarily have to be mathematically manipulated to derive value.

Persistent challenges remain in dealing with the enormous variety of formats in which data are presented — some  sources are difficult to analyze — their pages long data dictionaries  often include details about its collection.Add to that the realization that Data is not just numbers anymore. The automatic semantic annotation required to make sense of this has also entered a new era.

Facebook, LinkedIn, Google+, Pinterest and similar social media sites would fit in here, as well.  All offer a richness of information, much of it real-time, that can be monitored, mined and used to drive decisions and actions. The customer center conversations , or customer audio recordings , transcribed to text, and then subjected to text analytics is helping improve performance management practice, allows for campaign conversion performance tracking etc

The impact or convergence of the evolving technologies with all of this new data, is as overwhelming as the scanner data that was available and stored historically but few had resource capability or interest in mining it. That era has passed and with it, new questions and promises arise.https://i2.wp.com/www.connectedaction.net/wp-content/uploads/2012/04/20120421-NodeXL-Twitter-Global-Warming-Labeled-Groups-Network.png

Can we better understand and use consumers sentiment as in  do retail customers use more electricity/phone service/etc in a method that correlates to changes in the economy? weather? Or SAPs HANA data, now makes cancer DNA genome type analysis possible in minutes.

FB and Twitter,  may be drawing more attention, but those who  bring together the different streams  and mining more deeply old sources such as scanner data  are also causing quite a stir. Target found out that teenage girl was pregnant before her dad did using methods such as these.

Add the RFID  and imagine the benefit to stores knowing the quantity of each SKU that they currently have?  Can they coordinate sharing of product between their brick-and-mortar stores?

Perhaps the focus on twitter, etc., rather than scanner data comes from hoping that trending sentiment will precede and can be used to predict purchase, rather than log it as with scanner data. At cars.com, a participant shared  that vehicle search data on our website is predictive of sales.

It looks like QR codes might achieve the same benefits of RFID as smart phones are becoming ubiquitous.

The Geo-spatial data, according to McKinsey’s recent report on Big Data as the next big innovation, quantified billions in time savings from just helping consumers avert traffic.

Several other behavioral nudging based on real time feedback is now possible. Nike is exploring and furthering this automatic feedback. Check this article for more brands using the quantified self, and more information


SAP’s HANA, Google’s BigQuery , Splunk, Hadoop and NoSQL databases , Tableau, Tibco Spotfire, Omniture ,Pentaho (open source BI), Amazon’s Web Service suite (more of a platform), Cloud computing platforms, Data visualization, most business users, make the data preparation easier and allows them to focus more on analysis and develop insights in combination with Machine learning tools (neural networks, support vector machines, natural language processing, etc.)

Decision making , can and does BIG data make more accuracy possible?

It offers higher granularity — like Target’s ability to create coupon books customized to individual households, Or integrate GPS level stuff — e.g. texting coupons to customers right while they’re standing next to a certain store.  Or 2nd/3rd order analysis – correlating Target’s sales with weather data; creating ‘real-time’ personalized coupons; identifying ‘trend-setters’ among the customer base to influence ‘trend-followers’ coupons

The downside? Detecting or separating out spammers from these data sets or paid to  express a certain sentiment. Totally! Like those girls paid to say great things about clothes on Facebook — not maybe necessarily analyzing big data, but using the platform.

Greater real-time evidence  can reduce risk and  insure assumptions in product/service development and marketing are on or off target.

Hard to appreciate the analytic without the qualitative context or understanding;  but maybe some strange new ideas can come out of the data, like the “diapers next to beer” epiphany. Data needs some drivers to make it meaningful, as in cause and effect. In part because qualitative data is harder to analyze.

Is quant vs. qual or the social science methods to data collection really that different from the scientific data analysis approach?  Both approaches seek to explain cause and effect, or the relationship between a stimulus to produce a predicted response. The problem is  too many people will extend a model beyond it’s capacity. Claiming “the data said so”  lets people off the hook and avoids responsibility for  decision-making.

Suggested tips include  avoid extending a model beyond its capacity, or  understand and differentiate descriptive and predictive Stats. Likewise, be wary of  finding trends that don’t exist (e.g. “data mining” or “straws that look like needles”) and confusing correlation with causality.

Perhaps cross-validation from trained analysts can help avoid  these . Danger of expecting tools to automatically extract value from large datasets.  Need to ensure good analysis, disciplined hypothesis generation, etc.

The data, even when analyzed, does not represent the decision.  This is true with small and big data.



  • Big data is here to stay – need to figure out how to use it effectively.
  • I liked the point that lots of data is around and that people just don’t know what to do with it.  The best BIG DATA process or engine in the world still won’t create the insights that are needed.
  • Corporate culture is a huge factor–the problem is not availability of data, but commitment and focus of corporate leaders to shape a culture that moves the organization in that direction.
  • Big data is here. It’s a tool and like any other, it’s the latest and greatest on the block, with a bit too much hype. But it has a definite value in providing and stronger qualitative base to identifying trends and activities.
  • My realization  is that, once again, the technology is interesting, but it is the corporate culture and will that will matter. The culture and vision lead; the strategy and models follow.
  • sharing the questions with a wider audience confirms concerns and clearly lots of assumptions that need to be played out. There is a large dark side that we still don’t understand; but the positives and opportunities for real time decision.
  • Big Data! The piles get higher and  higher and wider and wider…to what purpose? That implies the need to “mine” the data, reduce it and subject it to analysis before it can be made useful.
  • Big data will revolutionize business but it is not strategy, potential for a lot of false positives .
  • The wise use of big data offers a huge opportunity for developing differentiating strategies and for finding new product/service needs.
  • “Big data” is the current term for things that have existed for a long time.  All types and sizes of organizations can benefit from big data if they recognize the importance of the human component (not just the data and software) and have specific objectives in mind before starting.
  • Much of the expertise about analytics developed over many decades still applies, and there are new dimensions to integrate and understand because of the availability of the technologies and data.
  • Everyone on the line has experience with Big data, so I don’t think it’s so scary.  Most people have business perspectives, wanting to teach the Analysts that their conclusions need to be driven by business needs.   My comment is that as leaders, and those trained with some behavioral awareness through business school, it is _OUR_ responsibility to try and massage the analysts towards an enthusiasm for our world view…;-)
  • The human side of utilizing the technology and expertise is just as challenging as ever. (Cognitive biases, communication skills, influencing skills) Garbage in – garbage out is a big risk without proper attention and skill in applying the technology and in communicating.  The data, even when analyzed, does not represent the decision.  This is true with small and big data.

In closing, let me return to my observations about the limitations of developing strategy rooted in an expectations of the experience curve relationship.  The frame with which you approach the problem often has far more bearing than the data, your analysis or the tools.  Or at least, in June we  plan to look at some of the assumptions around growth as the ultimate strategy.

Please throw your responses, or continue to post links for others as Big Data continues to be quite newsworthy as its impact and influence continues to unfold.

Articles and links:

We suggest an  Optional  short 5 min. video tutorial , EMC produced to understand what Big Data IS?    http://youtu.be/eEpxN0htRK
The following are required advance reading.

1. IBM’s Institute for Business Value, in collaboration with MIT Sloan Management Review  2010 research findings
Analytics: The new path to value: How the smartest organizations are embedding analytics to transform insights into action


2. Strata keynote  short 7 min. video by Google’s Digital Marketing Evangelist Avinash Kaushik- a bit irreverent and a little over the top – a bit irreverent, bordering on over the top – but not boring  effort to help us understand the problems and the approach undertaken by Google.

Big Data Imperative 
March 2012

3. Tom Davenport’s Culture of Analytics

April 5, 2011

4. Inside P&G’s digital revolution
McKinsey Quarterly November 2011
overseeing the large-scale application of digital technology and advanced analytics across every aspect of P&G’s operations and activities—from the way the consumer goods giant creates molecules in its R&D labs to how it maintains relationships with retailers, manufactures products, builds brands, and interacts with customers. The prize: better innovation, higher productivity, lower costs, and the promise of faster growth…

One more optional overview

The age of big data
NYTimes, Feb 2, 2012