Comments on “Competing on Analytics” by Thomas Davenport & Jeanne Harris

Much of American business is built on intuition and experience.  The authors make the case that the power is shifting to enterprises that quantify their decision process.  There are several notable examples of success.  There are few documented failures, although I would expect that they exist also.  The task of identifying a problem that Big Data and business analytics can solve is not trivial.  The execution of that plan can also be significant.  The price tag of business analytics needs to be viewed in the light of any other business expense, answering the question “what is the return on investment?”.  Usually, the outcome is unknown, so the benefit may be difficult to quantify until after the fact.  By then, of course, the money is spent.


The results can also be profound.  The authors take a look at several companies who have been able to create or extend competitive advantage based on a more quantitative view of decision making.  Several notable companies are counted among the quantitative winners such as Google, Netflix, Amazon, and others.   Basing decisions on executive whim is becoming an endangered species according to the book.  Indeed, as a customer base gets large enough to be beyond the comprehension of one person, you are better served to use statistics to understand the behavior of your customers.  Big Data/analytics can also be used to sharpen up manufacturing processes, operations, vendor management, pricing and human resources. 


That last one, human resources, takes a little explaining.  If you saw the movie Moneyball with Brad Pitt, you saw how statistics can trump intuition.  Billy Beane’s move to get a statistical look at players, and screen out their personal, emotional, qualitative aspects provided one of the best runs in baseball.  But they didn’t win the World Series.  But for a given budget they may have maximized their return on investment. 


The book makes a case that a company’s employees need to be as closely scrutinized as their customers.  An interesting theory, but with what data?  One of the problems with most professions is that their evaluations are largely qualitative.  Sales people have quotas so you can measure quota performance, but most positions defy easy quantification.  The risk is that trivial activities that can be measured become the focus instead of contributions to the business.  If analytics for human performance at work can be developed, I would expect to see a new class of executives moving to the forefront.  Current executives have strong social skills and business skills.  In many cases their social skills eclipse their business skills.  If the proper metrics can be developed and tracked and promotions follow, a different kind of executive may emerge with better business skills and perhaps lesser social skills.  This may be good or bad for the workplace.  But this isn’t about enjoying work.  It’s about maximum returns.


The authors create a spectrum for the role of analytics in a business from non-players (Analytically Impaired- stage 1) to masters (Analytical Competitors stage 5).  They portray the change that must be led by senior executives to make the transition, because it will impact managers, employees and their support structure with big budget and process disruption.  The highest use of analytics can create big results and a sustainable advantage enterprise wide.  Analytics then becomes the primary driver of performance and business value. 


Indeed there is data to suggest that extensive use of analytics can create significant market advantages and create a competitive advantage that is significant.  The authors do acknowledge that not every industry can be transformed with analytics.  They look to the airline industry that uses analytics extensively in pricing and operations.  The largest players in the industry keep flirting with bankruptcy.  Of course, it might be worse if they were not so analytically inclined. 


They break the domains for analytics into internal such as financial, manufacturing, R&D, and human resources as well as external for customers and suppliers.  Early examples might be cost management and getting a handle on costs which can be particularly tricky for services.  They also note that analytics is generally an iterative process, and some experimentation may be required.  This increases the price tag of analytics, but it is the predominant way that an organization gains experience and expertise to then leverage projects in different areas of the business.  The ability to progress from a stage one to stage five company will be different for each company, but the majority of companies that can overcome the management issues to persevere, can become dominant in their industries if they continue to evolve. 


The skill set for executives and analyst is significant and somewhat daunting.  The authors get a little hissy about have a doctorate as the only proof of competence.  But certainly the skills laid out in the book are not pervasive in the industry, and there is a wide concern about the scarce availability of skilled people to execute an analytics plan.  Executives need to also have some comprehension about analytics so they can correctly direct the effort and resources to achieve high return efforts. 


Finally, I don’t think there can be any doubt that Big Data/analytics is changing the world we live in.  This trend will only accelerate.  There will be out-sized rewards for companies that move quickly, and the laggards risk a changing market that they can not effectively compete.  Big Data/analytics will not change everything.  It will change most things.  The companies that brave the uncertainty and resource constraints will be the ones that have the best chance of survival in the economies of the twenty-first century.