Information Economics: The Foundation of Business Analytics.

The technology industry often produces multiple, not-altogether-consistent definitions of the latest "hot" thing. Business analytics is no exception.  Confusion can be the result.

Hence, the intensity of my focus on definitions. Having previously defined "data science", I now drag readers through an exercise in defining business analytics.  At the risk of appearing obsessively compulsive, I repeatedly emphasize the business context.

The charter statement for this blog emphasizes the study of the application data science to business problems.  I seek to apply the scientific method to its practice.
"Data and statistical methods" have become inseparably associated with the "how" of business analytics.  I want want to dig deeper.  

Science is about answering "why." Since business is the domain of interest for business analytics, we should look to economics as a candidate foundational science. In the following, I:

• Make the case for foundation for business analytics in economics;
• Introduce a specialized domain of economics on which business analytics is based; and
• Provide a simplified illustration of its use.

Why, reader, should you care about these things? Big data and business analytics are subjects of many bold claims regarding their transformational abilities.  Some of these claims are valid, and some are not.  I seek here to help you separate the science from the alchemy.

What is business analytics?

Service oriented architecture (SOA) — a source of significant tech-industry buzz during the last decade — provides a case study in definitions.  Distinct definitions appeared to arise for each stakeholder class. The Open Group — a non-profit organization promoting open standards for technology view — offers two definitions of SOA.  Software vendors tend to emphasize the key technology components.  For SOA, those are an Enterprise Service Bus (ESB) and a services registry.

Merrifield, et al,¹ identified the business payoff for a SOA approach to strategic technology management. SOA promises a cost-effective approach to mass customization of information technology. They describe a SOA planning method. But they did not explicitly define the practice.

Enterprise architecture (EA) provides another example.  I like Gartner's definition for two reasons:

• The veracity of its source (i.e., Gartner said it); and
• Its focused on "enterprise" in the business sense, independent of the technology.

The technology community captured the EA term to connote the architecture of the IT infrastructure — for either an organizational enterprise or for an individual system. Technology consulting group Forrester Research invented a new term, Business Architecture, apparently in response. Ross and Weill² established the need for a business-centric definition.

Why this circuitous path?  I want to make the case for an economics foundation for business analytics. Getting definitions right is important to my case. Statistical (and deterministic) models, modeling tools, and enterprise information management technologies are "how" business analytics is done.  Science seeks to answer the question, "Why"?

The important point here is that we focus business analytics on answering questions that lead to measurable, net-positive business outcomes.  We seek a scientific underpinning from which to achieve this objective.

What, then, does this mean for the discipline of business analytics? I continue with the pattern of a business-centric perspective. I also want to define it as precisely as possible. Wikipedia, the reflexive "go to" source, offers a pretty good definition:

Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods.

This definition borrows from Bartlett³ (whom I have yet to read, but have added to my Kindle wish list).  Davenport⁴ defines the payoff — "improving performance in key business domains" — without explicitly providing a definition.

So, business analytics is about quantitative characterization of business performance. What then is business about?  Those who have sat through an MBA program might observe that the majority of the curriculum is derived from economics and its applications. An economic foundation for business analytics therefore seems reasonable.

Hence, an economics grounding for business analytics.  I preserve here the distinction between business analytics and econometrics.  The two disciplines use many of the same tools. Business analytics focuses however on a distinct organization.  It arguably constitutes a subset of econometrics.

The foundation of business analytics

Information economics is business analytics in its must fundamental form. Information economics is the science of assigning economic value to information.  It combines principles from the following disciplines:

• Game theory, by which economic transactions are defined and modeled;
• Information theory, with bases both in engineering and psychology disciplines; and
• Microeconomics.

Figure 1 illustrates. 

Figure 1 — Information Economics resides at the intersection
four more familiar disciplines.

Practitioners of Info Economics employ a clearly defined toolset.

• Information modeling,⁵ borrowed from information theory, precisely represents the distribution of elements of information among participants in an interaction;
• Game theory contributes pattens for archetypical transactions between participants in an information exchange; and
• Microeconomics provides bases for economic valuation of elements of information involved in a transaction between counterparties.

The presence of uncertainty in economic transactions introduces probability theory as well.

Information economics provides the foundation for many well known theories about the operation of financial markets.⁶  The interplay between bid-ask prices in a financial exchange, for example, telegraphs considerable information about counterparties' intentions and abilities without explicitly "showing their hands."  The Efficient Market Hypothesis finds partial justification in Info Economics.

Marketing economics is replete with examples.  Applications occur of course in other business disciplines.  For example, information economics can inform investment decision making.  Its principles can also guide what aspects of operational cost and efficiency are most worthy of measuring.

As an aside, the similarities between information economics and real options⁷ are striking. Real options theory assigns economic value to flexibility in making investment decisions.  I leave that discussion for a future installment.

A simple, contrived illustration of assigning economic value to information†

My illustration here is based on the "Elementary Game,"⁸ one of the simplest models from game theory. It resembles the Binary Symmetric Chanel (BSC). I first saw the BSC in communications courses while studying electrical engineering.  Info economics and communications theory (the engineering variety) share roots from information theory. That their toolsets resemble each other does not surprise me.

Figure 2 — a BSC illustration — gives us a passable representation of the "Elementary Game." (Note:  Texts in communications theory⁹ and info economics share the "Alice" and "Bob" notation.) The a priori events appear on the left-hand side.  There is some probability of either of two events occurring.  "Alice" initiates one event or the other.  

"Bob" receives a "signal" indicating — with a probability p that it is correct — which event "Alice" effected.  He therefore views the event from an a posteriori perspective.  Based on knowledge of the a priori probability of what "Alice" did, the probability that the signal is correct, and the cost/benefit of either of two resulting courses of actions, "Bob" must decide what his optimum next step is.

Figure 2 — The "Elementary Game" from game theory resembles the Binary Symmetric Chanel (BSC), a basic building block of communication theory.  (Source: Wikipedia, http://en.wikipedia.org/wiki/Binary_symmetric_channel)

Let's see the "Elementary Game" in action. Say that I'm a BMW dealer. I operate in a market that generates 10,000 sales annually.  I capture an average of 1,000 of those sales — or a 10% market share. Sales produce an average of $50,000 in revenue.  I have traditionally used mass media — broadcast and newspapers — for my advertising.

Let's now say that I can identify decision factors — information — that influence buyers' decisions about whether and from whom to make a purchase of a new car in my market segment.  These factors might include:

• Capacity to make the purchase (e.g., disposable income);
• Brand preferences; and
• Age of current vehicle;

among other factors. I can use this information as the basis for a targeted advertising campaign that — with probability of 25% — increases my market share to 15%. Assume that (in order to keep this example simple) the targeted ad campaign costs the same as the mass media campaign.

How much is this information worth?  Information economics defines the value of information as:  "...the increase in utility from receiving the information and from optimally reacting to it."⁸  So, without the information I can take a course of action leading to one outcome — a specific revenue level in our case.  Given the information, I can make a decision to pursue an alternative course of action.  This alternative leads to a different outcome. I characterize my two alternatives using the same measure.

The increase in utility in our example is change in revenue realized from a targeted ad campaign. From elementary probability theory,

ΔRevenue = Pr{ΔSales} × ΔSales × Average revenue/sale
             = 25% × 500 × $50,000 
≈ $6,250,000.

Information that can — with probability 25% — increase my market share by from 10% to 15% is worth about $6 million to me!  This trivially simple illustration demonstrates the power of Google's business model.

Information economics at work

I illustrated a scientific approach — based on Information Economics — to assigning value to two specific elements of information in a specific business context. These essential elements of information are:

• What change in market share might I be able to effect with a targeted ad campaign; and
• What is the probability that my targeted ad campaign will produce that result.

This gives me an economic value of those two information elements.  I apply business analytics — data and statistical methods, tools, data scientists — to obtain the answers to these specific questions.  If the cost of getting this information is less than its economic value, then applying business analytics here yields a net-positive economic benefit.

This illustrative example admittedly oversimplifies things. Business decision makers should base their decisions on a range of probabilities. Few business questions lead to discrete, binary answers.

So, what does all this mean?  First, information economics provides a scientific approach to business case analyses for business analytics initiatives.  The value of business analytics is measured by its economic returns.  We now have a rigorous approach to determining the "goodness" of business analytics initiatives.

Second, this leads to criteria for a strategy for adoption of business analytics by organizations.  Lavalle, et al, advise would-be data-driven organizations to, "Start with questions, not data!"¹⁰  Successful adopters of business analytics as a foundation for decision making keep a laser-like focus on:

• Business outcomes; and 
• The questions that lead to them. 

This also implies a gradual, evolutionary approach to adoption.  But more on that, later.

Next installment:  Is more data always better?

Note:  Missed my cadence last week.  A short-notice proposal turned a slack week into a frenetic one.  But back in the saddle again, this week.

¹ R. Merrifield, J. Calhoun, and D. Stevens, "The next revolution in productivity," Harvard Business Review, June 2008,  http://goo.gl/Y58xqm.

²J. W. Ross and P. Weill, Enterprise architecture as strategy, Boston:  HBR Press, 2006, http://goo.gl/B7J5P8.

³ R. Bartlett, A practitioner's guide to business analytics," McGraw-Hill, 2013, http://goo.gl/o6dTOS.

⁴ T. H. Davenport, J. G. Harris, and R. Morison, Analytics at work, Boston:  HBR Press, 2010, Location 112, Kindle Edition, http://goo.gl/olZkKm.
⁵ L. Samuelson, "Modeling of knowledge in economic analysis," Journal of Economic Literature, June 2004, pp. 367-402.
⁶ M. K. Brunnermeier, Asset pricing under asymmetric information, London:  Oxford, 2001, http://goo.gl/7IMFDv.
⁷ See, e.g., M. Amram and N. Kulatilaka, Real options, Boston: HBR Press, 1999, http://goo.gl/6Bswjk.

⁸ M. Bütler, Information Economics, New York:  Routledge, 2007, p. 42, Kindle Edition, http://goo.gl/1zZKQ1.
⁹ see, e.g., B. Schneier, Applied cryptography, 2nd ed, New York:  Wiley, 2001.

¹⁰ S. Lavalle, et al, "Big data, analytics, and the path from insights to value," MITSloan Management Review, Winter 2011, pp. 21 - 31, http://goo.gl/8RSn5H.

† This example is purely fictional.  Any resemblance to experiences by actual BMW dealerships is purely coincidental.

© The Quant's Prism, 2014