Giving Information Meaning: The Rise of Business Analytics


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Business Analytics is the scientific process of transforming data into insight for making better decisions.  Data doesn’t always cooperate with this process, as it is often massive and messy.  But no matter what condition data is in, we use business analytics to make decisions with it.

In order to make these decisions, we have to understand the ultimate value that various combinations of this data can present.  So, we measure it.  That is, we measure what data carries: information.  Measurement is what informs uncertain decisions, and almost all decisions are made under uncertainty. 

Measurement is a very particular process, and prior to carrying it out in analytics, we need to address certain questions:

  • What decision will this measurement support?
  • What observable consequences are we measuring?
  • How will measuring these observable consequences matter for the decision we’re supporting?
  • How much do we currently know (what’s our current level of uncertainty)?
  • What is the value of additional information?

When we speak of measurement, we mean a quantitatively expressed reduction of uncertainty based on one or more observations.  So, if we have no idea how long a bridge is, we have complete uncertainty.  But if we then say that we are 90% confident that the bridge is between 10 and 10,000 feet long, we have reduced uncertainty about the length of the bridge.  That’s a measurement.  This is to say that, no matter how difficult we perceive a measurement problem to be, there is usually some level of uncertainty that can be reduced.  We can always look to other fields when stumped – it’s likely our measurement problem occurs elsewhere, and others have made progress in resolving it.  And even if we have no idea what to measure, it still makes sense to measure something.  The process itself will teach us what to measure.

In business, observational data tends to be what is measured.  And it is the first few observations that usually provide the highest payback in uncertainty reduction for a given amount of effort (Diminishing Returns set in quickly).

The most popular way for business analysts to measure something is through spreadsheets, mainly because they are easy to use.  Being easy to use means spreadsheets have been quickly and widely adopted.  It also means we tend to limit ourselves to solutions that are easy for a spreadsheet.  This is a form of Cognitive Bias called the Availability-Misweighing Tendency.  And familiarity with spreadsheets leads users to actually over-use them, or use them in places where they don’t belong.  This leads to errors or incorrect conclusions.  Even when used properly, spreadsheets are prone to human error; more than 20% of spreadsheets have errors, and as many as 5% of all calculated cells are incorrect.

Despite these drawbacks, spreadsheets play a critical role in the analytics process.  Most managers and analysts use them to segue from raw data to final report.  In fact, the ever-present nature of spreadsheets has helped to craft a new generation of thought.  One rooted deeply in measurement and analysis.

It’s not that business analytics is a new phenomenon – it’s been around for the past 20 years.  But it’s only recently making its breakthrough from a business perspective.  Why?  Well, this is how Malcolm Gladwell would likely explain it.

In Outliers, Gladwell did an excellent job of explaining how Bill Gates was perfectly positioned to pioneer the computer software industry.  One of largest contributing factors was his age.  This is the timeline Gladwell puts forth:

1955: Gates born

1964: First version of BASIC released

1965: First minicomputer (PDP-8) released

1967-1970: Ideal time for a future computer software maker to be in 7th grade

We can use the same “Gladwell math” to back in to the ideal age for an analytics professional.

1979: VisiCalc released

1982: Lotus 1-2-3 released

1985: Excel released

1989-1991: Ideal time for a future analytics professional to be in 7th grade

This would put the ideal analytics professional in his or her mid-thirties today, usually about the time that people reach some level of authoritative decision-making status.  It’s reasonable for these professionals to remain in decision-making positions for the next generation, meaning we are likely just now entering a prolonged period of applied analytics in business.