Wednesday, July 17, 2013

Business Analytics and Dimensional Data

Readers of this blog frequently ask about the relationship of business analytics to the dimensional data that is recorded in data marts and the data warehouse.

Business analytics operate on data that often does not come from the data warehouse. The value of business analytics, however, is measured by its impact on business metrics that are tracked in the data warehouse. 

Business analytics may also help adjust our notion of which metrics matter the most.

The Data Warehouse and Dimensional Data

The dimensional model is the focal point of business information in the data warehouse. It describes how we track business activities and measure business performance. It may also be the foundation for a performance management program that links metrics to business goals.

Dimensional data is the definitive record of what matters to the business about activities and status. Clearly defined performance indicators (facts) are recorded consistently and cross referenced with standardized and conformed reference data (dimensions).

In this post, when I talk about "the data warehouse," I will have this dimensional data in mind.

Business Analytics

Business analytics seek to provide new insight into business activities. Analytics do not always operate on business metrics, and they don't rely exclusively on information form the data warehouse. Dimensional information may be an input, but other sources of data are also drawn upon.

The outputs of business analytics, however, aim directly at the metrics tracked by our dimensional models. Insights from analytics are used by people to move key metrics in the desired directions. These results are called impacts.

Business analytics may also help in another way. Sometimes, they help us determine which metrics are actually the most important.

A great illustration of these dynamics can be found in the business of Major League Baseball. (If you don't follow baseball, don't worry. You don't have to understand baseball to follow this example.)

Metrics in Baseball

Major league baseball has long been in the business of measurement. Followers of the game are familiar with the "box score" that summarizes each game, "standings" that illustrate the relative performance of teams, and numerous statistics that describe the performance of each player.

These metrics have precise definitions and have been recorded consistently for almost 150 years.1 Like the metrics in your data warehouse, they are tracked systematically. Professional baseball teams can also set goals for these metrics and compare them to results, much like a scorecard in your performance management program.

How does one improve these results? If you run a baseball team, part of the answer lies in how you choose players. In the book Moneyball2 Michael Lewis describes how the Oakland Athletics used a set of techniques known as sabermetrics3 to make smarter choices about which players to add to their roster.

These analytics allowed the A's to make smarter choices with measurable impact--improving performance and reducing costs. Analytics also motivated the A's to change the emphasis given to various metrics.

Business Analytics and the Oakland Athletics

The traditional approach to selecting players was focused on long-held conventional wisdom about what makes a valuable player. For example, offensive value was generally held to derive from the ability to contact the baseball, and with a player's speed. These skills are at least partially evident in some of the standard baseball metrics -- things like the batting average, stolen bases, runs batted in and sacrifices.

The Oakland A's looked to data to refine their notion of what a valuable player looks like. How do the things players do actually contribute to a win or loss? To do this, the A's went beyond the box scores and statistics -- beyond the data warehouse, so to speak.

By studying every action that is a part of the game -- what players are on base, what kind of pitches are thrown, where the ball lands when it is hit, etc -- the A's realized they could be smarter about assessing how a player adds value. These business analytics led to several useful conclusions:
  • Batting averages don't tell the whole story about a player's ability to get on base; for example, they exclude walks.
  • Stolen bases don't always contribute to scoring; much depends on who comes to bat next.
  • Runs batted in tell as much about who hits before a player as they do about the player himself
  • Sacrifices, where an out is recorded but a runner advances, were found to contribute less to the outcome of a game than conventional wisdom held.
You may or may not understand these conclusions, but here is the important thing: the analytics suggested that the A's could better assess a player's impact on winning games by turning away from conventional wisdom. Contact and speed are not the best predictors for winning game.  "Patience at the plate" leads to better outcomes.

Impact for the A's

By using these insights to make choices, the A's were able to select less expensive players who could make a more significant contribution to team results. These choices resulted in measurable improvement in many of the standard metrics of baseball--the win/loss ratio in particular. These insights also enabled them to deliver improved financial results.

Analytics also helped the A's in another way: they refined exactly which metrics they should be tracking. For example, in assessing of offensive value, on base percentage should be emphasized over batting average. They also created some of their own metrics to track their performance over time.

The Impact of Analytics

Business analytics tell us what to look for, what works, or what might happen. Examples are signs of impending churn, what makes a web site "sticky", patterns that might indicate fraud, and so forth.

These insights, in turn, are applied in making business decisions. These choices provide valuable impact that can by tracking traditional business metrics. Examples include increased retention rates, reduced costs associated with fraud, and so forth.

These impacts are the desired outcome of the analytic program. If the analytics don't have a demonstrable impact on metrics, they are not providing value.

Business analytics can also help us revise our notion of what to track in our data warehouses, or which metrics to pay closest attention to. Number of calls to the support center, for example, may be less of an indicator of customer satisfaction than the average time to resolve an issue.


As you expand the scope of your BI program to include analytics, remember that your desired outcome is a positive impact on results. Move the needle on business metrics, and the analytics have done their job.

Thanks to my colleague Mark Peco, for suggesting that I use Moneyball as a way to explain analytics without revealing the proprietary insights attained by my customers. 


[1] The box score and many of these statistics were established in the mid 1800's by a sports writer named Henry Chadwick.

[2] Moneyball by Michael Lewis (Norton, 2011).

[3] The Oakland A's are a high-profile example of the use of sabermetrics, but did not originate the concept. See wikipedia for more information.

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