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.

Conclusion

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. 

Notes

[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.

Related Posts

See also these posts:

• In the Era of Big Data, The Dimensional Model is Essential (June 5, 2013)
• The Role of the Dimensional Model in Your BI Program (April 30, 2013)

In the Era of Big Data, The Dimensional Model is Essential

Don't let the hype around big data lead you to believe your BI program is obsolete. 

I receive a lot of questions about "big data."  Here is one:

We have been doing data warehousing using Kimball method and dimensional modeling for several years and are very successful (thanks for your 3 books, btw). However, these days we hear a lot about Big Data Analytics, and people say that Big Data is the future trend of BI, and that it will replace data warehousing, etc.

Personally I don't believe that Big Data is going to replace Data Warehousing but I guess that it may still bring certain value to BI.  I'm wondering if you could share some thoughts.

"Big data" is the never-ending quest to expand the ways in which our BI programs deliver business value.

As we expand the scope of what we deliver to the business, we must be able to tie our discoveries back to business metrics and measure the impact of our decisions. The dimensional model is the glue that allows us to achieve this.

Unless you plan to stop measuring your business, the dimensional model will remain essential to your BI program. The data warehouse remains relevant as a means to instantiate the information that supports this model. Reports of its death have been greatly exaggerated.

Big Data

"Big Data" is usually defined as a set of data management challenges known as "the three V's" -- volume, velocity and variety. These challenges are not new. Doug Laney first wrote about the three V's in 2001 -- twelve years ago.¹And even before that, we were dealing with these problems.

Photo from NASA in public domain.

Consider the first edition of The Data Warehouse Toolkit, published by Ralph Kimball in 1996.² For many readers, his "grocery store" example provided their first exposure to the star schema. This schema captured aggregated data! The 21 GB fact table was a daily summary of sales, not a detailed record of point-of-sale transactions. Such a data set was presumably too large at the time.

That's volume, the first V, circa 1996.

In the same era, we were also dealing with velocity and variety. Many organizations were moving from monthly, weekly or daily batch loads to real-time or near-real time loads. Some were also working to establish linkages between dimensional data and information stored in document repositories.

New business questions

As technology evolves, we are able to address an ever expanding set of business questions.

Today, it is not unreasonable to expect the grocery store's data warehouse to have a record for every product that moves across the checkout scanner, measured in terabytes rather than gigabytes. With this level of detail, market basket analysis is possible, along with longitudinal study of customer behavior.

But of course, the grocery store is now looking beyond sales to new analytic possibilities. These include tracking the movement of product through the supply and distribution process, capturing interaction behavior of on-line shoppers, and studying consumer sentiment.

We still measure our businesses

What does this mean for the dimensional model? As I've posted before, a dimensional model represents how we measure the business. That's not something we're going to stop doing. Traditional business questions remain relevant, and the information that supports them is the core of our BI solution.

At the same time, we need to be able to link this information to other types of data. For a variety of reasons (V-V-V), some of this information may not be stored in a relational format, and some may not be a part of the data warehouse.

Making sense of all this data requires placing it in the context of our business objectives and activities.

To do this, we must continue to understand and capture business metrics, record transaction identifiers, integrate around conformed dimensions, and maintain associated business keys. These are long established best practices of dimensional modeling.

By applying these dimensional techniques, we can (1) link insights from our analytics to business objectives and (2) measure the impact of resultant business decisions. If we don't do this, our big data analytics become a modern-day equivalent of the stove-pipe data mart.

The data warehouse

The function of the data warehouse is to instantiate the data that supports measurement of the business. The dimensional model can be used toward this aim (think: star schema, cube.)

The dimensional model also has other functions. It is used to express information requirements, to guide program scope, and to communicate with the business. Technology may eventually get us to a point where we can jettison the data warehouse on an enterprise scale,³ but these other functions will remain essential. In fact, their importance becomes elevated.

In any architecture that moves away from physically integrated data, we need a framework that allows us to bring that data together with semantic consistency. This is one of the key functions of the dimensional model.

The dimensional model is the glue that is used to assemble business information from distributed data.

Organizations that leverage a bus architecture already understand this. They routinely bring together information from separate physical data marts, a process supported by the dimensional principle of conformance. Wholesale elimination of the data warehouse takes things one step further.

Notes

1. Doug Laney's first published treatment of "The Three V's" can be found on his blog.
   
2. Now out of print, this discussion appeared in Chapter 2, "The Grocery Store."  Insight into the big data challenges of 1996 can be found in Chapter 17, "The Future."
3. I think we are a long time away from being able to do this on an enterprise scale. When we do get there, it will be as much due to master data management as it is due to big data or virtualization technologies. I'll discuss virtualization in some future posts.

More reading

Previous posts have dealt with this topic.

• The Role of the Dimensional Model in Your BI Program (4/30/2013) details the four ways we use the dimensional model. Only one of these functions involves a database.

• In Big Data and Dimensional Modeling (4/20/2012) you can see me discuss the impact of new technologies on the data warehouse and the importance of the dimensional model.

The Role of the Dimensional Model in Your BI Program

The dimensional model delivers value long before a database is designed or built, and even when no data is ever stored dimensionally. While it is best known as a basis for database design, its other roles may have more important impacts on your BI program.

The dimensional model plays four key roles in Business Intelligence:

1. The dimensional model is the ideal way define requirements, because it describes how the business is measured
2. The dimensional model is ideal for managing scope because it communicates to business people (functionality) and technical people (complexity) 
3. The dimensional model is ideal as a basis for data mart design because it provides ease of use and high performance
4. The dimensional model is ideal as a semantic layer because it communicates in business terms

Information Requirements

The dimensional model is best understood as an information model, rather than data model. It describes business activities the same way people do: as a system of measurement. This makes it the ideal form to express information needs, regardless of how information will be stored.

Image by Gravityx9
licensed under Creative Commons 2.0

A dimensional model defines business metrics or performance indicators in detail, and captures the attendant dimensional context. (For a refresher, see the post What is a Dimensional Model from 4/27/2010.) Metrics are grouped based on shared granularity, cross referenced to shared reference data, and traced to data sources.

This representation is valuable because business questions are constantly changing. If you simply state them, you produce a model with limited shelf life. If you model answers to the question of today, you've provided perishable goods.

A dimensional model establishes information requirements that endure, even as questions change. It provides a strong foundation for multiple facets of BI:

• Performance management, including dashboards and scorecards
• Analytic processing, including OLAP and ad hoc analysis
• Reporting, including both enterprise and operational reports
• Advanced analytics, including business analytics, data mining and predictive analytics

All these disciplines center on business metrics. It should be no surprise that when Howard Dresner coined the term Business Intelligence, his definition referenced "facts and fact based systems." It's all about measurement.

Program Roadmap and Project Scope

A dimensional model can be used to describe scope because it communicates to two important audiences.

• Business people: functionality   The dimensional model describes the measurement of a business process, reflecting how the process is evaluated by participants and observers. It communicates business capability.

• Technical personnel: level of effort A dimensional model has technical implications: it determines the data sources that must be integrated, how information must be integrated and cleansed, and how queries or reports can be built. In this respect, it communicates level of effort. 

These dual perspectives make the dimensional design an ideal centerpiece for managing the roadmap for your BI program. Fully documented and mapped to data sources, a dimensional model can be divided into projects and prioritized. It is a blueprint that can be understood by all interested parties. A simple conformance matrix communicates both intended functionality and technical level of effort for each project.

At the project level, a dimensional design can be used as the basis for progress reporting. It can also serve as a nonambiguous arbiter of change requests. Changes that add data sources or impact grain, for example, are considered out of scope. This is particularly useful for organizations that employ iterative methodologies, but its simplicity makes it easy to reconcile with any development methodology.

Database Design

The dimensional model is best known as the basis for database design. The term "star schema" is far more widely recognized than "dimensional model" (a fact that influenced the name of my most recent book).

In fact, the dimensional model is the de facto standard for data mart design, and many organizations use it to shape the entire data warehouse. It has an important place in the W.H. Inmon's Corporate Information Factory, Ralph Kimball Dimensional Bus architecture, and even in one-off data marts that lack an enterprise focus.

Implemented in a relational database, the dimensional model becomes known as a star schema or snowflake. Implemented in a multidimensional database, it is known as a cube. These implementations offer numerous benefits. They are:

• Easily understandable by business people
• Extraordinarily flexible from a reporting and analysis perspective
• Adaptable to change
• Capable of very high performance

Presentation and the Semantic Layer

A dimensional representation is the ideal way to present information to business people, regardless of how it is actually stored. It reflects how people think about the business, so it is used to organize the catalog of items they can call on for analysis.

Many business intelligence tools are architected around this concept, allowing a semantic layer to sit between the user and database tables. The elements with which people can frame questions are categorized as facts and dimensions. One need not know what physical data structures lay beneath.

Even the earliest incarnations of the semantic layer leveraged this notion. Many organizations used these tools to impose a dimensional view directly on top of operational data. Today, semantic layers are commonly linked to dimensional data marts.

A dimensional representation of business activity is the starting point for a variety of BI activities:

• Building enterprise reports
• Defining performance dashboards
• Performing ad hoc analysis
• Preparing data for an analytic model

The concept of dimensional presentation is receiving renewed attention as federated solutions promise the construction of virtual solutions rather than physical ones.

Further information

I briefly covered these four roles in an interview last year:

• Big Data and Dimensional Modeling (4/20/2012)

Many of these themes have been discussed previously:

• Three Data Warehouse Architectures that Use Star Schema (3/26/2007)
• Drive Warehouse Strategy with a Dimensional Model (7/6/2007)
• What is a Dimensional Model? (4/27/2010)
• The Conformance Matrix (6/5/2012)

Although I've touched on these topics before, I wanted to bring them together in a single article. In the coming months, I will refer back to these concepts as I address common questions about big data, agile BI and federation.

In the mean time, please help support this blog by picking up a copy of my latest book.