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Back to the Future

Business Dimensional Modeling

As I reflect on future of business intelligence, I cannot help but think of what we have accomplished so far. Over the past two decades, there have been incredible changes in the business climate and amazing advancements in technology. As an industry, we have accumulated extensive experience, much of which is available through a variety of publications. Notable advancements include:

  • Improved overall reliability of the entire environment.
  • Ability to effectively build, administer and query very large and complex analytical databases where it is not uncommon to have multiterabyte dimensional data marts.
  • Development of a thriving tools business for ETL and data management technologies.
  • Emergence of meta data standards that are beginning to be adopted.
  • Harnessing of the Internet for cost-effective delivery to many more people.
  • Increase in the number of well defined architectures and methodologies to discuss, debate and provide guidance.

Are we more successful? Are there fewer failed projects? Are all projects now wildly successful? Unfortunately, the answer is no. What are we doing wrong? What does the future hold?

During the 18 years that I have been working in this field, I have observed many people seeking a "silver bullet" - the technology that provides the answer. Regardless of how sophisticated technology becomes, it will not be able to automatically solve the two major challenges we face: providing clean, integrated, usable data; and having the business community use the environment.

Technology has enabled us to more quickly implement the proper business rules to clean and manipulate our data. However, the tools cannot define our business rules for us. Each organization must understand their data, determine what "clean" data means to them and decide how data from disparate systems should be integrated.

There are still problems as technological advancements enable us to tap directly into operational databases across the enterprise or to pull in a spreadsheet of data on the fly. These capabilities are initially appealing; however, unless the data from these different places is already formatted for integration, the technology does not solve the fundamental data problem.

We have the skills and experience to deal with data issues. It is much more difficult for us to ensure that the business community uses the analytical environment. Most of our training and education is focused on tools, technology, architecture and methodology, but the major challenges of a successful deployment are working with people.

Change is difficult. Change takes effort and is unsettling. Organizations or industries that are in crisis often have successful data warehouses because the pain of the status quo is greater than the pain to change.

Technology can be intimidating. New technology can be daunting, sometimes even for experienced systems professionals. For example, getting a new cell phone can be a frustrating adventure. If you switch companies (service provider or phone manufacturer), much of what you may have known in the past no longer applies. If you are comfortable with technology, you are probably willing to invest the time needed to master the new gadget. Not everyone feels that way. Often, people will simply learn a few basics and never take advantage of other capabilities. This is true for cell phones and business intelligence!

Desire to maintain image or status. People can be afraid to try new things out of fear of looking less intelligent or even foolish. Young employees may be more comfortable trying things. More experienced employees may be more cautious and less willing to explore or experiment - especially in a public setting. These folks may not try at all, rather than risk showing a lack of expertise.

Fortunately, several actions can be taken to overcome these challenges:

Create an environment to support change. People need to be encouraged and rewarded for making the effort to learn and change. This must come from the business community itself.

Provide effective education. It is not enough to post preconfigured reports on a Web page. You need to teach the users about the content of the data that has been loaded and how it is organized. Formal training is needed. Offer a series of classes and provide small-group and individual coaching.

Be proactive with support. Don't wait for users to call with questions or problems; go out to help them. Spend time helping incorporate use of the data mart into day-to-day functions.

Technological advancements help us work faster, better and cheaper. However, there is still a lot that we can do to leverage what is already built and get more out of the technologies we already have.


Laura L. Reeves, coauthor of The Data Warehouse Lifecycle Toolkit, has more than 18 years of experience in the data warehouse industry with an emphasis in gathering business requirements, developing enterprise data warehouse strategies and designing business dimensional models. As StarSoft Solutions cofounder, Reeves has implemented data warehouses for many business functions for private and public industry and may be reached at laura@starsoftinc.com.

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