Leveraging Subject Area Models

By Bruce Johnson

A Subject Area Model (SAM) is a key component of a successfully developed data warehousing or data architecture program.  Oftentimes, when a subject area model is created, they are frequently leveraged for only the purpose of segmenting a data model.  Whether you develop it yourself, or purchase a vendor data warehouse solution, having a subject area model can assist your effort in many additional ways.  If utilized effectively, it can also help in development, deployment, and efficient usage.

Let’s take a deeper look at the concept of a subject area model and how to get the most out of it.

What Is A Well Defined SAM?

A subject area model is most efficiently defined as a breakdown of business definitions that encompass the high level data areas of the solution you are trying to build.  While most often used to define the data areas of a data warehouse solution, in a new organization or one developing a formal data architecture program, it should be used as the basis for mapping all data across the organization.  The key in any successful subject area model is to make sure that the terminology and definitions associated with it are business focused and able to be understood at a high level at a glance.  Varying requirements exist for how many subjects are effective or desirable, but generally no fewer than 6 and no more than 20 is a starting guideline.  The greater the depth and complexity of the data in your business, the larger the number of subjects you may need to define.

A general concept for the creation of a valuable model is that the subjects should not change.  As your business evolves, it may increase in nature, but should not change significantly – if it did, one could argue that the initial model was not done well.

The varying methods and approaches for defining a sound subject area model are too numerous and lengthy to expound on in entirety here.


How Can You Use SAM?

A well defined SAM should not be something that is created and put on the shelf.  It is something that should be integrated into the data architecture that fits the original reason for creating it in the first place.  There is both business involvement in the ownership of the content of the model, and data architecture support of the maintenance and publishing of the model.  Defining business oversight and governance of the model insures that the business is not only actively involved, but helps to drive and realize the value that can be achieved.  Much of the IT support following the initial creation involves mapping and modeling of the detailed data model components that make up the comprehensive model.  Creating the SAM it enables the various models that support it to be developed in organized iterations that map to the business needs and drivers being addressed.


Get The Most Out Of SAM

Once you have the SAM created, there are several ways you can leverage it to achieve the most value.  Here are a few categories of usage that you might find useful:

  • Business Project Planning:  As business needs are prioritized and planned, a SAM can provide a basis for linking projects and systems to be developed and deployed.  Business leadership can help ensure that system planning and data activities are linked to provide a common terminology that fits the nature of the business.
  • Establishing Data Governance:  Defining how the business oversees the capture, quality, and usage of data is a key benefit of a SAM.  Often dividing up the stewardship of data is best done by each subject.  That could mean having formal stewards each responsible for a subject or having some stewards responsible for multiple subjects.
  • Data Capture Or Integration Planning:  In building a practice for data centric definition of systems design or ETL/data integration frameworks, a SAM can help logically separate the various components.  In doing this, it can provide a separation that allows resources to focus on the quality and integrity of specific areas of data and link those resources to the appropriate data stewards.
  • Communications:  An effective communications plan often reduces the hurdles and roadblocks that slow projects and delivery.  Sharing the overall treatment of data as an asset to your organization can provide several advantages.  It may help alleviate fears over the protection of the data.  It also can help resources see how the evolution of systems and data relates to their individual roles/responsibilities and how it will impact overall business success.  For those building data warehouses, it can be used to help describe why the data is necessary to enabling analytical efforts.
  • Requirements Definition:  When defining the data needs of an individual project or effort, it is beneficial to have a high level model that can be used to quickly outline the data components required.  In this case, a SAM is used to communicate and validate with the business how the data needs of any effort fit into the overall data architecture.  In data warehousing related efforts, it provides a basis for the sorting and ordering of source to target mapping efforts.
  • Data Model Development:  The most common usage of a SAM is to allow a data modeling team to focus and prioritize the creation of a formal data architecture design.  It can then be the basis for building your model a piece at a time, allowing multiple resources to work on parts of the model without having to boil the ocean by building an enterprise data model all at once.  Effective managing and progress is then reported at a level that both business and IT can align with.



A Subject Area Model is a tool that once created can and should be used for a variety of purposes.  Ideally, it becomes the cornerstone of a well defined data architecture program.  Most importantly, it should be shared, governed, and used to build an integrated data architecture program.  Aligning both the business and IT in development and oversight can help bridge the gap between efforts and planning.

About the Author

Bruce has over 20 years of IT experience focused on data / application architecture, and IT management, mostly relating to Data Warehousing. His work spans the industries of healthcare, finance, travel, transportation, retailing, and other areas working formally as an IT architect, manager/director, and consultant. Bruce has successfully engaged business leadership in understanding the value of enterprise data management and establishing the backing and funding to build enterprise data architecture programs for large companies. He has taught classes to business and IT resources ranging from data modeling and ETL architecture to specific BI/ETL tools and subjects like “getting business value from BI tools”. He enjoys speaking at conferences and seminars on data delivery and data architectures. Bruce D. Johnson is the Managing director of Data Architecture, Strategy, and Governance for Recombinant Data (a healthcare solutions provider) and can be reached at bjohnson@recomdata.com

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