The Conceptual Data Model Assessment (CDMA): Ensuring That Your Conceptual Data Models Are “Disruptive”

By Pete Stiglich

The Conceptual Data Model (CDM) is a technology and application neutral data model that is developed to help ensure that IT understands the business – before data stores and applications are designed and developed.   The CDM is a diagram identifying real world concepts/objects/things (entities) and the relationships between these to gain, reflect, anddocument understanding of the business in order to:

  • Foster semantic reconciliation
  • Improve business/IT collaboration
  • Serve as a framework for the development of high quality information and systems
  • Improve project scoping (for data and application design)

 

Why would we want CDM’s to be disruptive?   Because we want to challenge the status quo by delivering higher quality information and applications on-time, under budget,  and by helping the business gain insight into itself in a new way (a frequent result of developing Conceptual Data Models).  Disruption is a new buzzword that means being “forced to think of another solution, a fresh idea, which in turn forces a positive reassessment of a company’s offering”[1]

The Conceptual Data Model (aka Semantic Model, Business Information Model, etc) should usually be the first data model developed in a phased modeling approach, but all too often is not performed due to factors such as lack of modeling experience and training, time pressures, emphasis on quantity versus quality, and mistaken notions about what a CDM is and its benefits.

Due to the outstanding benefits that can accrue when development of CDM’s becomes a part of your organizations’ data modeling standard (best practice is a phased data modeling approach: Conceptual -> Logical -> Physical), it is important to have guidelines for the development of your CDM’s and a way to ensure that your CDM’s will be understood and utilized effectively.  The Conceptual Data Model Assessment (CDMA) is a tool for just this.

The CDMA is comprised of a tool for measuring a CDM against objective criteria, along with an assessment methodology to ensure the CDMA is effectively performed and that the results are properly delivered to the right audience and used effectively to influence not just the quality of data and applications, but to help the business understand itself in a way it may not be used to.   The business is used to process models, org charts, and other business models, but often does not have exposure to a model that models the business from a “data” perspective – a “what” not “how” model.

The Conceptual Data Model identifies the key business objects and how these objects relate to each other.  The Conceptual Data Model can be disruptive[2] because:

  • Business objects “entities” need to be named and defined.  These names often have to be resolved through coordination between multiple business units, which leads to improved communication, understanding, standardization, and collaboration.
  • As a result of the improved understanding of data (captured as business meta data and incorporated into or linked to the CDM via a meta data repository tool), data quality can be improved.  If you don’t know what a data element is supposed to be, how can you know whether the data is of high quality?
  • The CDM helps to align IT to the business.  By taking the time to understand the business via the CDM, the business feels it is truly listened to and understood.  The CDM is used to validate and correct understanding – before solutions are developed thereby saving time, effort, and money.  Misunderstandings can be cleared on the front end of a project, rather than at the back end during implementation.
  • Information is a key asset – however, it is often not treated with the same care that financial, human, or other resources are treated.  Conceptual Data Models are a key enabler of Data Governance to ensure that information assets are understood and utilized effectively.  The Conceptual Data Model helps an organization identify and understand its information assets and helps to ensure that highly complex information systems are aligned with the business vision.
  • Application design is significantly affected by the data model, whether the application is an Enterprise Data Warehouse, ERP, SOA, OLTP (Transaction processing, a Master Data Management hub, etc.   For example, if a many-to-many (M:M) relationship is missed (e.g. a CDM not developed), this can significantly affect application design.  A common way to resolve a M:M relationship is with an associative entity which means that 1 more table and 1 more relationship has to be taken into account in:  program code, screen forms, reports, SQL, interfaces, testing – significantly affecting project schedules and costs.
  • More important are the costs associated with the data duplication or missing data that can occur as a result of an incorrectly modeled relationship.  A large bank recorded all customer information at the account level.  When there was more than one customer on the account critical information (e.g. identifying information, FICO score) about the secondary customers was not recorded.  As a result, the bank did not have a true picture as to the number of customers it had and the true level of activity for its customers.

 

The measurement tool consists of a checklist (over 80 questions) in an MS Excel spreadsheet for scoring, and an associated chart for visualization of the results of the CDMA.  The checklist is broken into the following six (6) categories:

  • Descriptiveness
  • Presentability / Understandability
  • Model dissemination and usage
  • Model meta data
  • Lexicon / Terminology
  • Model maintenance

 

To derive the score for each question, the following five (5) criterions are individually taken into account (criterion 1 is a yes or no answer, criterion 2-5 are each given a score from 1 to 10).

  1. Defined as an enterprise standard?  Each question represents a functional consideration (“consideration”) for delivering outstanding Conceptual Data Models, and this criterion determines whether the consideration is part of the modeling standards for the organization being assessed.
  2. Degree of tool capability to meet desired consideration? Not all modeling tools are created equal.  This criterion helps determine whether the tool supports the functional consideration and the degree to which it does so.
  3. Client degree of importance?  How important is the consideration to the organization?
  4. EWSolutions degree of importance? To come to a more comprehensive and meaningful score, an objective, industry viewpoint should be incorporated.  EWSolutions is a vendor neutral consultancy with many years of enterprise and project level data modeling experience, and can provide an objective score of how important the consideration should be, given the scope and objectives of the modeling effort, requirements, the industry the organization is in, etc.
  5. Score.  Objective score from 1 to 10 assigned to the question judging how well the Conceptual Data Modeling effort addresses the question.

The scores for all of the questions in a category are averaged to derive a category score(Combined Score).  The combined score can then be graphed to provide an overall picture of your Conceptual Data Modeling efforts to identify strengths and weaknesses.

CDMACategoryRanking

The measurement tool is one part of the CDMA.  An accompanying methodology utilized by an independent third party helps to ensure the CDMA is conducted in a professional, effective manner to ensure that Conceptual Data Models meet the needs of the enterprise or project.  The assessment methodology includes considerations for:

  • Information gathering (Interviews, documentation review)
  • Architecture review
  • Modeling standards review
  • Modeling tool capability review
  • Modeling notation review
  • Presentation of findings

 

To be complete, the CDMA requires a summary report and executive presentation which identifies:

  • What the CDMA is
  • Approach taken for the CDMA
  • Current modeling methodologies / standards in use
  • Strengths of the Conceptual Data Models, and CDM practices
  • Areas for improvement in these
  • Recommendations, including:
    • Specific modeling recommendations
    • Ways to better leverage existing modeling tools, or recommendations for new modeling tools
    • Training required
    • Data analysis techniques, e.g. data profiling, that can be employed
    • Data Quality issues caused by poor or missing models
    • Recommendations for improving business / IT alignment using CDM’s
    • Standards to implement, why and how
    • How to realize the benefits of Conceptual Data Modeling (business and IT)
    • Project plan for implementing recommendations

 

The CDMA consultant or independent third party will present and review findings to appropriate executives, management, and staff.

For more information about the CDMA, please contact John Faulkenberry, Sales Manager at (312) 303-4242.

About the Author

Pete Stiglich is a Principal Consultant/Trainer with EWSolutions with nearly 25 years of IT experience in the fields of data modeling, Data Warehousing (DW), Business Intelligence (BI), Meta Data management, Enterprise Architecture (EA), Data Governance, Data Integration, Customer Relationship Management (CRM), Customer Data Integration (CDI), Master Data Management (MDM), Database Design and Administration, Data Quality, and Project Management. Pete has taught courses on Managed Meta Data Environments (MME), Data Modeling, Dimensional Data Modeling, Conceptual Data Modeling, ER/Studio, and SQL. Pete has presented at the 2008 MIT Information Quality conference, 2007 and 2008 Marco Masters Series, at DAMA at the international and local level, and at the 2007 IADQ Conference. Pete’s articles on Information Architecture have been published in Real World Decision Support, DMForum, InfoAdvisors, and the Information and Data Quality Newsletter. Pete is a listed expert for SearchDataManagement on the topics of data modeling and data warehousing. Pete is an industry thought leader in the field of Conceptual Data Modeling. He can be reached at pstiglich@ewsolutions.com

[1] www.disruption.com

[2] One insurance client changed its practice of product development as a result of the CDM effort.  The product subject area was being modeled and there wasn’t a clear picture of the many types of products and classifications due to conflicting terminology and a lack of product development standards.  An enterprise product taxonomy/categorization was developed as part of the CDM and became the standard way to classify products and helped the business understand its product line much better.

 
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