Templates for data models can be found for different industries, such as education and learning, healthcare, energy and utilities, banking and financial markets, insurance, telecommunications, retail, aviation, and more others. As a quick FYI, these are often put together by standards bodies or vendors of different systems and databases. They can also go by different names such as a standard data model, industry data model, or industry standard data model (ISDM). If you’re currently evaluating if you should be using them or not, please first consider the following risks and benefits.

Data Model Benefits:

  • Can help identify business processes and associated dimensions
  • Can reduce the time and effort of designing the data model
  • Will address most common business processes and provide business terminology
  • Can be extended to address your own business environment
  • Often includes data warehouse design models
  • Besides the logical data model, it might include the physical model or a database script and data exchange schemas (great if you’re building or extending a system)
  • Might include pre-built ETL jobs, metrics, and reporting templates – note that these are also usually tied to the vendor’s products and services

 risks and benefits of standard data models

Data Model Risks:

  • Some of your source data won’t be able to be translated and transformed into the industry model’s generic language
  • Does not necessarily follow best practices for business reporting, analytics, or source system data capture
  • Besides the models being industry specific they can also be country or region specific so remember to take this under consideration
  • Extensive work and challenges will occur to translate the source data language into the industry model and then translate it again into a vocabulary used in the final presentation layer for the business users to make sense of it. This is probably the biggest ignored risk so here is an example to clarify what I mean: “Let’s assume you have data stored into a Customer_Type_A field which translates to the industry model as Customer_Code_001 which needs to be outputted as Active Customer in the presentation layer.” Can be convoluted and in the modeling process, some of these insights and contexts can be lost.
  • Could disengage with business stakeholders and forgo eliciting the business requirements in favor of solely following the industry data model

Most of the time, industry standard data models can offer a lot of value, especially when needing them to develop and launch new products and services, but plan for mitigating the associated risks if you’re using them as a starting point to map your own business’ data model or develop your data warehouse, your own system,  and/or enterprise data strategy.

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About the author 

George Firican

George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

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