Better Data Governance with These Data Modeling Tips

Data governance can be defined as a collection of practices and processes used to facilitate the formal management of enterprise data assets.

The increased focus and value of corporate data resources have led to greater interest in using governance to ensure that information is used consistently throughout an organization.

Inconsistent data definitions or terms can result in confusion, loss of productivity, and an inability to take advantage of changing market conditions. Data governance can help eliminate these problems. 

An IDERA sponsored whitepaper presented by Karen Lopez of InfoAdvisors provides five in-depth tips for creating better data models to enhance data governance efforts.

In this post, we will look at two of these tips to give you a flavor of their benefits. We strongly recommend the paper to anyone involved with data modeling and data governance. It will repay your time with valuable information that can be used for improving data models. 

See also:

• What Is Data Modeling

Data Modeling Tips for Data Governance

It’s important to use high-quality data for a data governance initiative to produce viable results. Data quality is directly impacted by the quality of the underlying data models.

Simple data models that just provide high-level database diagrams are not very useful for improving data governance.

Collaboration is an integral component of a data governance program. Using data consistently across multiple internal departments requires a collaborative effort when creating the data definitions used throughout the organization.

Proper collaboration around the data models used for the governance initiative will save time and aid productivity when engaged in data governance tasks. 

Add Metadata to Data Models

The first modeling tip we will look at is to use metadata to extend and enhance data model entities. Adding business-related metadata to basic data models makes them more useful and understandable to the diverse audiences that will use them.

Taking the time upfront to include metadata pays long-term dividends by creating better models. Following are some items to include in data models.

• Extended definitions and notes that appear in data models help everyone understand the entities described in the models. Consider using a specific pattern when creating definitions to enhance data models and don’t rely on naming standards to convey all meaningful information. 

• Data steward information should be part of data models to provide relevant information on who should be contacted if concerns arise. Identifying the source of a data definition enables issues to be resolved quickly and directly.

• Data privacy and sensitivity classifications are necessary to allow data models to be reviewed and discussed by various entities in the organization. Everyone involved with the data model needs to maintain an emphasis on data security and correctly handling sensitive information. Securing sensitive data is one of the goals of a data governance program and will be improved with informative metadata. 

Include Data Security Requirements in Data Models

Security requirements are vitally important when creating viable data models. The need to secure sensitive data and comply with regulatory guidelines should be incorporated into data models to ensure everything is in place before implementation.  

• Logical data models should include business security requirements such as those needed for encrypting, masking, and accessing data objects. This includes defining which business roles can view unencrypted or masked data. 

• Physical data models are used by DBAs when implementing the models in databases. Technical security requirements need to be defined in physical models so data analysts can determine how data can be used, how it is masked, and how it needs to be protected. 

• See also: The Types of Data Model Explained

More descriptive and informative data models contribute to data governance efforts and foster better communication throughout an enterprise. They increase the value of data resources by making them easier to use when addressing business requirements. 

Tools for Data Modeling

IDERA’s ER/Studio family of data modeling solutions offer excellent tools for building the foundation of a robust data governance program. Three distinct data modeling applications are available to satisfy the needs of any organization looking to create better data models to assist with data governance programs. 

• ER/Studio Business Architect helps to understand how information flows through an organization and lets you document the relationships between people, processes, and enterprise data assets. 

• ER/Studio Data Architect enables teams to efficiently catalog data assets across platforms and track end-to-end data lineage. It enables sensitive data objects to be specified so additional security can be afforded to meet compliance requirements. 

• ER/Studio Enterprise Team Edition is a collaborative tool that streamlines the understanding and use of corporate data assets through the creation of an enterprise data glossary of business definitions. Models can easily be shared across the organization and collaboratively tune data definitions to conform to evolving business requirements. 

These tools are all available for a fully functional free trial period during which you can test their features and see how you can improve the data models that drive governance. Bolster your data governance standing with better and more usable data models for consistency across all areas of your business.