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Better Data Models to Support Data Governance

Effective decision-making relies on high-quality information. Even the most prescient and fortunate business leaders need the right data when deciding on a course of action. Low quality or incomplete information will lead to erroneous and perhaps dangerous decisions that can negatively affect an organization or business. 

Enterprise data assets have become increasingly important to the ability of companies to remain competitive in their market sector. Ensuring that data is used consistently and accurately throughout an organization can be extremely challenging. One of the methods used to address this complex issue is data governance.

Data governance can be defined as the planning, monitoring, and enforcement activities that are used in the management of data assets. The focus is on how decisions regarding data are made and how people and processes interact with data.

Robust data governance helps organizations obtain maximum value from their information resources while ensuring that data is used uniformly by all parts of the enterprise.

Data models are a crucial component of data governance. Better data models make it easier to complete governance tasks. They also facilitate the collaboration that is necessary to get the most value from data governance.

An IDERA sponsored white paper is available that takes an in-depth look at methods for developing better data models that will improve the utility of your data governance program.

Creating Better Data Models

Let’s take a look at some of the insights contained in the white paper to whet your appetite for the full course. The information it provides will enable your team to construct robust data models that further your data governance goals. 

Classifying data based on its sensitivity and the need for privacy is vital to avoid exposing an enterprise to the risk of a data breach. Data models need to take into consideration the attributes of the entities they are modeling.

The increase of data privacy regulations and the fines that accompany non-compliance makes it critically important that data privacy and sensitivity are addressed consistently. Business data stewards should be consulted for their expertise regarding the levels of sensitivity required for specific pieces of information.

Their in-depth knowledge of data resources can thereby be shared with everyone in the company. 

Using metadata to extend and enhance data entities makes them more useful throughout the organization. Data models are enhanced by the incorporation of business-related metadata into data definitions.

Rather than simply using database objects and properties for data definitions, including informative metadata makes the definitions more easily understandable. Following is some of the additional information that should be included in the metadata.

  • Data steward information that is included in data definitions provides easy access to individuals with the most in-depth knowledge of the specific entity if questions arise.
  • Data privacy and sensitivity classification is mandatory for effective data modeling. Data definitions should clearly identify the level of privacy and sensitivity required for that specific information. 
  • Business data objects are non-technical groupings of entities in a logical data model that make them easier to manage.

A data model needs to be easily accessible for use by everyone in the organization. Questions and comments can be included with data definitions to make them easier to understand and to eliminate concerns that may be shared by multiple individuals. Using a centralized repository to store data models enables them to be used more efficiently and contributes to the creation of a collaborative environment. 

Use the Right Tool for Data Modeling

Collaboration among teams and team members is an important facet of implementing data governance. Increasingly, organizations are implementing collaborative data modeling tools to facilitate this. Having a dedicated tool that facilitates collaboration helps organizations develop a consistent language and approach to data across departments, that isn't tied to one employee, or siloed from the stakeholders that could otherwise benefit from or contribute to the process.

Representatives from diverse areas of the organization need to work together and collaborate on defining the shared data language that forms the foundation of a governance initiative. 

The ER/Studio family of data modeling and collaborative tools offers teams the perfect vehicles for data modeling and, by extension, for implementing data governance. ER/Studio Enterprise Team Edition lets your organization build out an enterprise data model to serve as the foundation for a data governance program.

The tool can discover and document existing data assets as well as perform impact analysis on new policies or changes to models. Additional functionality can be used to implement naming standards. Teams can also create a data dictionary to enhance enterprise informational consistency.

One of the outstanding features of ER/Studio Enterprise Team Edition is its model and metadata repository. The repository enables real-time collaboration with data modelers with token-based check-in/check-out of model objects. Using this feature fosters collaboration with activity and discussion streams that allows knowledge to be easily shared among all involved parties.

A combination of the right approach and versatile tools like ER/Studio will give your organization the proper instruments for implementing data governance. This can’t be a bad thing in the data-driven and competitive business world in which your company is operating. 

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