ER/Studio Team Server adds powerful collaboration capabilities to メタデータ管理. All stakeholders, ranging from technical staff and business analysts through business subject experts and data stewards, can work as a team, with views of the metadata that are appropriate to specific roles.
ER/Studio Team Server provides users with catalogs of data models, including the enterprise models, along with a Data Catalog, which allows users to browse business glossaries of terms to find data assets or a technical data dictionary of assets. Team Server supports simple data governance or integrates tightly with industry-leading data governance tools like Collibra.
It includes powerful tools to help initiate a data governance program by harvesting business terms from データモデル.
This social collaboration paradigm allows users to have meaningful discussions regarding specific areas of interest, with a full audit trail. It not only records the decisions but also the process that led to those decisions. This capability enables all participants to reach a much higher level of knowledge and understanding, crucial for both data creation and data consumption.
The capabilities of Team Server are continuously enhanced to provide even greater functionalities in the future.
Data model quality is data quality. The quality of data models has a direct impact on data quality. Data models, as requirements and specifications for data, are the standards against which we measure data quality. Barebones data models, often just diagrams of databases, do not aid in better data governance.
How mature データガバナンス is in an organization affects their ability to succeed. Good data modeling also affects how and where we complete data governance work. Collaboration, usability, and completeness all form vital components of data governance efficiency.
Within this harmonious ecosystem, as data stewards define standards of data, data modelers can ensure that data assets deliver against those standards.
Are you new to data governance? An organization starting its path towards formal data governance might enhance its logical data models with necessary metadata around data stewards and sensitivity levels. This organization might also start a business glossary and implement a data model and standards portal. New data governance initiatives can leverage such knowledge from mature data modeling assets.
Do you need more mature data governance? A more mature organization with a formal, enterprise-wide data governance program might manage these items in other systems and publish the results to their data models and portals.
They might also document new data governance items in their models, then publish those to their formal data governance tools. Where we execute the work is less important than the fact that the activities are happening, being recorded, and shared.
Leverage your data models to support data governance. No matter where your organization fits in these maturity models, your data models play a crucial role in ensuring your data governance is engaging and successful.
Read the 16-page whitepaper “Five Data Modeling Tips for Better Data Governance” by Karen López to learn more about how better data models result in better data governance.
