The role of data modeling (DM) has expanded to support enterprise data management, including data governance and intelligence efforts. After all, you can’t manage or govern what you can’t see, much less use it to make smart decisions.
Metadata management is the key to managing and governing your data and drawing intelligence from it. Beyond harvesting and cataloging metadata, it also must be visualized to break down the complexity of how data is organized and what data relationships there are so that meaning is explicit to all stakeholders in the data value chain.
Data models provide this visualization capability, create additional metadata and standardize the data design across the enterprise.
While modeling has always been the best way to understand complex data sources and automate design standards, modern data modeling goes well beyond these domains to ensure and accelerate the overall success of data governance in any organization.
You can’t overestimate the importance of success as data governance keeps the business in line with privacy mandates such as the General Data Protection Regulation (GDPR). It drives innovation too. Companies who want to advance AI initiatives, for instance, won’t get very far without quality data and well-defined data models.
Why Is Data Modeling the Building Block of Enterprise Data Management?
DM mitigates complexity and increases collaboration and literacy across a broad range of data stakeholders.
- DM uncovers the connections between disparate data elements.
The DM process enables the creation and integration of business and semantic metadata to augment and accelerate data governance and intelligence efforts.
- DM captures and shares how the business describes and uses data.
DM delivers design task automation and enforcement to ensure data integrity.
- DM builds higher quality data sources with the appropriate structural veracity.
DM delivers design task standardization to improve business alignment and simplify integration.
- DM builds a more agile and governable data architecture.
The DM process manages the design and maintenance lifecycle for data sources.
- DM governs the design and deployment of data across the enterprise.
DM documents, standardizes and aligns any type of data no matter where it lives.
Realizing the Data Governance Value from Data Modeling
Modeling becomes the point of true collaboration within an organization because it delivers a visual source of truth for everyone to follow – data management and business professionals – to conform to governance requirements.
Information is readily available within intuitive business glossaries, accessible to user roles according to parameters set by the business. The metadata repository behind these glossaries, populated by information stored in data models, serves up the key terms that are understandable and meaningful to every party in the enterprise.
The stage, then, is equally set for improved data intelligence, because stakeholders now can use, understand and trust relevant data to enhance decision-making across the enterprise.
The enterprise is coming to the point where both business and IT co-own data modeling processes and data models. Business analysts and other power users start to understand data complexities because they can grasp terms and contribute to making the data in their organization accurate and complete, and modeling grows in importance in the eyes of business users.
Bringing data to the business and making it easy to access and understand increases the value of data assets, providing a return on investment and a return on opportunity. But neither would be possible without data modeling providing the backbone for metadata management and proper data governance.
For more information, check out our whitepaper, Drive Business Value and Underpin Data Governance with an Enterprise Data Model.
You also can take erwin DM, the world’s No. 1 data modeling software, for a free spin.