A business glossary is a collection of business terms defined in user-friendly language, making it accessible for business users across the organization.
Constructing and managing business glossaries plays a critical role in データガバナンス efforts. They help mitigate miscommunication within and across departments by eliminating ambiguity in terminology.
Managing a ビジネス用語集 is essential in today’s complex, data-driven landscape. By undertaking this task thoroughly, organizations ensure all stakeholders are consistently on the same page.
If your organization hasn’t yet formalized a business glossary, the best time to start is now. Standardizing terms ensures clarity across different departments and minimizes confusion.
Consider the term “User,” which might be defined as a current user of a product or service from the “Vendor.” However, this definition may lead to ambiguity:
When issuing product updates or other communications, ambiguity around terms like “User” can cause confusion:
Misunderstandings from ambiguous terms can have significant impacts, including:
A well-maintained business glossary offers multiple advantages:
The first step in governing data is identifying what needs to be governed. Data stewards should pinpoint the terms used within the organization, which often stem from the company’s language and industry practices.
Sources of business terms may include:
Once terms are identified, managing them effectively requires grouping and categorizing. Establishing taxonomies makes finding and generalizing terms easier.
例
In a manufacturing organization, “Equipment” could be a category, with sub-categories like “Computing Hardware” and “Machinery.”
Not all terms are of equal significance. Prioritize those that represent critical business data, like ‘Customer’, ‘Order’, and ‘Product’. High-priority terms often serve as heads of taxonomy trees, streamlining data management.
After prioritizing, organizations can map relationships between concepts to form an オントロジー. This reduces complexity and helps organize data entities and their relationships.
Examples include:
This allows data stewards to manage a single definition of a concept and then map synonymous terms back to the preferred term. Ontologies may also uncover some terms representing values that another term may take. Each value should have a clear definition that needs to be managed in the glossary.
Establishing and documenting ontological relationships between terms is vital in helping organizations understand and govern their data.
Step 5 – Define: Creating Clear Definitions
Each data term should have a clear, reviewed, and approved definition by subject matter experts to avoid ambiguity. This ensures consistent understanding across teams, especially when dealing with specialized or industry-specific terminology.
Take a petrochemical company, for example. A safety procedure may state the following:
“In case of a broken drill chain, the fish should be removed before engaging the kelly. This should be performed only by the drill finger located in the dog house”.
While parties familiar with the industry may interpret the above as intended, terms like “fish”, “kelly”, “finger” and “dog house” have well-known alternative meanings and so introduce the potential for ambiguity.
Similarly, when making decisions based on data, all parties should have the same understanding of the meaning of that data. I.e. the “User”/“End-user” distinction above.
Step 6 – Classify: Assigning Classifications
Classify data based on characteristics like personal data, confidentiality, or business impact. High-priority classifications help organizations manage sensitive data and meet security requirements.
For example, personally identifiable information should be scored highly under “personal data” and “confidentiality.”
Such classifications help organizations meet the requirements for data more efficiently. Of course, this has applications where an organization needs to identify and establish security around sensitive data and information. However, it also has implications for data access and data democratization.
In summary, effective classification of data helps make the right data available to the right people.
Once classified, data stewards can use the classifications to assign rules for data based on priority such as usage, data retention periods and quality standards.
A business glossary is a living document that evolves as definitions, policies, and rules change. Ongoing revisions should be controlled and collaborative, ensuring that changes are carefully reviewed and approved to avoid conflicts in business procedures and documentation.
Changes should be well controlled, as changes to definitions of some terms can fundamentally change documents that reference them such as company policies and procedures. A collaborative approach to iterating a business glossary allows changes to be reviewed and approved by the appropriate authorities.
Business glossary automation provides organizations with an efficient way to construct business glossaries. Typically, the most time-consuming step is gathering and categorizing business terms. However, data-driven organizations can leverage their データモデル, which are often built using well-known business language and contain definitions and relationships between terms.
For instance, an enterprise 論理データモデル should be semantically similar to the ontology within the prospective business glossary.
Business glossary automation enables data architects to quickly and comprehensively harvest メタデータ from data models. This reduces the manual effort of populating glossaries and allows organizations to extract business terms and relationships directly from their models, facilitating the creation and ongoing maintenance of a business glossary.
Organizations seeking automation should consider IDERA’s ER/Studio. This enterprise tool assists data architects in designing and documenting data assets, making it an excellent resource for data governance initiatives such as building business glossaries.
ER/Studio not only helps construct but also manage business glossaries. Its automation capabilities support the iterative process needed to maintain the quality and integrity of the glossary.
ER/Studio allows organizations to synchronize updates to the glossary throughout the data ecosystem. For example:
With the release of ER/Studio 19.1, Collibra users benefit from a powerful integration between ER/Studio and Collibra Data Catalog. This integration enables the bi-directional synchronization of business terms and ontological relationships between the two platforms, ensuring changes in one tool are reflected in the other.
By connecting data modeling and governance in this way, organizations can introduce a unified data ecosystem.
Click here to learn more about the ER/Studio-Collibra integration
