Documenting a Database

Key Approaches to Documenting Databases in ER/Studio

ER/Studio has two primary use cases: designing databases and documenting databases. In this blog, we will discuss documenting a database. We need to be able to document it to understand its contents. We have three approaches to this:

1. As a Localized Project
2. As Part of a Broader Data Modeling Initiative
3. As Part of a Data Governance Initiative

In all three approaches, we have two models:

• A model of the information that business users can easily understand.
• A model that provides a faithful snapshot of the data asset in technical terms.

The first two approaches will use traditional data modeling techniques, which include logical and physical データモデル.

The first two approaches will use traditional data modeling techniques comprises logical and physical data models.

Localized Database Project Overview

Project Scope

As a localized project we will look at the database in isolation.

Reverse Engineering Process

We reverse engineer directly from the database creating a 物理モデル in technical terms.

Building the Logical Model

We use ER/Studio to help us then build a 論理モデル of the data asset with logical names and definitions that make sense to the business community which can be published through the Team Server web product.

Naming Standards and Templates

We can create Naming Standards Templates that allow us to automatically convert unintelligible technical abbreviated names to expanded logical names within the logical model.

Business Metadata Management

Attachments allow us to specify business properties so that we can store useful business metadata against the database.

Data Modeling Initiative Overview

As part of a broader data modeling initiative, we will maintain a single view of the information of the organization as a corporate logical data model or canonical model. We can then reverse engineer data assets and map them to that single logical model.

Universal Mappings in ER/Studio

ER/Studio has the notion of Universal Mappings, which allow relationships to be created across models. When they are published to Team Server, the user can explore these relationships to either:

• Explore from the logical to the physical: “Where is this data?”
• Explore from the physical to the logical: “What does this data mean?”, “Who owns this data?”, or “What rules apply to this data?”

Data Governance and Information Modeling

Here’s a breakdown of the paragraph into key points with explanations:

1. Overview of Data Governance Initiatives

   • In organizations that prioritize strong data governance, the information model is stored and managed in a ビジネス用語集.
   • This glossary is maintained by data stewards, ensuring that definitions and structures of data are consistent, reliable, and aligned with business needs.

2. Reverse Engineering and Classification

   • Sometimes, data governance initiatives require teams to reverse engineer data assets, meaning they analyze existing databases or systems to understand their structure.
   • After reverse engineering, these physical data models are classified by mapping them to business terms found in the business glossary.
   • This process connects technical data structures with business definitions, improving understanding across the organization.

3. ER/Studio Integration

   • ER/Studio supports this process by allowing users to classify models against business terms using a feature called チームサーバー.
   • Through this integration, users can browse the models, similar to earlier scenarios, and ask critical questions about their data.
   • This capability ensures that both technical and business users can collaborate effectively, fostering better data governance and clarity.

Unified Data Management with ER/Studio

• Collaboration Across Approaches

Nowadays, we find that all three approaches exist in the same organization, which presents risk if they occur in silos.

• Creating a Unified Ecosystem

ER/Studio helps produce a single unified ecosystem that allows different groups who need to understand the information and data of the organization to collaborate on a single contiguous model.

• Knowledge Exchange and Traceability

We can exchange the knowledge in this model with other tools that are involved in this ecosystem. This produces a single understanding of the information of the organization and the instances of these as data assets with full traceability between them.

Watch how we do it with ER/Studio

We have produced a series of videos which you can watch available in the リソースセンター.