In the matrix on our About page we list some of the most popular and up and coming Data Governance frameworks for 2024, but there are many frameworks in use today. If your company currently doesn’t use a framework, we can examine your needs and help you choose one that best suits your needs. We are primarily results driven and do not push any particular application vendor or solution.
Data Governance Overview
Data is critical to any business and a precious resource that is often targeted for attack, mishandled resulting in risk as well as under-appreciated for what it represents to the business. Data exists in all of the nooks and crannies of the business and contains a wealth of detail regarding how you are doing and how to improve. Ignoring it is folly and failing to protect and handle it properly can be disastrous.
We can summarize data governance down to four major tenants: Know your data, Find and Access, Control, and Qualify. A governance suite has to include the tools to execute all aspects of governance.
Common Data Governance terminology:
Business Glossary
A comprehensive collection of business terms including detailed definitions and relationships to other terms. With the glossary, it should be possible to automatically classify datasets and annotate them with the right business terms so users can quickly understand the true meaning of the data and put it in the right context. A business glossary is the foundation of a semantic layer that companies can use to define a common language across the business. A semantic layer makes that language (i.e. the meaning of the data) accessible to machines and with that opens the door to automation.
Data Catalog
The core element of a data governance suite. It holds all company data sources, and identifies, classifies, correlates, index, and registers data sets. Data catalogs can automatically identify relations and correlations between data sets. With that, we can get a comprehensive view of the data, and its flow.
Data Inventory
Catalog your data and tag it with meaningful tags such as business glossary entities, privacy and security levels, etc. A catalog enables consumers to quickly find and understand the data they need.
Data Ownership
Identify the individuals and the organizations that “own” a data set and are responsible to maintain it according to the company’s best practices. Like any asset, it is important to know who is responsible for it. Who maintains it, who grants access to it etc. Maintaining an up-to-date data ownership per data set saves time and effort and simplifies collaboration. Governance tools help companies maintain ownership and facilitate the process of granting access to data, sharing, and collaborating on it.
DPIA
A Data Protection Impact Assessment (DPIA) describes a process designed to identify risks arising out of the processing of personal data and to minimize these risks as far and as early as possible.
Data Quality
Data quality refers to the perceived appropriateness of data in a specific situation. It is a continuous effort to ensure that the data maintains its quality level so consumers know how to relate and trust it. In a broader sense, it encapsulates elements such as:
- Completeness — There are no missing values.
- Uniqueness — There are no duplicates.
- Timeliness — The data is ordered by a timeline with respect to time zones.
- Validity — All values are valid.
- Accuracy — All values are accurate.
- Consistency — Values are represented in a consistent manner.
- Relevance — Values are relevant to the problem space.
- Reliability — Values are true.
- Accessibility — Values can be accessed by users with special needs.
The business use case defines the aspect and level of quality required. Not all aspects of quality should be maintained at all times, so specific business rules should be defined to maintain and assess the data set’s quality. Governance tools such as enterprise data catalogs help the business automate data quality assessment based on business rules.
Data Laws & Regulations (for Security, Privacy, and Compliance)
As data owners, it is our legal responsibility per the GDPR, CCPA, CCPR etc. regulations to protect the data, and comply with privacy laws governing the collection and use of personal data. The assumption behind many of the privacy regulations is that customers own their data and it is their right to control where that data is stored and how it is being used. The fact that companies hold large amounts of user data should not disturb users to be in control. Therefore it is the user’s right to demand removing her data from all the company’s storage and change her contents at any point in time. That introduces a complex technical challenge as companies must be able to find and remove/update the data in a short period of time. Data governance tools are designed to help the company automate such service requests and produce the documentation required by law.
Master Data Management (MDM)
MDM creates and exposes a single master record for all critical business data from across internal and external data sources and applications.
Most companies regard data as a gold mine… whose nuggets are increasingly difficult to extract.
- Data is typically replicated in multiple silos (databases, Excel spreadsheets, etc.) spread across the enterprise with little or no governance to manage or update information. This affects the quality, speed, andavailability of data. Data quality issues lead to failures in business processes and transactions.
- Decision makers then make poor decisions based on incorrect data.
This is why data classification is essential. Master Data Management addresses these issues by providing a unified view of reference data for better decision-making and business processes.
Reference Data
(From Data Galaxy article‘What is Reference Data? Definition and Benefits’ at
https://www.datagalaxy.com/en/blog/what-is-reference-data/ ):
What are the types of Reference Data?
There are two kinds of reference data, as you can note from the definition: external and internal. External reference data includes rarely changing norms like countries, currencies, languages, and units of measure. Internal reference data is where it gets complicated.
What is an example of Reference Data?
For example, data that connects customers and products such as cost/revenue accounting information, sales personnel, business units, geographies, or industry data would all be included in the reference data.
What is Master Data vs Reference Data?
Reference data differs from master data. While both provide context for business transactions, reference data is concerned with classification and categorization, while master data is concerned with business entities.
What does Reference Data include in MDM?
Reference data is a specialized subset of master data that is used to classify and provide additional context around the data throughout the enterprise. While your core master data domains like customer, product and location data typically change infrequently, reference data can change rapidly over time.
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