9 June 2022

A Data Governance Glossary: Terms & Definitions

Data Governance Glossary

As you start navigating on your path to data transformation and start with data governance, you will come across a variety of terms that can leave you overwhelmed. We understand that in this vast world of data management, data governance has a crucial part to play, and there are several subsets that make the whole.

At To-Increase, we have built several solutions that help our customers not just with their data governance needs but also data preparation, business intelligence, and advanced analytics. Therefore, we have put together this article to help our readers understand the several terms associated with data governance in order to build a solid foundation as the first step in their journey.

Before we dive into the glossary, we recommend reading our beginner’s guide blog that explains data governance and how it can help your business.


Related reading: What is Data Governance? Why is it Important?

Since it might not be possible to remember so many terms or read and understand all the terms listed in our glossary below in the first instance, we recommend bookmarking this article so that you can come back to it later.

Data governance terms

While data governance involves the strategy, the people, the tools, and the processes involved in data management, it is just a subset of data management. Data governance dictates the strategy for all the areas that come under data management be it data architecture management or master data. Let’s start with these terms that are closely associated with data governance.

Master Data Management
: Commonly referred to as MDM, Master Data Management is a technology-enabled discipline that comprises specific tools, processes, policies, and rules to ensure one point of reference for the entire organization. MDM ensures timely, consistent, and accurate data management and distribution across your business departments, entities, and applications.

Data warehousing: involves the storage of the organization’s various data sources in internal or external databases.

BI management: or Business Intelligence management ensures that the tools, processes, and units involved are following the guidelines outlined in the data governance strategy. While BI is the process of analyzing data that has been checked for accuracy and validity and delivering actionable business insights that help organizations make better decisions.

Document and content management: are both different processes that intersect. A DMS or a document management system is used to store and retain different document formats while a content management system can handle unstructured and structured data such as web content.

Data security management (DSM): ensures that there are measures to protect data from theft, breaches, and corruption. There are also laws in place that vary from region to region that organizations have to keep in mind while ensuring DSM.

Data operations management: is the management of DataOps or data operations and focuses on data delivery to the organization. DataOps deals with implementing, planning, and managing a distributed data architecture that will support a wide range of tools and guidelines that have been outlined.

Data development: is the collation of data sets with a common objective. This means the way the data is collected has no consequence on this process. An ideal data developmental process would help the organization chart out data standards that are aligned with consistent data collection.

Data architecture management (DAM): keeps a track of the organization's data assets and charts out the data flow. On the basis of the data flowing through several systems, DAM aims to provide a strategy for managing this data flow.

Data integration and distribution: ensures that data is synching and integrated across all business systems, applications, and the ERP. A data integration tool can be used to integrate all data so that there are no data silos that could slow down operations or result in issues. This is also a means to distribute data within the ERP and to other legal entities.

Data quality management: is the process of adding rules and validations to ensure that data is meeting the set criteria for accuracy, consistency, timeliness, integrity, validity, and completeness. Consistent data quality is required to ensure that any analytics performed on the data is accurate and meaningful. It would be advisable to have periodic assessments to your data to ensure data quality even with changes in validation rules.

Data stewardship: is responsible for the accessibility, usability, and security of the organization’s data. A data steward oversees all functions that come under the data lifecycle from creation to storage to deletion.

Reference data management: is the management of classifications that distinguish data across business systems. This include tracking any changes, the creation and distribution of reference data.

Product information management: is the management of all product-related information that is used to market and sell products through existing channels.

Workflow automation: is the process of automating task flows for documents and data across business functions adhering to the set business rules. You can use a data entry workflow tool to quicken this process.

Data management: Data governance is a subset of data management but all the areas that data governance sets out a strategy for, come under data management as well. Therefore, data management involves the collection, storage, protection, organization, correction, management, and distribution of the enterprise’s data. Data management processes ensure that the data is ready to be analyzed for extracting business insights that impacts the growth of the organization.

Data terms

Data is the constant that flows through the organization and there are several terms associated with data that are relevant to data governance. In the section below, we cover terms related to types of data, data-related processes, and data storage.

Types of data

Master data: is what we like to call the single source of truth. While master data is the content, MDM is the practice area. It is the data that is absolutely critical for day-to-day operations within a business unit or organization.

Metadata: shares distinct attributes that help describe and categorize other data within a database. There are various types of metadata such as descriptive, structural, administrative, reference, statistical and legal.

Reference data: is a subset of master data that is used to classify other data throughout the organization.

Data related processes

Data migration: is the process of moving or migrating data between systems, formats, or servers.

Data protection and compliance: is an important process to safeguard and protect important business information from corruption or loss. Compliance ensures that there are strict guidelines that are followed to protect data and in keeping with international and local data privacy laws.

Bulk data transfer: A mechanism, usually software-based, which is designed to move large data files, supporting compression, blocking, and buffering in order to cut down on wait times.

Data staging: ensures a place for data to be stored where it can be validated or corrected.

Data storage

Database: is the collection of the organization’s data listed out that can easily be retrieved or searched via data catalogs or other means to categorize data.

Data Lake: is a storage repository for all categories of data regardless of its size. A data lake acts as a large container for data coming from various sources into an organization, internal or external.

Data Warehouse: is the central location of data that is integrated across systems and applications. A data warehouse stores real-time and older data and can be used to create reports and for analysis.

Extract, Transform, Load (ETL): is part of warehousing data and has to do with the movement of data from one location to another.

How can data governance help your business?

Using a data governance strategy with a framework to govern your data from potential errors, misuse, or duplication you can enhance your overall data quality and be confident in the business insights you derive from data analytics. By creating a guideline of policies on processes, access, storage, and distribution, you can ensure your data is a single source of truth for your organization.

The benefits of data governance are vast and you will see the ripples of improved data quality in your streamlined processes, better analytics, and better insights to help you make smarter decisions. To start off with data governance, if you are looking for more information on improving your data processes, be sure to attend our webinar on Data Entry workflow.

Tune in to our webinar to understand how you can improve your data workflows.

Jerry Caous
Jerry Caous,
Jerry Caous,
Sales Specialist Business Integration

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