Data Management vs. Data Governance: What is the Difference?

To fully unlock the power of data, enterprises must understand the people, processes, and technologies behind it. Managing critical enterprise data correctly and efficiently is the key to gaining insights, meeting regulatory requirements, and exceeding business objectives. These goals can only be achieved with a strategic and well-planned data management strategy, which includes the need for effective data governance. While both data management and data governance work in unison to build, maintain, and manage enterprise data, they are in fact different.

To differentiate between data management and data governance, it is important first to have a clear definition of each.

Data Management

Enterprises struggle to integrate and manage the vast amounts of data moving in and out of their systems and applications. Without an organized and systematic strategy, data can quickly spiral out of control and wreak havoc on the business as a whole. As a result, enterprises have placed enormous effort into creating well-architected data management approaches.

According to DAMA, data management is a comprehensive collection of practices, concepts, procedures, processes, and a wide range of accompanying systems that allow for an organization to gain control of its data resources.  To put it simply, data management keeps your data organized in a safe, usable manner.

According to DAMA, there are 11 knowledge areas of data management:

1. Data Governance
2. Data Architecture
3. Data Modeling and Design
4. Data Storage and Operations
5. Data Security
6. Data Integration and Interoperability
7. Documents and Content
8. Reference and Master Data
9. Data Warehousing and Business Intelligence
10. Metadata Management
11. Data Quality

These knowledge areas cover every aspect of data—how it is integrated, secured, transformed, housed, and more. Each area should be included in a well-developed enterprise data management strategy. Even if one aspect is ignored or underdeveloped, the effectiveness of the overall umbrella is reduced. If there isn’t a clear framework in place to enforce policies or capabilities, the effectiveness of data management is significantly reduced.

Data Governance

The key prerequisite for creating a sustainable and successful data management strategy is to establish an effective data governance model. In our previous article, PeerNova defines data governance as a set of practices, policies, and capabilities that enable an enterprise to ensure that high data quality exists throughout the complete lifecycle of data. You can think of data governance as the backbone of data management; setting the standards, rules, and controls that all data must follow. To guarantee high data quality, data governance focuses on creating policies to ensure accuracy, consistency, and completeness (in addition to accessibility, compliance, and usage).

There are three capabilities that are essential in order to successfully implement a data governance model:

1. Clear Data ownership:  Data assets, especially critical data elements, need to have clearly designated owners, who are responsible for the content of those data assets, data sharing agreements, as well as data quality. Data assets include data, metadata, and rules that act on the data, along with the relevant permissions.
2. Trusted data: For data consumers to be able to trust data, especially critical in regulated industries like financial services,  there needs to be comprehensive data quality controls that govern data. The scope of these controls should be broad as well as deep:

   a) Breadth: Control scope should be end-to-end, i.e., front-to-back covering every step of the workflow. A data consumer should be able to “drill across” multiple steps of a workflow to explore the transformations that occurred across multiple systems that resulted in a specific critical data element.

   b) Depth: Controls should also be deep. A data consumer should be able to “drill down” from a critical data element and explore the transformations that happened within each system that acted upon that data element.

3. Easy discovery and exploration: Data assets should be organized such that consumers across the enterprise should be able to quickly discover the data they are looking for, request access to the relevant data assets, and explore the provenance of that data as well as the relationships that govern them. All discovery and changes on the data assets should be business-user friendly, as data stewards are not just IT teams, but also business analysts, operations team members, and regulatory/compliance teams.

Data governance doesn’t start and stop at different parts of the data’s lifecycle—the rules that are created to ensure quality and privacy apply to the data at every step of its journey. Therefore, if you don’t have a solid foundation of policies and controls, effective data management will be nearly impossible.

Four Key Differences Between Data Management and Data Governance

While data management and data governance overlap in some areas, it’s still important to understand a few key differentiators.

PeerNova’s Cuneiform Platform: Active Data Governance

Enterprises struggle with building effective data management and data governance strategies due to siloed systems and data quality challenges. Enterprises experience data quality issues due to their existing data governance approach and static metadata tools. 

PeerNova’s Cuneiform Platform is an active data governance and data quality tool that provides a strong backbone to enterprise data management strategy.  The platform automatically builds, updates, monitors, and optimizes data dictionaries, glossaries, catalogs, and rule repositories. Using a dynamic approach to data quality and management, the platform creates end-to-end (E2E), integrated, and active lineages across disparate tools and systems. The Rules Engine in the platform executes all business rules in near real-time. Data Quality rules are also run as part of the Rules Engine dynamically. This means that high-quality data and metrics around data quality are always current. When there are data quality issues, the platform provides integration into third-party workflow/exception management tools to ensure that the issues are resolved quickly. Root cause analysis of the data quality issues can be performed faster using active lineages.  Through a self-serve model, enterprises can create accurate regulatory and governance reports with strict audit control.

In summary, PeerNova’s solution ensures enterprises can more easily implement an effective data governance framework and data management strategy.

While data management and data governance are not exactly the same, they both work in harmony to achieve automated, compliant, and high-quality data. An enterprise cannot successfully gain insights, meet compliance requirements, or make better business decisions with one and not the other. Once this is understood, only then can an enterprise successfully unlock the power behind their data, leveraging it in a way that elevates their business to a whole new level of success. For more information or to receive a demo, please reach out to our sales team.

Sources:
Knight, Michelle. “What Is Data Management?” DATAVERSITY, 2 Oct. 2019, Link