Data Governance vs. Data Management: Principles, Processes and Frameworks Explained

The terms data governance and data management are often used interchangeably, but are they the same? Not quite. Though they are closely related, there are important differences characterizing data management vs data governance.

In this article we outline those key differences. We also explain why thorough planning for data governance strategy and implementation is essential for business-wide performance and compliance with current and emerging data protection regulations.

Splitting the Difference: Data Management vs Data Governance

Let’s start with a metaphor. If you’re building a new house, you’ll first need to create a blueprint. This includes all the measurements, features, and practical considerations. It’s a plan for how the whole thing will come together.

Once the blueprint is agreed, it then needs to be built. That requires people and technologies: the builders, electricians, plumbers all need to play their part; while all the correct equipment needs to be used to make it so.

The first part of this equation is a neat metaphor for data governance. Ultimately, data governance is a detailed plan for how data is gathered, processed and protected. It’s the blueprint by which a business:

• Understands and ensures the protection of data assets. Not just for the business but for the interests of customers, workers, and the general public.

• Ensures compliance with data privacy laws and regulations

• Supports data strategy

• Maintains the accuracy and usefulness of data

Data management on the other hand, is how a company puts this blueprint into action.

It’s the actual implementation of data architectures, tools and processes to ensure data governance objectives are met. For example, data cleansing and standardization tools are just one aspect of putting data governance objectives into action.

Often when data governance is discussed, it’s in the context of data protection compliance.

Without doubt, the fast evolution of new data privacy laws has increased the need for strong data governance procedures. Indeed, the situation continues to evolve as illustrated by the development of the California Privacy Rights Act (CPRA).

The Act, if passed, would effectively amend the CCPA and expand the compliance obligations of businesses collecting, processing and otherwise utilizing personal information on California residents.

However, the importance of data governance goes beyond regulatory compliance.

It’s also a fundamental part of how companies can ensure they get the most out of their data. Purely out of self-interest, you should understand how your data operates across the business: where you’re drawing it from, how it’s being stored, verified and activated.

For example, if you’re holding onto vast amounts of outdated or irrelevant data; it comes at a cost to the business (both in terms of storage but also potentially misdirected marketing campaigns as well as the financial and reputation damage caused by fines).

In Summary…

Data Governance: defines how data is accessed and treated within a broader data strategy.

Data Management: is the implementation of architectures, tools and processes to achieve data governance objectives.

Why Does a Data Governance Framework Matter?

The data governance market is set to be worth $3.53bn by 2023, according to research by Markets & Markets.

It’s easy to understand why. Nearly 57% of Gartner survey respondents cited “supporting data governance and data security” as one of the biggest challenges for their data management practice.

Ultimately, data governance is a form of quality control. But it’s worth looking in greater detail into why it matters.

Data Management and Data Governance Benefits:

1. Lowers Data Privacy Risks

As regulators more tightly regulate the use of data, both here and around the world, strong data governance procedures help ensure you don’t incur hefty fines or the damage to reputation associated with failed compliance. Under CCPA, the cap (per case) for unintentional data protection violations is $2,500, while the cap on intentional violations is $7,500.

2. Saves Money

Effective data governance can lower the costs associated with data, including storage, incorrect/corrupted datasets, and communications going to the wrong people. Once upon a time, marketers would boast of the size of their databases. The more savvy among them are now more excited by response rates and productivity that comes with a well-maintained database. The efficient cleansing of incorrect or misleading data (such as duplicate entries for the same individual) are an important aspect of ensuring that databases are a trustworthy source of information.

3. Improves Insight & Decision-Making

Accurate information boosts business-wide performance. Sales teams have access to clean and accurate databases, which means they avoid contacting the same person twice. Marketing teams are working from trustworthy data enabling them to create more engaging brand messages. Business managers can more accurately gauge the performance of their teams and identify opportunities for improvement.

4. Future-Proofs Your Business

New innovations in AI, VR and IoT, and other digital technologies; mean that data will have an increasingly vital role to play. The amount of data available to us is increasing at a colossal rate, as digital technology produces more and more of it, from more and more sources.

Data overwhelm is already a major problem, but the challenge is only going to get bigger.

But there is a solution. Yep, you guessed it: strong data governance and data management business rules and processes. While the process of successfully managing data will continue to be a challenge; those who do so will be at a competitive advantage in the fast-accelerating data-driven age.

Where Do You Start?

The logical starting point is your data governance framework given this will provide the blueprint for successful data management. It will outline the process by which you collect, store, verify and use data.

There are three key factors to address:

• People: your employees need to be incorporated into your wider data strategy, so that the whole team is empowered with the knowledge of good practice and possession of data skills appropriate to their role.

• Processes: create a process-driven approach to ensure that data is managed in a cohesive, productive and legally compliant way.

• Technologies: like people, technologies need to be incorporated into the governance plan, so that you have an effective means of collecting, storing, cleaning and organizing data so that it’s fit for the needs of your business.