Data governance plays a critical role in every company’s data strategy. As your organization collects more and more data, you have the opportunity to make better, more informed decisions, but that data growth also introduces new challenges. The more data your organization collects, the less you can meet the demands of self-service, if you can meet them at all. And as more data pours in from more sources, your organization loses more control. There’s no way of knowing when or from where inaccurate data enters your systems, making untrustworthy data accessible to everyone. And then there’s the increased risks of noncompliance with government and industry regulations, and the higher costs associated with managing an increasingly complex data environment. 

That’s why good data governance is so essential for business success. It ensures the effective and efficient use of information in enabling an organization to achieve its goals, and defines data and people processes that provide the data quality and data security used across a business. It outlines who can take what action upon what data, in which situations, and using what methods. And it underpins how the business benefits from consistent, standard processes and responsibilities. 

What is a data governance framework?

Business drivers dictate what data needs to be carefully controlled (and to what extent) in a data governance strategy. For example, one of a health care provider’s business drivers may be to ensure the privacy of patient-related data, requiring that data be securely managed as it flows through the business to ensure compliance with relevant government and industry regulations. These requirements inform the provider’s data governance strategy, which becomes the basis of its data governance framework.

A well-planned data governance framework covers strategic, tactical, and operational roles and responsibilities. It ensures data is trusted, well-documented, and easy to find within an organization, and that it’s kept secure, compliant, and confidential.

Some of the most important benefits the framework provides include:

  • A consistent view of — and business glossary for — data, while allowing appropriate flexibility for the needs of individual business units
  • A plan that ensures data accuracy, completeness, and consistency
  • An advanced ability to understand the location of all data related to critical entities, making data assets usable and easier to connect with business outcomes
  • A framework for delivering a “single version of the truth” for critical business entities
  • A platform for meeting the demands of government regulations and industry requirements
  • Well-defined methodologies and best practices for data and data management that can be applied across the organization
  • Easily accessible data that’s kept secure, compliant, and confidential

Data governance framework examples — the traditional approaches

There are two traditional approaches to establishing a data governance framework: top-down and bottom-up.

The top-down method takes a centralized approach to data governance. It relies on a small team of data professionals who employ well-defined methodologies and well-known best practices. This means data modeling and governance is prioritized, and only later is the data made more broadly available to the rest of the organization to be consumed for analytics.

However, this creates a massive scalability issue. In this framework, there’s a clear distinction between data providers and data consumers, and only the former are empowered to have any sort of control over the data. In the past, this was less of an issue because there was a smaller amount of data to be governed, and fewer teams that needed access to it. But today, these small teams of data stewards can’t cope with the demand from data consumers. There are simply too many users making too many requests for these teams to manage. And it’s now a business requirement to have clean, complete, and uncompromised data available to everyone who needs it, whenever they need it.

Conversely, the bottom-up approach allows for much more agility when managing data. While the top-down approach starts with data modeling and governance, the bottom-up approach starts with raw data. After the raw data is ingested, structures on top of the data can be created (referred to as “schema on read”), and data quality controls, security rules, and policies can be implemented.

This framework, popularized with the advent of big data, is more scalable than the centralized approach, but it creates a new set of problems. Because data governance isn’t implemented until later in the process, and because anyone can enter data without control, governance is harder to establish. And as we already discussed, lack of data governance can lead to increased risk, a higher cost of data management, and an overwhelming data sprawl that grows progressively more difficult to control.

What we need is a modern approach to a data governance framework — one that establishes control early on in the process but doesn’t sacrifice the ability for users to become data owners and curators.

Collaborative data governance framework template — a modern approach

A collaborative data governance framework is all about balancing the top-down and bottom-up approaches. This framework recognizes that working with data as a team is essential for success; otherwise, the amount of work needed to validate that the data is trustworthy will be overwhelming.

The collaborative framework allows for an increasing number of data sources to be introduced by an increasing number of people across the organization. To maintain the scalability of data governance, well-defined principles for collaborative content curation must be established. These principles ensure scalability without compromising a defined level of trust in the content.  
 
By establishing these principles for data curation, anyone can collaborate as long as they follow the standards, and organizations can engage the entire business in contributing to the process of turning raw data into something that is trusted, documented, and ready to be shared. 
 
Of course, there are still some heavily regulated business processes with data elements that require specific attention. Risk data aggregation in financial services, for example, or data like consumer credit card information may not be the best candidates for this approach. In these cases, the collaborative framework can complement, rather than replace, the top-down approach. The organization’s data governance team should define which data governance model applies in these types of situations. 

Measuring success with a data governance maturity model

Data governance is a journey, not a destination, and to ensure its success, there needs to be a way to measure its progress and identify areas for improvement. 

The three maturity levels that organizations go through as they become more data-driven companies are:

  1. Data integration: application integration, data integration, and data loading
  2. Data integrity: data preparation, data stewardship, and data quality
  3. Data intelligence: data cataloging, data lineage, and metadata management

Because organizations require trusted data to empower data users, improve customer experiences, and make decisions with confidence, data quality must be a core component of any data governance program. The further along an organization is in this maturity curve, the more it can take advantage of powerful technologies like data profiling and data matching with machine learning. And the better positioned it is to get the maximum value out of all of its data assets while maintaining the necessary level of control and trust in that data.

Unlocking the full potential of a data governance framework

Governance is about more than data protection and control. And if implemented correctly, data governance processes don’t slow or prevent access to data. Rather, they can improve data access by helping organizations deliver trusted data to the right people in the right format at the right time, all while ensuring data privacy and regulatory compliance.

To get a more in-depth look at the methods and best practices you can use to implement a successful data governance initiative at your organization, check out the <Definitive Guide to Data Governance>. You’ll learn additional details about the topics covered in this blog and explore new areas, including:

  • Multiple ways data governance can save you time, money, and resources
  • How to choose the best data governance model for your needs
  • Three steps to delivering data you can trust at any scale
  • Tips for building a data team and a data-driven culture