Establishing a data governance framework is key to ensuring that data is secure, easily accessible, and compliant with international regulations, but the highly collaborative nature of how that happens can be a herculean task for data-driven organizations. Many companies fumble when figuring out how to build a data governance framework, but with proper procedures in place, you can build one that is informative and efficient. This article will lay out the steps to follow as well as provide helpful examples.
Jump to a section…
What is a data governance framework?
The pillars of a data governance framework
Master data management
How to build a data governance framework
Outline your data governance strategy
Start off small
Build channels for communication
Regularly update your framework
3 data governance framework examples
DAMA-DMBOK Functional Framework
Learn more about how to keep you and your business’ information secure with the Symmetry Systems’ guide, What Is Data Governance?
What is a Data Governance Framework?
A data governance framework lays out the standard operating procedure for collecting, retrieving, and storing information, creating a single set of rules that applies to all data across an enterprise. It ensures that data is reliable and consistent enough to drive business decisions, but also compliant with international regulations. It’s common to interpret data governance as data management, but that’s not the case; data governance is the rule set that informs how data management should operate.
A data governance framework is a fundamental part of cloud data security and keeps your enterprise from wasting time and money on non-value-added tasks derived from low-quality data management. Whether bad data is creating inefficiencies or poor data stewardship is creating data security breach risks, a data governance framework will help you tighten security protocols and reduce time spent on valueless tasks.
The Pillars of an Effective Data Governance Framework
These key components of a data governance framework help ensure it runs smoothly across an entire enterprise. The lack of any one of these can mean serious trouble for your business’ health.
Part of what makes data governance so difficult is that it requires an entire company to be mindful of how things are being stored and accessed, even though it’s often seen as a responsibility for IT. That’s where data stewardship comes in. A data steward is responsible for enforcing data access protocols, ensuring data quality, documenting metadata, and keeping compliance with regulatory requirements. While your team absolutely must be informed about standard operating procedures for data governance, the data steward acts as a line of defense to make sure that data is being handled properly.
There’s good data and there’s bad data, and it’s essential that you understand the difference when building a data governance framework. Good data is accurate, complete, consistent, and well-formatted. When all of those prerequisites have been met, we can guarantee that the data we’re accessing is fit for informing business decisions. It’s important to perform frequent data checks to look out for these metrics, especially when assessing whether or not regulatory compliances are being met.
Master Data Management
If high-quality data is the goal, master data management (MDM) is the means to create and manage it. MDM is the technology-backed process by which enterprises maintain a master list of data. Having multiple master lists is inefficient, so MDM helps centralize, localize, categorize, synchronize, and enrich a single master list.
It’s key to connect your data governance framework to use cases that reflect your business needs. For example, one of the primary drivers of a solid data governance framework is to implement self-service business intelligence. Decision-makers within an enterprise need data to help come to actionable conclusions, but that’s typically reliant on IT professionals to help pull data for reports. Unfortunately, it’s all too common for IT departments to be understaffed, and that makes generating data-driven reports difficult. Self-service BI helps decision-makers generate their own reports, but that relies on data governance frameworks that make accessing information easy and secure.
There’s also something to be said for machine learning and AI, which also benefit from air-tight data governance protocols. Both are highly dependent on quality data to function, so data governance is a crucial part of their success.
How to Build a Data Governance Framework
Now it’s time to take each of those pillars and tie them together. Whether you’re handling 100 files or 100,000,000, we can guarantee compliance and efficiency by keeping the four pillars in mind and outlining some fundamental goals. Let’s look at some crucial steps.
Outline Your Data Governance Strategy
How do people in your organization use data, how do they access it, and who will make sure that it’s compliant with regulations? These are all questions that you should be asking as you develop your data governance framework. It’s a wise idea to have meetings dedicated to data governance on a semi-regular basis to keep track of how your data needs are changing and keep tabs on how your current strategy is faring.
You’re not going to meet all of your data governance needs in one swing, so start small. Where are the biggest holes in your current data management solution? Which business areas need the most attention? Pick one of them and use it to test how well your data governance framework applies, then start to expand to other areas if your testing goes well.
Build Channels for Communication
If there’s one key takeaway when researching how to build data governance frameworks, it’s that communication between departments is essential. Everyone needs to know the proper procedures for accessing and storing data, and that means inter-departmental collaboration. Establish a line between the data steward and other employees so that questions can be answered as they arise. Again, data governance is about consistency and clarity, and that’s primarily people-driven.
Regularly Update Your Framework
Finally, it’s important to keep your data governance framework up-to-date. During data governance meetings, carve out time to discuss possible updates to your framework. Issues are bound to pop up eventually, so it’s best to approach data governance with that in mind. These don’t need to be fundamental changes, but tweaks to refine your approach.
3 Data Governance Framework Examples
Your data governance framework doesn’t need to be wholly original. There are many existing frameworks out there that your company can use to make the process a bit more approachable. While it might be difficult to find a one-size-fits-all solution, these are some of the best examples of data governance frameworks currently available.
DAMA-DMBOK Functional Framework
Picture data governance as the hub of a wheel. Surrounding that wheel are nine key knowledge areas, each of which is an important aspect of data governance. That’s the essence of the DAMA-DMBOK Functional Framework, which uses areas like Data Architecture Management, Data Development, Data Quality Management, and more to create a cohesive framework for how data governance should work within an enterprise. The framework even goes so far as to define how a company’s culture must evolve for such data governance protocols to work, making it a valuable asset for teams of any size.
McKinsey’s framework hammers home the value of data governance from top to bottom within a company, emphasizing that even C-suite executives should pay close attention to protocols and procedures, rather than relegating the responsibility to IT. In doing so, their framework helps lower the number of employees stuck on non-value-added tasks that stem from bad data quality. Executives are able to champion their individual data governance areas, assign effective data stewards, and ensure proper prioritization for tasks based on their improved data sets.
The McKinsey data governance framework starts with a central data management office run by a chief data officer. That CDO creates a targeted data strategy and governance leadership roles that set the standards for data governance frameworks, organizing day-to-day tasks by their data domains. Additionally, the framework uses a data council that brings governance leaders and the data management office together to assign priority tasks and design corporate strategy, as well as approve funding and address issues as they arise.
Finally, the Eckerson system has six layers and 39 components, which highlight key factors like people, culture, processes, methods, technology, and goals that are crucial to building a data governance framework. It’s a similar system to McKinsey fundamentally, but it also leans heavily on designing a modern, scalable system. Eckerson also stresses that creating a data governance framework is a journey rather than a sprint, a mentality that helps build a strong foundation for safety and security from data breaches.
Security is crucial, too, as improper handling of data can result in data breaches, exposing documents and client information. Symmetry’s DataGuard helps you protect your data, tightening IAM policies around data with cloud security teams. DataGuard features complete visibility into your data objects, offering insights into compromised credentials, application vulnerabilities, and more to guarantee data governance protocols are being met. To see DataGuard in action, request a demo today.