It’s no secret that data is a business’s most valuable asset, but it can lose its value if it isn’t properly managed. Implementing a strong data governance framework is critical to keeping your data safe and productive.
What is data governance?
Do any of these questions sound familiar to you?
- Why is the data in my presentation wrong?
- Where did this data come from?
- Who owns data at our organization?
- Can we handle requirements from new privacy laws? How?
- Why do I have two different values for the same metric on these two reports?
- Can we provide or delete a customer’s data if they request it?
A data governance strategy can help resolve issues like these. Data governance is a program of decision rights and accountabilities to appropriately treat data as a strategic asset, including managing, leveraging and protecting it accordingly.
What are the benefits of data governance?
The benefits of having a strong data governance program are vast and include:
- Accelerating management decisions involving multiple systems with centralized accountability, documented escalation process for issue resolution, and improved information for management decisions
- Reducing duplication of data, number of system interfaces and manual data entry processes
- Increasing the accuracy and consistency of reports and dashboards leading to improved decisions, fewer expensive errors based on poor or inconsistent data, and reduced rework from these errors
- Reducing compliance issues
To realize these results you will need collaboration and dedicated resources—data governance isn’t a project you can leave on the backburner. But before you can implement a successful data program, it is important to understand the scope of data governance.
Data governance framework
The foundation for a successful data governance program is a strong framework, including:
Data stewardship: A data stewardship program is the operational implementation of a data governance program. Data stewards do the day-to-day work of administering the enterprise’s data.
Data quality: A proactive management of the quality of data in the enterprise.
Metadata management: A formal process of defining and documenting data assets across the enterprise.
Master and reference data management: An enterprise management of data elements that are critical and shared across data systems.
Data security management: A defined and documented inventory of what data needs to be secured, who can access it, and how to secure it.
Data privacy: Information privacy is a vital area of risk identification and management. New legislation requires organizations to assess compliance efforts in handling customer data.
Information lifecycle governance: The management of data/information from creation to disposition. It establishes enterprise standards, policies and procedures for data retention and destruction.
8 Ways to Ensure a Strong Data Governance Framework
Whether you’re starting from scratch, or enhancing an existing strategy, there are eight important principles to keep in mind for a successful data governance framework.
- Treat data like the strategic asset that it is and apply a disciplined framework that will manage, leverage and protect it.
- Data governance is not a project, it is a program. It requires people, processes and technologies to work together successfully.
- Data governance is a team sport and requires dedicated resources and business support.
- Data governance processes cross all data in all systems, internal and external.
- Think big, start small. Start with an opportunity to fix a real problem.
- Create a repository for data asset inventory, definitions and lineage.
- Use data governance to connect your data stakeholders to rally around data activities and decisions.
- Continually measure and share the value of your data assets.
Following these principles can help ensure your data governance program runs like a well-oiled machine.
Data governance maturity curve
You may be wondering, “What does data governance in action actually look like at an organization?”
The following data governance maturity curve illustrates different levels of adoption.
Even the most sophisticated organizations, operating at a high-level of data governance—the “run” state—began at a “crawl” level. By understanding where your organization is right now, you can make a strategic plan for data governance maturity.
The characteristics in the “crawl” phase describe individuals and departments who operate independently, with a corresponding short-sighted view of data and data standards beyond their business area. Data management efforts are typically reactive: ad-hoc requests are the norm, spreadsheets dominate, and larger analytics projects are backlogged. Conflicting metrics and reports make it challenging to trust and leverage data assets in the organization, spawning additional data integrity tasks.
When an organization progresses to the “walk” phase, business and IT leaders start to understand the value of data and information. They acknowledge the need for true enterprise information management—this acknowledgment is critical to advance a data governance journey.
In this phase, even the most determined leaders will be challenged by cultural barriers that inhibit progress. To overcome inertia and resistance, leadership needs to promote data management efforts through clear and consistent messaging, as well as through integration with existing organizational processes.
Many organizations find that creating a team to coordinate and promote data governance efforts helps bring data leaders and business leaders together to establish a data governance plan and oversee implementation.
Data standards, along with incentives and support tools, are researched, developed, questioned, and tested in this phase. Application and adherence are low at first, but slowly take root as benefits are seen and data governance processes are scaled for the business. A culture of operating as a data team begins to define the norm in order to fully transition to the “walk” phase.
Finally, in the “run” stage, organizations fully embrace data as a strategic asset. An enterprise-wide effort championed at the executive level, data governance is a regular and expected aspect of every project. Data is an indispensable part of business operations, with strategy and investment decisions being informed by measurable outcomes and expected ROI.
The CAO or CDO has a seat on the board of directors to provide an outside-in data perspective. Data and analytics training and self-service capabilities are provided for employees, allowing them to operate seamlessly within the model, with improved productivity and efficiency.
In organizations that reach this phase, data governance is not just supported, but exemplified by leadership in the organization, defining what is largely a self-managed, data-informed, productive culture.
Implement your data governance framework and program
You can improve the quality of your data, reduce regulatory risks and make better business decisions by implementing a data governance program. As you can see, getting to the “run” stage requires dedication and resources. But it doesn’t have to be difficult.
Once your leadership team truly understand the value of your data, it becomes easier to communicate the urgency around creating a data governance strategy. Working with a trusted advisor that leads with an independent perspective can help you develop a comprehensive data governance program that is custom-fit to your organization.