Data Governance Maturity and Tracking Progress
Data governance is best defined as the strategic, ongoing and collaborative processes involved in managing data’s access, availability, usability, quality and security in line with established internal policies and relevant data regulations.
erwin recently hosted the third in its six-part webinar series on the practice of data governance and how to proactively deal with its complexities. Led by Frank Pörschmann of iDIGMA GmbH, an IT industry veteran and data governance strategist, this latest webinar focused on “Data Governance Maturity & Tracking Progress.”
The webinar looked at how to gauge the maturity and progress of data governance programs and why it is important for both IT and the business to be able to measure success
Data Governance Is Business Transformation
Data governance is about how an organization uses its data. That includes how it creates or collects data, as well as how its data is stored and accessed. It ensures that the right data of the right quality, regardless of where it is stored or what format it is stored in, is available for use – but only by the right people and for the right purpose.
Quite simply, data governance is business transformation, as Mr. Pörschmann highlights in the webinar. Meaning that it is a complex system that changes from one stable state into another stable state.
The basic principles of transformation are:
- Complexity
- Predictability
- Synchronicity
However, the practice of data governance is a relatively new discipline that is still evolving. And while its effects will be felt throughout the entire organization, the weight of its impact will be felt differently across the business.
“You have to deal with this ambiguity and it’s volatile,” said Mr. Pörschmann. “Some business units benefit more from data governance than others, and some business units have to invest more energy and resources into the change than others.”
Maturity Levels
Data governance maturity includes the ability to rely on automated and repeatable processes, which ultimately helps to increase productivity. While it has gained traction over the past few years, many organizations are still formalizing it as a practice.
Implementing a data governance initiative can be tricky, so it is important to have clear goals for what you want it to achieve.
According to Mr. Pörschman, there are six levels of maturity with one being the lowest.
- Aware: Partial awareness of data governance but not yet started
- Initiated: Some ad-hoc data governance initiatives
- Acknowledged: An official acknowledgement of data governance from executive management with budget allocated
- Managed: Dedicated resources, managed and adjusted with KPIs
- Monitored: Dedicated resources and performance monitoring
- Enhanced: Data managed equally
For a fully mature or “enhanced” data governance program, IT and the business need to take responsibility for selling the benefits of data governance across the enterprise and ensure all stakeholders are properly educated about it. However, IT may have to go it alone, at least initially, educating the business on the risks and rewards, as well as the expectations and accountabilities in implementing it.
To move data governance to the next level, organizations need to discover, understand, govern and socialize data assets. Appropriately implemented — with business stakeholders driving alignment between data governance and strategic enterprise goals and IT handling the technical mechanics of data management — the door opens to trusting data, planning for change, and putting it to work for peak organizational performance.
The Medici Maturity Approach
In a rush to implement a data governance methodology and system, you can forget that a system must serve a process – and be governed/controlled by one.
To choose the correct system and implement it effectively and efficiently, you must know – in every detail – all the processes it will impact, how it will impact them, who needs to be involved and when.
Business, data governance and data leaders want a methodology that is lean, scalable and lightweight. This model has been dubbed the Medici maturity model – named after Romina Medici, head of data management and governance for global energy provider E.ON.
Ms. Medici found that the approaches on the market did not cover transformation challenges, and only a few addressed the operational data management disciplines. Her research also found that it doesn’t make sense to look at your functional disciplines unless you already have a minimum maturity (aware plus initiated levels).
People, process, technology and governance structure are on the one side of the axis with functional data management disciplines on the other.
Mr. Pörschman then shared the “Data Maturity Canvas” that incorporates core dimensions, maturity levels and execution disciplines. The first time you run this, you can define the target situation, all the actions needed, and the next best actions.
This methodology gives you a view of the four areas, people, process, technology and governance, so you can link your findings across them. It is an easy method that you can run for different purposes including:
- Initial assessment
- Designing a data governance program
- Monitoring a whole program
- Beginning strategy processes
- Benchmarking
Data governance can be many things to many people. Before starting, decide what your primary objectives are: to enable better decision-making or to help you meet compliance objectives. Or are you looking to reduce data management costs and improve data quality through formal, repeatable processes? Whatever your motivation, you need to identify it first and foremost to get a grip on data governance.
Click here to read our success story on how E.ON used erwin Data Intelligence for its digital transformation and innovation efforts.
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