What are the levels of maturity in a Data Governance program?

Level 6: innovative stage Level 5: optimized stage Level 4: managed stage Level 3: defined stage Level 2: initial or developmental stage Level 1: ad-hoc stage

I have found visuals help the most when trying to explain data governance maturity, so, think of "Ad Hoc" in Data Governance like a bedroom where toys and clothes are everywhere (like my daughter's room!). In this level, there's no clear place for things. When there's a data problem, it's like trying to find one toy in that messy room. Solutions at this stage are often quick fixes, like just tossing toys into a box without sorting. The data, similar to these toys, isn't well looked after, and mistakes can happen easily. People handling the data might not always work together, almost like kids playing separately in different corners. If a company stays at this level, they'll face challenges and might not get things done efficiently.

Ad-hoc maturity in data governance means that an organization manages its data in an informal and unstructured way. There are no set rules or policies, and actions are often just reactions to immediate problems. Data management varies across departments, there's little awareness or involvement in data governance throughout the organization, and no specific people or teams are responsible for it. This leads to inconsistent data quality and decisions based on individual know-how rather than standard procedures. To improve, organizations should develop formal data governance strategies with clear rules, roles, and goals focusing on people, processes, technologies and data as a strategic asset to the organization.

Data governance maturity levels: Initial/Ad Hoc: Informal practices. Awareness/Emerging: Recognizing the need. Defined/Repeatable: Formalizing roles and processes. Managed/Measured: Data quality and compliance. Optimized/Optimizing: Continuous improvement, strategic data focus.

Level 1 implies a lot of improvisation, and includes individual best-effort (re)actions. There are many open topics (including compliance issues), and areas for operational improvements and process formalization.

The phrase "some degree of Data Governance planning" in Level 2 shows we're just starting to get organized. A simple step like using easier Excel templates could make a big difference. This can help everyone track data better and work together more smoothly. It's a small change, but it can help us move towards stronger Data Governance, making the process less of a chore and more of a routine.

Here could we could leverage the power of a Business Glossary or Data Dictionary, which can serve as a common language among different stakeholders, ensuring everyone is on the same page regarding data definitions and standards. Initiating a “Center of Excellence” could further organize the efforts of data stewards, promoting best practices and nurturing a culture of continuous improvement in data governance. Moreover, having Business Analysts control the data management process can introduce a layer of expertise ensuring data aligns with business objectives.

- A well defined process to have the data governance measured in the reporting to the board and executives would ensure responsible leaders are made aware of any on-going risks that need to be managed as the data governance practices matures in the organisation

I disagree with this way to assess maturity level it's essential to link them to tangible benefits aligned with short, mid, and long-term business plans. For instance, imagine a scenario where a competitor improved its data governance maturity. This enabled them to harness data more efficiently, streamline operations, and enhance decision-making. As a result, they outpaced industry benchmarks and achieved ambitious growth targets. By showcasing such examples, we emphasize the direct correlation between data governance maturity and tangible business success, helping organizations make informed decisions about their data governance programs.

1. Ad-hoc/Initial Stage: Limited awareness and understanding. Inconsistent data management practices. No formal policies and procedures. 2. Repeatable/Managed Stage: Basic governance structures and processes established, standardized policies for increased awareness. 3. Defined Stage: Clearly defined roles and responsibilities. Formalized policies, standards, and structured data quality practices. 4. Managed/Optimizing Stage: Proactive monitoring and enforcement of DG policies. 5. Optimized Stage: Automated processes for quality, security, compliance, and continuous innovation in DG practices. Establishing foundations and fostering a data culture enables organizations to leverage data for strategic decision-making and innovation.

Working with HD 4k data ✔️, speaking the same language across business lines🪐 and having defined share data accountability are all signs of maturity for data governance and data initiatives in general 💪

Governance is not only as it is listed at the start of this article, and not only as some bits of the various levels this article refers to. According to top industry experts: 1. Data Governance is mainly agreements made and kept about how we manage data as an asset. 2. Regular assessment of maturity levels of all the 11 capabilities are crucial for success. 3. Modern approach is rather service minded than controlling, because success depends on how everyone behaves towards data. 4. Business needs to grow awareness. 5. DG to support by a decision support framework of clear lead: responsibilities, guidelines, standards, principles, processes. + Change by design & communication + Solid teamwork All is needed to succeed can not fit in here.

I see too much lip service to DG, but in most cases, these are the REAL data maturity levels: 0 - we love XLS 1 - we extract reports from apps to make them XLS 2 - we want a "data lake" to keep our XLS 3 - oh shit, we have a gazilion duplicates! 4 - we take data quality seriously now 5 - we go for data mesh!

There are many maturity models out there. Every major consulting house has one. In my experience, you should be careful about how you use them. Just because Model Y says you are mature, your company's C-Suite will probably measure entirely differently.