How do you prioritize data governance initiatives by maturity level?

When assessing your organization's maturity level, it is imperative to be objective and honest. A low maturity level is nothing more than the starting point of your journey. By accurately identifying your true current state, you can identify key areas of focus and opportunities to mature in meaningful and impactful ways.

A learning from previous engagements it is important to note that you do not necessarily stick with the same maturity model throughout your maturity journey. In fact it is prudent to select the simplest model initially and then review if it's still fit for purpose as the data management capability matures over time

Assessing maturity with DAMA or DCAM is crucial to pinpoint areas for improvement and prioritize initiatives. Regular reassessment tracks progress and ensures alignment with evolving needs, making it a key step in enhancing data management practices.

In my work with enterprises at various digital maturity levels, I've noticed a critical oversight when setting data governance targets: not clearly explaining the "why." This understanding must reach beyond the champions to the whole organization. It's crucial for data governance, whose impact isn't always obvious and can be seen as stifling innovation. Many equate governance with control. Orgs that communicate the purpose and value of data governance well are better at addressing misconceptions, gaining support, and aligning all levels. This shared "why" is key to achieving data governance maturity and supporting business goals.

I help organizations establish Data Governance Working Groups. These groups bring together data professionals from different offices and divisions. Together, working group members develop governance targets and methods that align with the organization's data strategy.

Maturity Models have limited utility and must be used in context. They must be focused on Data drivers for an organization blending Data offense and Defense. Maturity models are generally geared towards a top-down regulatory approach to Data.

It can be tempting to "shoot for the moon", but it is important to remember that not every company needs to reach industry leading, optimizing levels of maturity in every facet of data governance. Setting achievable levels of maturity that align with organizational goals will serve the organization better than setting lofty, unachievable targets.

I want to highlight the idea of having in consideration the business value that comes with the data governance practice. Trying to invest a tonne of effort into something that won't bring business value might not end up in the good graces of senior management. Therefore, there's a necessity to align DG maturity with business needs and ambitions, so everyone is on the same page and executive sponsorship is secured.

1. A simple analysis on the gaps has to be performed on the current state of the data capabilities - People, Process & Technology. 2. The priorities of these capability gaps needs to be agreed, and published basis the value, risk and contribution to data & business strategy. 3. A roadmap & work breakdown plan has to be agreed.

Agree with including a process lens. By incorporating a process-oriented perspective into prioritisation, you can focus on critical area which are more susceptible to data quality issues and have a higher impact based on the criticality of data. The targeted approach to managing risk allows better use of limited resources while achieving maximum value.

For instance, you might give precedence to projects aimed at rectifying significant data quality issues, strengthening data security and compliance measures, or facilitating data-powered decision-making. Additionally, you should factor in the interdependencies, potential risks, associated expenses, and necessary resources for each project while striking a balance between immediate and long-term advantages.

In a previous role I found that its helpful to include a process, people and technology lens to the prioritisation matrix used to review data maturity uplift initiatives.

You may want to try an Outcomes and Key results overlay to help the prioritisation and the reasoning behind it for your stakeholders.

Build partnerships and engage customers in co-creating solutions. Cross functional collaboration is key. Iterate and adjust. Support communities of practice. Foster bi-directional literacy so that the data teams speak business and business folks become data savvy.

This is where the changes are happening; managing these changes is critical to the success of the Data Governance project. Effective change management ensures that all stakeholders are aligned, processes are smoothly transitioned, and the overall goals of the DG project are achieved.

Data governance is a program encompassing various streams such as standardizing terminology, inventorying metadata, ensuring data quality, privacy, and establishing data architecture. It also includes essential components like change management that span across all initiatives and streams in the program. Each organization has its unique way for achieving a success. My recommendation is to begin with a pilot initiative that covers multiple streams and business areas. This approach will allow you to identify what works and what doesn't, as well as potential barriers. By developing plans to overcome these obstacles and adopting a design-first approach, you can engage with your data users early on in the program and gather their valuable input.

Implementation should be iterative. Get the priority items completed first. That being said - if certain tasks are quick-hit, short duration, you may be able to fit them in the schedule.

Engaging in a DG thought exercise may make you feel good about the whole thing but the implementation is where the thought exercise meets the practicality of the circumstances and the messiness of the real world. However, you shouldn't get bogged down by precise implementation unless it ties back to compliance and regulatory controls and security etc You should adapt and evolve as you go in tune with the needs of the business and contextual circumstances.

Part 2: 4. Data User Experience: Critical to also measure and track the user experience of data users or consumers through these maturity assessments. Understand if users are spending time on non-productive data-related activities outside their core job responsibilities. 5. Inclusive Surveys: All stakeholders, from data leaders to analysts, engineers, and scientists, need to be part of this assessment—it's their playground after all. 6. Feedback Mechanism: Incorporate feedback mechanisms into data maturity assessments. 7. Keep it Simple: Complexity isn't needed here. Simple, tangible KPIs can often reveal the most about your data maturity. 8. Customization is Key: Customization is key for relevance.

I have found a highly effective tool working with my customers and prospects over the years: the governance scorecard. It consolidates all your key indicators onto a single page, with each metric color-coded to indicate growth, stability, or decline. Additionally, by simply clicking on each metric, you can view a progress bar that visually represents its performance. This tool is perfect for confidently presenting progress to your leadership and seeking assistance in areas that require improvement.

Understanding potential drawbacks in data governance is crucial to mitigate the impact of failures and ensure the success of a data management program. Without this awareness, initiatives can suffer from a lack of executive support, unclear data ownership, poor data quality, and insufficient communication, among other issues. These pitfalls can lead to ineffective governance, non-compliance, and resistance to change. By recognizing these challenges early, organizations can implement targeted strategies to secure leadership buy-in, establish clear roles, maintain high data quality, foster collaboration, simplify processes, and ensure ongoing compliance and training.

This is the most critical step, if you don’t measure things, you can’t improve them. 1. Set your metrics - find the top 3-5 metrics that are most important. 2. Set a benchmark for these metrics. 3. Set a target for each metric. 4. Execute Not harder than that

Measuring and reporting on progress towards data governance maturity goals helps reinforce the value and impact of the initiatives you have implemented. It can also serve as a reminder of how far you have come on your maturity journey.

I have used several different types of Maturity models to help drive data governance initiatives. I feel like they quickly fall flat. If you dive into the optimized state and ask real questions like what would it take to get there and when would that be possible; the obstacles are tall and wide. I prefer to set out a roadmap with achievable goals and a set of targets that stretch and push the organization to a new level of thinking. Maturity models can support rounding out the thinking for the achievable roadmap.

In addition to the valuable insights shared, I'd like to emphasize the importance of fostering a culture of data ownership and accountability throughout the organization. While setting clear metrics and aligning data governance with business objectives is crucial, it's equally vital that every member of the organization understands their role in data governance. Encouraging a sense of ownership over data quality and security among employees can significantly enhance the effectiveness of data governance initiatives. This entails providing training and resources to improve data literacy at all levels so that data stewardship becomes a shared responsibility.

To achieve true data excellence inside the organisation, one should take a strategic planning approach that outlines your vision, mission, and specific objectives. Assessing the current state of enterprise data and defining the target state of data governance maturity is pivotal. It's about improving user experience and trust in enterprise data with strong data management. Along with this a strong alignment with overall business strategy and other internal and national strategies influencing org. With this every organisation should adopt a custom data management framework focusing on people process and Tech. with continuous monitoring and excellence at its core. Hence a well-crafted data strategy embrace the real power of enterprises data.

A lot of good thoughts here. What I think is often overlooked or not clearly understood is the relationship between the scope of data coverage and the data governance maturity level. Governance maturity can be thought of as a scale ranging from something as basic as an inventory to a very sophisticated real time data quality monitoring system, with other governance services fitting somewhere in between. This scale can then be applied to your data assets to determine the desired target state for each data asset. It then is a relatively simple task to develop a prioritized roadmap for Implementation.

I think first of all need to understand where there is such a governance in the company or not. Do people know about data governance, benefits and so on? So : - Identify if the company has existing data governance practices. - Assess employee awareness of data governance and its benefits. - If awareness is low, provide training or presentations on the topic. - Share the advantages of data management and governance with the team. - Start implementing small initiatives to demonstrate the value of data governance. - Regularly showcase the positive impact of these initiatives to reinforce their importance.