¿Cómo prioriza las iniciativas de gobierno de datos por nivel de madurez? (2024)

In order to make proper maturity assessments, you must be honest with yourself.Some systems are flawed in their design. Others are flawed in their execution.

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The DAMA Data Governance Maturity Model is a framework developed by the Data Management Association (DAMA) to assess and improve an organization's data governance practices. It consists of several maturity levels, typically ranging from initial or ad-hoc practices to fully optimized and mature data governance processes. Organizations can use this model to evaluate their current state of data governance and chart a path toward more robust and effective data governance practices. As an organization progresses through the maturity levels, it gains greater control over its data assets, enhances data quality, and ensures compliance with data-related regulations and standards.

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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.

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Besides DAMA for maturity assessment, we can also leverage the DCAM framework, powered by EDM Council. It actually helps in doing an exhaustive fit gap analysis based on which you can build your Data Management capability enhancement roadmap. What's good in using DCAM is that you can actually tie back your Business Usecases to do the maturity assessment instead of trying to "boil the ocean". A good enabler for active data governance it also helps the program sponsors to assess the ROI

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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