¿A qué retos se enfrentan los consultores a la hora de implementar el Data Governance? (2024)

Zzzz. Data governance is the most non-sexy topic, like, ever. Only boring lawyers want to write treatises full of rules. Rules are hard to implement, monitor and enforce, despite an abundance of tools like glossaries, catalogs, metadata repositories, etc. Leadership does not understand it and won't enforce it. Boring and difficult, let's just kick the can down the road and hope nothing bad happens.Yet DG gives order to chaos and mitigates risk - including self-inflicted. Don't cause a critical data error that ends in loss of reputation and revenue.The rise of low code/no code citizen development like Microsoft Power Platform makes DG even more critical. Build a flexible, light-touch enforceable solution that users will follow.

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Data governance needs a leader. As a consultant, you can set up the structure for data governance and assign people to roles. You can meet with everyone involved and get buy-in. You can have them participate in creating the structure and documentation. You can even run the first meeting. However, if there isn't an effective leader in place when you leave, you run the risk of having developed shelf-ware. If the company is truly committed to data governance, which is likely since they spent money on consultants, they have to decide if they're ready for the consultants to leave. If they don't have a data governance leader in place, they might need to consider a fractional leader until they can get someone in-house on a permanent basis.

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Prevailing stakeholder attitudes think of Data its Governance as being project based and not program and business journey based. A longer strategic approach is needed to embed governance into the process at all levels as opposed to a stand alone concept.

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Ensuring data quality and consistency poses a significant challenge for data governance consultants. The need to audit, cleanse, validate, and integrate data from diverse sources and formats requires the establishment of rigorous data quality rules and standards. To overcome this challenge, you need to establish data quality rules and standards, and use tools and methods to monitor and measure data quality

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The challenge of ensuring accurate and consistent data is crucial, even if it is time-consuming. Providing correct data hundreds of times can be undermined by a single error, leading to a loss of trust in the data team's work. Therefore, it is essential to establish and maintain clear data quality rules, and to implement tools for ongoing monitoring and continuous improvement. Furthermore, collaboration among teams and education about the importance of data quality are key to success in data governance

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Data Risk Assessments primarily focus on evaluating and mitigating risks related to the security and integrity of data assets. It addresses data breaches, unauthorized access, data loss, and data security measures. If personal data is processed, Privacy Risk Assessments to evaluate current and future privacy risks to rights and freedoms of individuals might be necessary that specifically concentrate on the impact of data practices on individual privacy and compliance with privacy regulations, whereas Data Risk Assessments emphasize data security and protection from external threats.

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It's about building a secure data environment that aligns with the organization's risk appetite and business objectives. By integrating risk assessments, robust policies, and advanced security technologies, we not only protect sensitive data but also instill confidence in stakeholders that their information is handled with the utmost care and diligence.

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In my experience, Data Literacy is the bedrock for a data culture. We usually start educating the Executives with a more executive training and using some workshops to help them to visualize the possibilities and the importance for the subject. Conquering the leadership, we can start to create the vision of more autonomy and responsibilities related data usage to businesses areas, instead IT. The business areas should be able to read, work and communicate with their data. The IT or Data Office should be responsible just to provide guidance, services and more noble cross services related data and enable the business to run by themself with their analysis. But it all depends on time and a constant data literacy program in place.

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In our pursuit of successful data governance, the development and nurturing of a robust data culture and literacy are vital. Nurturing, or weaving data governance into the fabric of the organisation is foundational element that extends data governance need beyond compliance driver. In my experience, before introducing specific data roles entailing accountability and responsibility, start the conversation connecting familiar daily processes and decision actively taking place in people’s lives to data governance. This approach helps in breaking down potential resistance associated with new roles, and fosters culture receptive of data related concepts and ultimately enhancing data literacy within the organization.

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This is the scary part when you're getting individuals to 'sign up,' e.g., to be a data steward. If you tell them right off the bat just how many mutual decisions they will need to weigh in on, how much work they will need to do (e.g., defining business terms, determining metadata properties, delving into DQ), they could conclude "hell no, I won't go." For example, in the DMM, 104 practice statements express or imply governance engagement. Therefore, it is important to segment efforts and operate your governance program like a series of small projects, which you can complete, declare victory, and then move on to the next task.With governance, you are wooing, educating, and growing commitment over time. Recognition is important.

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Lots of measures and framework to measure maturity. However, most management is looking at maturity as a badge. This must not be the case. We need to measure maturity by how the organisation is doing with data governance. Having a framework and some policies in place are not maturity "badge". We need to have this kind of maturity goes across the organisation, from the CEO to the data analyst. This is always easier said than done. There must be good champion to lead this to get to this level. And one important thing - as with any maturity, it will take TIME. Some top management will be impatient about this and will force the issue. Again, this is a maturity killer!

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One of the biggest challenges for data governance is that it is considered a cost… And so, perhaps the most effective way for consultants to succeed in their implementation is to link data governance to data value… Try:1. Making a list of initiatives to be implemented 2. For each initiative, identify individual implications of not executing 3. Are any of implications financial?4. Capture the costs associated with each individual initiative 5. Are there clear indications of tangible financial benefits?Top tip: avoid, where possible, presenting single opinion solutions for delivering data governance… In my experience, three options provide ample room for focus and consideration…Hope these help

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Often times the greatest challenge lies with the “why”, and it’s important to break down to the 5 why strategy and communicate the same. You’ll find that it reaches out different personas and increases adoption.

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In my experience, the DAMA-DMBok guide is the most completed one, but it's a huge body of knowledge. To improve data governance maturity I prefer start small and focusing on and following this hierarchy: Principles, policies, standards, process and procedures. Defining them based on BUSINESS needs, not creating a lot of paper stuff that nobody will use for nothing.

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I never really liked the name Data Governance. I think there is a better message around calling it Data Enablement. Governance makes people think of stop signs. Enablement is around helping the company extract and even create maximum value from one of the biggest assets. Always maintain the "how can I help today" attitude.

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