What do you do if your data governance strategy is not effective?

Assessing current state in terms not just data (governance) maturity but with business sense : - Data Management Fundamentals: how the company manages data (accessing it, sharing, processing... with quality/trustful, governance and all DMFundamentals) - Business Analytics Activation: how the company takes advantage of better data in business terms/impact Showing how the company is (un)balanced in these terms will help business users understand current point of departure, discovering gaps and opps 4 improvement and reflecting about its potential and desired target state, in a challenging but realistic scenario.

The first step to improvement is to know where we are, to know and recognize our strengths, which are certainly there, and also our weaknesses or opportunities. It is also necessary to identify the priorities, because many times we can find many shortcomings, but it is necessary to attack the ones that can generate quick wins that allow us to begin to show the value of the implementation of the strategy.

In my opinion, putting a data lens (quality, security, availability, usage, compliance) gives great insights into the adoption of governance practices but are not necessarily a holistic indicator of its efficacy. While data quality may be pristine today, it may not be tomorrow. Then comes the question, how adaptive are governance processes and policies to changing data dynamics. Today’s dynamic data universe requires agility and nimbleness. Assess governance operational effectives including roles, responsibilities, and processes to make it less bureaucratic and more democratic.

The key issue is to understand why it is not effective. A pre-cursor to the launch of any strategy is understanding what success looks like and review this regularly. This will involve having some metrics in place to measure success. An example in strategy is we want to improve compliance with legislation so a key metric might be we want to reduce the number of low level data protection breaches by X%. There are lots of things that contribute to this metric such as education, policies, technology but the overarching metric should move downwards if this approach is successful. If it's not, review and refine. Sometimes it helps to bring in external expertise or peer review to assist with reviewing and refining.

If a data governance strategy isn't yielding the desired results, it's time to reassess and recalibrate. 1. Conduct a thorough evaluation to pinpoint weaknesses and areas for improvement. 2. Engage stakeholders to gather feedback and insights on the shortcomings of the current strategy. 3. Realign the strategy with the organization's evolving needs and business objectives. 4. Implement corrective measures, such as refining policies, processes, and governance structures. 5. Invest in training and upskilling initiatives to ensure teams understand and adhere to data governance protocols.

This should probably be the first step - asking ‘what business problem we’re trying to solve’. Without alignment to strategic, corporate, or business goals most data governance efforts end up in the dust bin of history. No organization ever took on a data governance program for itself - it is best conducted as a necessary oversight framework tied to business outcomes and goals.

Ensure that your data governance strategy is aligned with the organization's overall business objectives. Revisit your strategy to determine that it supports key business objectives, such as improving decision-making, enhancing customer satisfaction, or achieving regulatory compliance. Adjustments may be necessary to ensure that data governance efforts are contributing effectively to achieve these objectives.

In my opinion, Data Governor must be from business or the program manager supporting business. Only then there will be right traction.

Absolutely align with business goals. Make sure you understand issues that may impact the organization’s ability to reach these goals and then align your data governance objectives to these.

Align your data governance strategy with business/organization objectives; Prioritizing data quality and consistency; Ensure regulatory compliance and data security

Il faut Federer ses équipes autour de la gouvernance et hiérarchiser les droits pour une sécurisation de la Data, une gouvernance est avant tout inscrit dans une stratégie de sens et d ordonnancement

A common issue in ineffective data governance strategies is ambiguity around roles and responsibilities. Clarify and communicate the responsibilities of data stewards, data owners, and other key figures involved in data governance. Ensuring everyone understands their role in managing and protecting data is crucial for fostering accountability and enhancing data governance efforts.

Create a 1 page RACI matrix for the organisation. Make sure that this is included in the employment terms for the employee. You must have top leadership support to implement these.

Accountability and responsibility is tantamount to success as it sets out who should do something and why it should be done. Be clear and concise in roles and responsibilities. Develop a RACI (responsibility/accountability/consulted/informed) matrix if necessary.

Clear roles and responsibilities need not only be defined but need to be written into job descriptions and become part of the day-to-day operations of the business. It should be made clear that data governance is not a technology or IT thing, but is the responsibility of all in the organisation. A clear message needs to be sent from senior leadership by senior leaders being champions of good data governance and leading by example.

Examine and refine your data governance policies and standards to ensure they are comprehensive, enforceable, and aligned with the best practices. Policies should encompass all essential aspects of data management, including data quality, privacy, security, and lifecycle management. Clear, effective standards are essential for guiding behavior and ensuring consistency in how data is processed across the organization.

Policies and standards are of course important and it can sometimes be very difficult establishing these by overcoming what I call "corporate inertia". I recommend organisations begin with the basics, e.g. a data governance framework introduced at a project level and then built upon from there.

What is often overlooked or forgotten when establishing policy and best practice is that it is compromised by poor integration with business practices and poor training. The policy is only as good as the people brining it into practice, especially in systems where there is a high degree of autonomy around data use (and therefore more chance for practices that go against policy) this is critical, without the proper support and training to implement policy there is a high likelihood of practice that breaches a new policy or procedure.

Richtlinien und Standards sind immer nur so gut wie das aktuelle Wissen. Ein mir bekannter Fall ist die Berechtigung des Administrators. Woher wei? man, dass dieser irgenwann einmal Unfug macht. Wer überwacht die W?chter? Und Standards gehen von eine zuvor erkannten Referenz aus. Was Gut/wie schlecht sein kann… wir arbeiten n einem endlichen Rahmen und da machen Regeln/Standards oder Richtungsempfehlungen Sinn. Darüberhinaus haben Demokratische Regeln erst uns Menschen geholfen - fern der nahen Familie- zu funktionieren und erfolgreich zu sein. Ich bin für Richtlinen merke aber dass der Reiz diese zu brechen bei jedem bleibt und dass ist irgendwie gut, sonst ist das Leben nicht mehr ?divers“ oder ?bunt“ oder ?prosperieren“.

To build a strong data governance strategy, start with setting up clear policies and standards—it's a game-changer. Think of it as setting the rules of the game for how data is handled, ensuring everything from data quality to security is top-notch. Keep these policies in sync with your business goals and the ever-changing regulations. A key point is: make this a team effort. Involve people from different parts of your organization to get everyone on board and accountable. This not just boosts data quality and security but also turns data into a powerful tool for decision-making. A Good data governance is all about evolving and adapting. So, keep iterating and improving. It’s a journey that keeps your organization sharp and innovative.

I have come across many large / mid size organizations who fall for the new shinny object without an understanding of need vs. want, which for sure will morph over time as the organization and more so the data governance matures. Start with an understanding of where you are in your governance journey, what are the objectives, and evaluate investments for ROI before making such investments that may result as a perpetually licensed showpiece. Never know a simple MS SharePoint may be the best tool for what is needed for governance in your organization.

For me it's very useful to implement a Data Management / DataOps Framework to help the people that works around data to be aligned and manage their Data Products in an interactive way. This Framework helps it to achieve Data Governance goals as have higher levels of data quality, decrease the security risk levels a increase the knowledge about the Data Product managed (doc, links, categorization, ownership, security profiles,...). I implemented it some times and always it works. The people begins to work aligned through the process.

Evaluate the effectiveness of the processes and tools currently utilized for data governance. Identify areas where improvements or enhancements are needed, whether it's implementing new technologies, streamlining processes, or enhancing data quality controls. The implementation of processes and tools is crucial to implementing your data governance strategy.

Effective governance relies on choosing tools and processes that enhance strategy efficiency. Key to Success: Automation and simplification are essential. Tools that auto-categorize data boost operational efficiency significantly. Case Study: A health insurance company implemented a DG tool for HIPAA compliance, which proactively identified and addressed compliance gaps, reducing manual efforts and compliance risks. This led to secure patient data and lower compliance costs. Choosing the right tools and continuously refining them creates a resilient, effective data governance framework. The health insurance company's success in managing HIPAA compliance illustrates the value of this strategic approach.

It is important to note that technology is not a silver bullet and data governance strategies generally fail because of the people and process aspects. Tools like an automated data catalogue can of course make things easier, but it is important to first have the supporting processes and roles in place.

From my point of view, it is essential to have a working methodology focused on continuous improvement. We must understand (or assume) that it is practically impossible to have perfect repositories or processes without any type of error, and even if we manage with a lot of effort (and investment) to get closer to this perfection, new processes, new sources, new requirements will always appear. The key is to create a culture that puts data quality, privacy and security at the center of the data culture. And don't just set and forget your data governance strategy; review it constantly to make sure it's still right and having the desired impact and effectiveness.

Article est intelligible et pertinent. Je propose de rajouter au point 6. la notion d'amélioration continue, consistant à effectuer des mises à jours régulières pouvant être déclenchées selon le cas par un changement au sein de l'organisme, une évolution réglementaire, une conjoncture sociale ou économique, etc.

Revisit and clearly define what you want to achieve with data governance (e.g., compliance, data quality improvement, data democratization) and align these objectives with overall business goals.

Again, these are good things to monitor and are indicators of good practice and policy being in place, but one of the most critical areas to monitor is stakeholder knowledge and understanding, not just in terms of academic understanding but of the business or organisational practices around privacy and data governance and how they adopt this into their day to day work. Monitoring uptake of new practices is key in establishing whether your organisation is now in a more secure and compliant state.

If your data governance strategy is not yielding the desired results, you should be willing to change it. Remember, a strategy is merely a hypothesis – a proposed approach based on certain assumptions. For instance, your hypothesis might have been: "If I clean all my data, then we will move faster in two years." However, if that hypothesis proves to be incorrect, you must approach the situation scientifically and reject your hypothesis. Rethink what behavior or approach might lead you to your desired outcome. A strategy is just a hypothesis, not dogma, don't cling to it rigidly. Try different approaches until you develop an instinct for what will succeed or fail. It's an iterative process of experimentation, learning, and adaptation.

How does your organization’s culture help or hinder governance efforts? While the mark of a maturing company is increased visibility and governance of their data processes, it could be that the organization views governance as “bureaucracy” or “red tape.” In these instances it’s crucial to engage with the organization where they are and bring them to increased maturity in ways that are congruent to their ethos and culture.

Any data governance strategy could be failing due to issues in strategy conceptualization or execution Conceptualization issues: -Incorrect operating model proposed -Lack of proper corporate buy-in -Roles and responsibilities not well demarcated or socialized with DG teams -Lack of well-defined KPIs and metrics to track success Execution issues: -Failure to adopt DG processes and policies by DG personnel -Procuring DG tool / technology that fails to fulfil requirements Correcting these issues requires: - Detailed analysis of enterprise to yield the right operating model, roles and responsibilities, KPIs - Securing executive buy-in, procuring appropriate DG technology - Proper evangelization of DG initiatives and policies/ processes

Data science and data governance are fundamental elements in modern Public Administration. Through real success stories, we have seen how these disciplines can generate significant benefits, such as greater efficiency in public services, better decision making and greater citizen satisfaction.

We need to review the strategy and perform the root cause analysis supported by a remediation strategy or a refresh of data governance strategy. To do the RCA for the existing data governance strategy perform current state assessment. Use persona based approach to analyse the state of operationalization of data governance strategy. Focus from people and process side of things as part of persona based approach and technology from current state assessment. Post this define remediation plan based on the gaps identified