Votre équipe est divisée sur les directives de gouvernance des données. Comment vous assurez-vous que tout le monde est sur la même longueur d’onde ?

Begin by conducting comprehensive needs analyses to understand unique requirements and concerns of each team member. Different roles in your organization will have varying interactions with data. Look for patterns or commonalities across the team. Even if there are differences, there will likely be shared challenges or objectives, such as data accuracy, security, or regulatory compliance. Identifying these commonalities is crucial because they serve as the foundation upon which you can build consensus. Focus on creating policies and procedures that directly address the real-world challenges your team members face. Set up regular check-ins or feedback loops to gauge how well the guidelines are working and where they might need adjustment.

One of the best ways that I've seen alignment in terms of data governance initiatives is starting at the basics. Data governance often gets confused in many different circles because there's not a common understanding or definition of WHAT we are trying to accomplish. Once the "WHAT" is established, the objective of alignment between teams and stakeholders is a necessity for being able to move forward with an actionable and aligned program.

To unify the team on data governance guidelines, I recommend: Facilitate Open Discussions: Host meetings to openly discuss differing views, allowing team members to voice concerns and provide input on guidelines. Clarify Guidelines and Benefits: Clearly explain the guidelines, their purpose, and how they benefit the organization, making them more relatable and understandable. Seek Consensus: Involve team members in refining the guidelines, encouraging collaborative decision-making Provide Training and Resources: Offer training sessions Lead by Example: Demonstrate commitment to the guidelines through leadership actions Regularly Review and Update: Periodically review the guidelines to address any ongoing concerns

Also it requires clear understanding of what the end goal of data governance by each team members. The team members should be encouraged to ask questions about the outcome.

Start with a clear and comprehensive communication plan. Involve key stakeholders from different departments in the decision-making process. Clearly define the roles and responsibilities related to data governance. Provide training sessions to educate team members about data governance principles, policies, and best practices.

Needs should be defined in linear swim lane process maps aligned to OE or similar widely adopted North Star. The steps should reinforce the required data. The swim lanes in each process or sub process will show accountability across and within organizations. Each step of each process or sub process will have operational and required metrics. (If a metric cannot be associated with a step, 1) it is not a step, or 2) a potential trigger for waste.)

When establishing objectives, align with business objectives AND impact. Focus on those areas that drive the greatest business benefit.

Using the example of Telco company I’m currently with, I’d highlight several DG goals: 1. Data Quality: Ensure accurate, consistent data across the organization. 2. Privacy & Compliance: Protect customer data and meet legal requirements (e.g., GDPR). 3. Data Security: Prevent unauthorized access and data breaches. 4. Data Availability: Ensure data is accessible for operations and decisions. 5. Dara Lifecycle Management: Manage data from creation to deletion as per policies. 6. Standardization / Data catalogue: Promote consistent data management across systems. These objectives might generally be applicable to different types of organizations working simultaneously with various data sources.

Policy is important. However, starting with a simple statement of agreement regarding data handling will go a long way. Starting with a data governance agreement sets the foundation for what is expected of each member of the organization. This agreement should also be outlined in policy and standards.

Desenvolver política: Crie diretrizes concisas e práticas que atendam tanto às necessidades da empresa quanto aos padrões de conformidade.

Es ist wichtig, dass eine Datengovernance-Richtlinie nicht nur technisch korrekt, sondern auch für alle Teammitglieder verständlich ist. Statt komplizierte Fachbegriffe zu verwenden, sollte die Richtlinie klar und einfach erklären, wer welche Verantwortung hat und wie Daten verarbeitet werden. Jeder im Team, unabhängig vom technischen Hintergrund, sollte genau wissen, wie Daten erfasst, gespeichert und weitergegeben werden. So wird sichergestellt, dass alle Aufgaben effizient ausgeführt werden können und niemand durch unnötigen Fachjargon verwirrt wird.

Developing a comprehensive data governance policy involves several key components to ensure that it is effective, accessible, and understandable. Here is a step-by-step guide to creating such a policy: Executive Summary Objectives Scope Roles and Responsibilities Data Standards Data Lifecycle Management Data Access and Security Data Sharing and Distribution Compliance and Monitoring Training and Communication Incident Response and Breach Management Policy Review and Update Glossary and Definitions Appendices and References Once the data governance policy is developed, it should be distributed to all relevant stakeholders and made easily accessible, such as through the company intranet or documentation portal.

Nomenclature is everything. Having clear definitions avoids confusion, errors, and general complications. It is imperative to define an owner of any given domain to resolve disputes. One example is the identification of "Sales." For Human Resources, it refers to a department; for Accounting, it means shipped orders; for the Sales department, it's booked orders; for Information Technology, it's transactions; for Finance, its revenue; and for Marketing, its opportunities generated. The importance of establishing clear definitions and ownership within data governance cannot be overstated.

Training and raising awareness plays a critical role in ensuring guidelines are understood and followed. Offer training sessions to explain the guidelines in detail and provide examples of how to implement them in daily tasks. It is also important to ensure there is ongoing support available and dedicated channels and forums established to address questions with regular communication and training offered.

Training is the most critical step in a successful data governance program. This requires time, effort, and iterations (as learnings from "pilot data domains" should be factored in to refine the overall governance concept). As training is about educating people and changing their behavior, various training formats are required (in-class trainings, videos, recommended readings, email newsletters, intranet pages, Q&A hours). As data governance affects many different roles - each role with different data capabilities, training curriculas should be tailored to each role group.

Defining roles and responsibilities of each team member within the guidelines also helps. offer training sessions so that everyone understands the guidelines and knows how to apply them in their daily work. Listening and adjusting based on the team's experiences is key . Finally as a team leader, foster a culture where teamwork and collaboration are valued.

✅ 𝗖𝗿𝗲𝗮𝘁𝗲 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗲𝗱 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗠𝗮𝘁𝗲𝗿𝗶𝗮𝗹𝘀: Develop materials that are role-specific, such as detailed guidelines for data stewards and overviews for executives. Use real-world examples and case studies relevant to your organization. ✅ 𝗜𝗻𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲: Include exercises that allow the team to apply guidelines to actual scenarios they might encounter. This could be through simulations, role-playing, or guided practice sessions. ✅ 𝗢𝗻𝗴𝗼𝗶𝗻𝗴 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁𝘀: Periodically assess the team’s understanding through quizzes, practical assignments, or audits. Use these assessments to identify areas that might need retraining or additional support.

O treinamento é essencial para garantir que todos os membros da equipe estejam alinhados em relação às diretrizes de governança de dados. Fornecer sessões de treinamento abrangentes que explorem essas diretrizes em profundidade é fundamental para assegurar que cada integrante da equipe compreenda completamente o que é esperado. Durante o treinamento, é importante usar cenários da vida real que demonstrem como as novas diretrizes devem ser aplicadas no trabalho diário. Isso torna o aprendizado mais prático e relevante, facilitando a adoção das práticas corretas. Além disso, é crucial incentivar perguntas e discussões durante as sessões de treinamento.

Beyond offering valuable feedback to improve adherence to data governance guidelines, monitoring can also identify areas where the guidelines themselves need improvement. This makes monitoring a critical component of the continuous improvement cycle for data governance.

Monitoring the compliance is one of the important areas after all the rules and policies are defined. It is similar to having the rules but also to monitor the implementation to get the results. Only defining the policies would not fetch the benefits unless the users are monitored and directed to meet the compliance. So, monitoring the compliance for data governance is one of the most important aspects.

As you develop data governance policies, it's key to ensure that you have discussions around the business related to monitoring. You want to do this before things are implemented to ensure the full business is on the same page and aligned with the benefits and limitations related to it. Having clean monitoring metrics helps shift the conversation from subjective to objective needs aligned with the needs of the organization.

1. Clearly articulate the purpose and benefits of establishing data guidelines to define the goal. 2. Encourage participation from all team members to involve all stakeholders and allow diverse perspectives. 3. Establish a common understanding by defining key terms and concepts to remove ambiguity. 4. Illustrate the guidelines with concrete examples and scenarios to facilitate comprehension. 5. Foster open discussion and feedback to encourage active engagement. 6. Identify and addressing pain points by understanding the concerns and challenges of each team member. 7. Establish a clear decision-making process for making decisions and resolving disputes. 8. Document and disseminate the finalized guidelines. 9. Offer training.

Compliance and monitoring shows whether your procedures, roles and responsibilities as written are effective. Periodic audits and testing by your control officers as well data governance stuarts , data quality teams, and internal audit organization to assess vulnerabilities and gaps. The technology risks associated with data governance should be incorporated in the RCSA framework to add the compliance and testing efforts.

One thing I have found helpful when it comes to ensuring policies and guidelines are practical to implement is to pilot test them. Pick up a couple of use cases, follow the guidelines and review the outcomes. This can help demonstrate their effectiveness and provide an opportunity for further refinement. Encouraging feedback from all stakeholders involved and impacted and refining guidelines to ensure the guidelines work for a given organisation and its operational model is also critical. Collect feedback from the team on specific points of disagreement. Be open to revising the guidelines where reasonable and beneficial to the team.

In a company focused on improving data organization, leaders actively encourage feedback by creating easy-to-use feedback tools and rewarding valuable input. Regular sessions are held for open discussions on data management, and employees are trained on its importance. As feedback is consistently acted upon, employees feel their ideas are valued, fostering a culture of ongoing improvement. Obviously, it all based on employees confidence that there will not be a punitive reaction, embracing bottom up approach and entrepreneurial mindset among linear employees and middle management. There is no ‘bad’ feedback, it only might be more or less helpful.

A oportunidade de rever as decisões passadas e tomar medidas corretivas é uma prática que traz frutos para todos os lados de uma equipe de dados. Uma cultura que mantem a

Incentive o feedback: Promova uma cultura de abertura, onde os membros da equipe possam sugerir melhorias e ajustes nas políticas de governança.

Bring the entire team together for a dedicated session to discuss the data governance guidelines. This allows for open communication, clarification of misunderstandings, and sharing of perspectives.