Here's how you can navigate conflicts in a Data Governance role.

Data Governance is like the invisible 'glue' that holds together disparate pieces, delivering trust and confidence in the end product. Successful data governance projects or data stewards executing this, begin with business objectives and goals, rather than focusing heavily on technical frameworks and solutions in an attempt to achieve perfection. In my past experiences at both large corporations and growth startups, educating business partners, creating RACI early on, and running through scenarios have helped clarify the WHY, WHAT, and HOW. The core of a successful data governance practice at any company lies in people and communication, not the tech stack.

In my context, I have noticed that people respect rules (policies) compared to verbal or informal directives. Also, myself I find it easy to work with standards in place. These avoid so much conflicts or frustrations once the whole team agrees. What I have seen working effective is having clear policies and processes in place. The policies sets the key expectations and everyone can perform their job with very minimal issues. Once policies are set, periodic reviews will help to improve the existing structure in place.

Even if I see that most among my stakeholders and team have heard about the roles of data steward and data owners, they often don't fully understand what these roles entail. Providing detailed job descriptions and a RACI matrix for each domain clarifies responsibilities and activities. This approach helps visualize clear boundaries, reduces misunderstandings and ensures everyone knows their specific duties, which can significantly reduce conflicts and improve collaboration.

Navigating conflicts in a Data Governance role requires technical acumen, strong communication, and understanding organizational dynamics. Address data ownership and quality issues by establishing clear policies and procedures. Engage stakeholders regularly, implement conflict resolution mechanisms, and leverage data governance tools for transparency. Provide ongoing training and promote awareness programs to foster a collaborative culture. Ensure regulatory compliance and risk management. By building cross-functional teams and incentivizing compliance, you can effectively manage conflicts and create a data-driven culture that values good governance.

Listening to your team members is crucial in this role. By actively listening, you can understand the key challenges and problems they face. Once these issues are effectively framed, it's easier to tackle them through open dialogue. Continuous and transparent communication helps address conflicts early, fosters trust and ensures that solutions are collaboratively developed and accepted.

Posso dizer com seguran?a que esse tópico é, sem dúvidas, um dos mais importantes no trabalho em equipe. Ter um bom diálogo, onde n?o existem represálias, por eventuais erros, transforma o ambiente de trabalho em um lugar mais leve, consequentemente, mas colaborativo e é aqui onde todos ganham

Dialogue is always the starting point for improvement, it should be welcomed by all roles in data governance. In my experience, these conversations typically highlight areas where a function or policy is being a blocker, and taking these feedback typically leads to a more well defined policy of process that is supported by the stakeholder it applies to. A well defined policy allows disputes to be reconciled in a shorter timeframe and solutions to implemented to enable an impacted person or function to move forward. This benefits the organization as a whole.

Clear Data Policy definitely is very essential for governance. In addition to ensure the policies are properly implemented adequate understanding of the policy, automation and enforcement wherever applicable is also equality needed.

Clear and well-defined policies provide a framework for decision-making and conflict resolution. They set expectations and standards, reducing ambiguity and misunderstandings. Run those policies through various use cases to check their effectiveness, at least with those use cases which are regular and if those are not sufficient, they have to be reformed/modified to ensure the key objective is attained.

A clear understanding of the roles and personas that exist in the organization and their need(s) when working with data is fundamental to defining comprehensive data governance policies for an organization. These policies should enable these roles and personas to execute their responsibilities without becoming the blocker towards them achieving their objectives. Broad policies should be refined with this in mind, rather than restrict with the intention to prevent governance incidences from occuring such as data leaks and PII breaches. With this approach, the organization may further progress in its data and analytics vision, supported and enabled by the governance framework introduced.

Dashboards and visualization tools can provide a clear overview of data governance activities, making it easier to track tasks and identify bottlenecks. Lineage tools help trace data origins, ensuring transparency and improving the ability to understand the data landscape better. BPMN workflows streamline processes, enhancing coordination among team members. These technologies enable the organization to gradually improve the organization's data governance maturity, leading to faster and more efficient outputs and reducing conflicts.

One of the key things in this role is not just staying updated with technology but also closely monitoring changes in data policies and regulatory compliance. Keeping up with the latest standards and responding to regulatory requirements is crucial. Regular training sessions for the team and stakeholders help everyone understand and reinforce the principles of data governance, reducing conflicts and ensuring cohesive collaboration.

All parts of the conflict must understand what are the point of the responsibility each another and how in the limit they must collaborate and take action based on theirs responsibility: this way you can guarantee a consensus.

The biggest problem with data analytics is it finds things some people dont want highlighted. For example, when your procurement data for "servers" does not match the enterprise hardware scanning tool output, it highlights deficiencies in the delivery data recording process, normally through human intervention. More importantly, if you have computers, not registered in your organisation, this is a massive Cyber threat. Good data governance requires key stakeholder buy-in, so my advice, take your data to the stakeholders and involve them in good governance, fixing process/automation and address the DQ, before you put up a dashboard, showing their deficiencies.

As with openness to having dialogues around processess and policies in data governance, achieving consensus amongs the stakholders enable the organization to move forward as each contributor will taken on part of the responsibility to achieve the solution collectively. The ability to obtain the buy-ins from those stakeholders involved and thr proposal that accounts for the each stakholder objectives is a key proficiency. The outcome having achieved this is the trust gained.