You're facing data ownership disputes between teams. How can you ensure fair resolution?

- 📝 Clearly define data ownership. Specify the responsibilities and rights associated with managing, updating, and using data. - 📜 Establish a data governance framework. This should outline the criteria for data ownership, including creation, maintenance, and usage. - 🗣️ Ensure all teams understand these criteria. Communication and clarity are key to preventing misunderstandings. - 🤝 Facilitate open discussions. Encourage teams to voice their concerns and perspectives to reach a mutual understanding. - ⚖️ Mediate disputes fairly. Use the governance framework as a reference to make impartial decisions. - 📈 Regularly review and update the framework. Adapt to changes in the organization or industry standards to maintain relevance.

Ownership should be defined by who benefits or is at risk the most due to bad data. Ownership should align to company structure and culture and not one imposed via external entity frameworks

As part of establishing the Data Governance program, the roles and responsibilities are rolled out. Data Ownership is one among them. The role draws the line on the responsibilities. A formal agreement like Data Contracts can also help prevent and resolve disputes.

There will always be ownership disputes if you think you need a single owner or you don't clearly define responsibilities in your data governance organization. Just like multiple departments in the company use the same data, representatives from those multiple departments need to be involved in owning tasks related to that data. They work collaboratively, and could still disagree with each other, but their role is to work together for the benefit of the company, not their own personal agenda. Clearly define ownership of the rules (the business) and ownership of implementation of the rules (IT).

Clarity in responsibility and accountability that data ownership assigns to the owner team is very important. Ownership means that the team is responsible for all aspects of data quality, including accuracy, completeness, uniqueness, timeliness, validity, and consistency. The ownership should reside with the team where the data originates or at the first touch point within the organization’s data landscape if the data is onboarded from outside (as a third party). The data knowledge should be with the SMEs within the team, and stewards should maintain the quality aspects. As many organizations move towards a data product approach, this extends the team’s responsibility to generate products as per the requests from consumers and stakeholders.

The Data Governance controls may vary business by business, so first step would be to identify the essential controls for organisation which must be defined in DG policies.

Reaching "fair resolution" starts by involving the ownership-disputing teams in the writing of a "fair" policy early on, and that in itself is part of communicating clearly to stakeholders.

Policies are easier to craft when they are hinged on data principles that aligns with the strategic goal of the organisation. Signing off on the agreed principles will also improve effecting of the standard and policies going forward.

The policy should ensure the awareness of all stakeholders to be discipline in implementing the data governance policy. This includes from data input to storage management.

To resolve data ownership disputes between teams, start by reviewing and enforcing clear data governance policies. Understand each team's perspective on the data's usage and significance, and assess the impact on their operations. Propose shared ownership where feasible or negotiate specific roles, formalizing the agreement through data-sharing terms. If an agreement cannot be reached, escalate the issue to higher management for mediation. Regularly monitor the situation to ensure the resolution remains effective, and refine policies as needed to prevent future conflicts. This approach promotes fairness and collaboration while aligning with organizational objectives.

In disputes over ownership of data between teams, the following considerations should also be taken into account ... Responsibilities: Clearly define who is responsible for what data and define the purpose of the data. This minimizes confusion and creates clear expectations from the start. Conflicts: Establish a transparent process for resolving disputes. This framework should include steps for mediation, criteria for decision making and involve neutral parties if needed. Collaboration: Encourage teams to work together and share their views on the meaning and use of the data. Facilitating open communication can lead to mutual understanding and innovative solutions.

Data ownership is not a static concept. It can evolve over time as data is created, modified, and used. Therefore, it is crucial to establish mechanisms to periodically review and update such ownership (Data Owner figure). This person is responsible for, among others, tracking quality, analyzing and authorizing use cases, evolving to new use cases, etc. However, if we assign many responsibilities without compensation, the involvement of new Data Owners becomes complicated. The solution is to be able to measure the value that this data provides and to share this value very transparently throughout the company. If you want people to get involved, try introducing gamification. #dataownership #datagovernance #datavalue #gamification

Because data is an enterprise asset with use cases spanning multiple business domains in most cases, the idea of data ownership is likely to be problematic. Data governance is about responsibility and accountability for data, which is the real deal. To get past possible conflicts about data ownership, a data stewardship framework with clear accountabilities and responsibilities is the way to go.

Define a rigorous mechanism/process both to resolve conflicts and to establish ownership where one exists. Utilize data management pattens and best practices to offer alternative solutions to address conflicts.