Three general mediator roles suggested by Moore: Social network mediators: They are the respected members of the team and are perceived as being fair. Authoritative mediators: They are in a position of authority and are able to use their authority to enforce agreements. Independent mediators: Help teams to develop mutually acceptable solutions. Two types of tactics used by mediators: General tactics Contingent tactics General tactics include tactics for: Entering dispute Analyzing conflict Planning mediation Identifying interests Negotiating Contingent tactics are used to address: Value clashes Power imbalances Destructive interaction patterns Communication problems Strong emotions Misinformation Differing analyses

As a leader, I would first set the expectation that bad quality data is expected in any type of enterprise. The models should always focus on the business problem we are trying to solve but at the same time, it is always trivial to keep a track of all the quality issues and come up with a road map to enable governance at all levels. Standardization, road map, planning and governance rollout are key to resolving any kind of conflict.

Manage expectations early by doing EDA to understand what your team is working with. Does it meet the necessary standard? Any mitigation plan to prepare if you decide to proceed? Communicate the findings to the dataset owners. This may be a good opportunity for the dataset owners as well to test and improve the quality of their dataset. Identify dataset owners early and involve them in the process (at least keep them informed and consulted). Having a clear data project governance, knowing who is responsible for what and where to raise and solve issues helps manage the team's focus and productivity.

To avoid disputes on data quality standards it is imperative to - Define data quality check dimensions- timeliness, integrity, completeness,accuracy, consistency, relevance, accessibility and serviceability - Agree on data quality profiling strategy and corresponding business rules - The impact analysis should be based on shared and common understanding of the team members so that data cleansing and remediation can follow - The data quality cost vs benefits should be quantified for buy in by all team members and business - Agree on monitoring of KPI s and selected thresholds - Ensure proper roles are defined amongst team members such as data governance lead, data steward, data custodian, data domain manager, data user

Its all about how we are setting up the OKR, it will change the perception of measuring the quality of Data.. So Ensuring and reiterating the target in every stand up will make the difference in perceptions of measuring quality and subsequently changing the raw data requirement.. Hope this helps..!

To effectively resolve disputes over data quality and maintain collaboration, the following strategies can be applied using agile methodologies without compromising the roles of each team member: 1. Encourage cross-functional collaboration 2. Define and use clear metrics 3. Create a clear escalation process 4. Encourage shared responsibility 5. Document and align standards By integrating agile methodologies, communication, collaborative focus, and continuous improvement in data quality management can be enhanced while respecting the roles of each team member.

Its important to establish the meaning of data quality. When that is defined, lwt us test the data to see if it fits the standards. Deviation may mean the modelling is not done properly.

Mediate data quality conflicts by: 1. Identifying root causes via open dialogue (e.g., misaligned priorities, unclear standards). 2. Refocusing teams on shared business goals (e.g., decision accuracy). 3. Using neutral frameworks (e.g., DAMA-DMBOK) to depersonalize debates. 4. Piloting competing standards on small projects; let data-driven outcomes resolve disputes. 5. Clarifying decision roles/escalation paths to avoid gridlock. 6. Bridging knowledge gaps with cross-team training. 7. Modeling calm, collaborative behavior to maintain trust. Follow-up: Document agreements, track adherence, and celebrate collaborative wins. Balances rigor with empathy to uphold quality and morale.

Effective strategies include: 1. Common Definitions: Agree on key metrics like accuracy and consistency. 2. Open Discussions: Encourage team input through meetings or workshops. 3. Review Process: Regularly update standards to fit project needs. 4. Quality Tools: Use software for objective data checks. 5. Role Clarity: Define responsibilities to avoid overlaps. 6. Training: Educate the team on best practices. 7. Pilot Projects: Test standards on a small scale first. 8. Third-Party Mediation: Involve neutral mediators if needed. 9. Documentation: Keep records for transparency. 10. Celebrate Success: Acknowledge achievements to boost morale. These steps promote effective dispute resolution and maintain team cohesion.

To mediate the dispute, I would facilitate open communication between the teams, identify shared goals, and collaboratively define clear, measurable data quality standards. By encouraging compromise, prioritizing critical issues, and documenting agreements, I would ensure alignment and foster a cooperative approach to resolving the conflict. Regular check-ins would help maintain progress and address future concerns.