What do you do if conflicts arise between data architects and data analysts in Data Warehousing projects?

Data warehouse is built for Business Users and Data Analyst - architecture is backbone. If there is a conflict , architecture should be modified to suit business and analyst. Modifying / refactoring an architecture is easier than asking business to do it differently. However, presently them an alternate path ( with reasons) also helps

One of the best ways to identify potential conflicts between Analysts and Architects is to maintain a work item backlog. Normally, conflicts arise due to different sets of priorities at different levels. Having a transparent backlog makes it a lot easier to identify the actual work items that could potentially lead to risks and conflicts that would need to be mitigated in the future between analysts and architects.

Get them into a virtual or actual room and facilitate the discussion on the agreement of the problem statement of both or one of the parties. You will very likely find that the conflict is rooted in the prioritisation of the roles. Your Data Architects are likely interpreting specifications /need and having to consider Enterprise standards, security standards, Audit standards (E.g. ISOXXX) data normalisation , transmission latency, ability to backup and archive without compromising modern data retention policies and the list goes on... Your Data Analysts have entirely different focus areas and needs and interpret the specification / need in an entirely different manner. Share perspective, Share knowledge and aim for a win-win compromise

It is important to identify the root cause of such conflict. A Data Architect is responsible for designing a robust, comprehensive and flexible Datawarehouse architecture that can cater to current and future business needs for insights. However, if it is poorly designed, a Data Analyst may struggle to slice and dice with the data and generate desired insights. It important in such situations to take a holistic look at the Data architecture, identify missing objects, relationships and if needed tweak / partially redesign the architecture to fulfill the Data analyst requirements.

Em um impasse entre arquitetos de dados e analistas de dados prevalece sempre o que esta refletido no modelo de negócios. O Modelo de dados mais adequado será o modelo de dados que melhor suporte os processos de negócios da empresa.

Actively listen to each party's viewpoints to demonstrate empathy and understanding. Clarify roles, responsibilities, and expectations to ensure that both parties have a clear understanding of their respective contributions to the data warehousing project.

Communication is the key. 'Listen' to both the sides and then take action. Mutual agreement and cooperation is very important for any project and it can be achieved by communicating with each other.

Offener Dialog, ja. Aber ich würde auch "tiefer gehen" und nach den Gründen bzw. Ursachen bzw. Entstehungsgeschichte für den Konflikt suchen und danach entscheiden, was angebracht bzw. sinnvoll ist.

Foster open dialogue between data architects and analysts post-issue identification. Encourage expressing concerns openly, facilitated by a neutral party if feasible. Aim for a collaborative environment where roles and contributions are understood. Mutual respect and clear communication enhance innovation and team cohesion.

Having regular cadence between different parties often facilitates meaningful conversations. Everyone should have a say at the meetings and put forward a point of view in as assertive manner as possible. Needless to say, there is no single way to achieve a solution to any problem. Having a good discussion in the meetings paves the way for a much stronger team and tackle issues in a constructive fashion.

Recognize and acknowledge the contributions of both data architects and data analysts towards the attainment of shared objectives.

Aligning objectives between data architects and analysts is pivotal for conflict mitigation. Revisit project goals, ensuring clarity and shared understanding. A shared vision document can clarify roles in the data warehousing project. This alignment fosters a cohesive team, ensuring everyone works towards common objectives, understanding their contribution to the project's success.

Conflicts are good in a way to welcome different viewpoints to the table. By facilitating a constructive discussion among data architects and data analysts, we could identify the real risks and explore on the mitigation plans. It is important to enforce open communication and offering an opportunities to provide possible solutions to the agreed problem statements in order to accomplish the objectives of ‘one team, one goal’.

As a Data Architect, you need to condition your Data Analysts to think like database engineers and business logic experts. Meet with them regularly. Co-review analytic reviews and show/teach them how to think on both perspectives of data definition and business definition of everything they analyze. This is not a one time process. This is an ongoing process forever.

Expend some time , effort and money on the team looking for a viable technical or procedural win-win solution to the issue. Affording them to work together to see each others issue manifest will allow for understanding , if not acceptance, and pave the way for a compromise in both competencies to move forward.

Monitor progress towards resolution and address any new challenges or concerns that arise along the way. Flexibility and adaptability are key to ensuring that compromise solutions remain viable and sustainable over time.

Compromise is such a subjective term. Here is the objective headline: Never compromise on data integrity. Everything else is negotiable. If the data can be extracted/loaded without compromise. Everything else is negotiable. Get the data, then decide how to handle it. As long as the business signs off on how its handled from there, everything is great.

Monitor the progress of the implementation closely to ensure that the changes are being executed according to the agreed-upon plan.

To resolve conflicts between data architects and data analysts in data warehouse projects: 1. Encourage open communication. 2. Identify underlying issues. 3. Mediate and find common ground. 4. Clarify roles and responsibilities. 5. Foster collaboration. 6. Seek compromises. 7. Involve project stakeholders if needed. 8. Establish a conflict resolution process. 9. Provide ongoing support.

Both are critical roles in any data project. A conflict simply means a difference in opinions which arises when the priority and goals differ between the teams. In order to avoid this situation, it is utmost important that you as a leader constantly align different stakeholders on a common goal that is aligned to the business. Even though there are multiple ways of doing things irrespective of ideas where it comes from, there is only one best solution which serves the purpose of company goals. Having defined responsibilities of each team under supervision of controlling authority (Chosen SME as members responsible for excellence) can significantly have a positive impact and help in remediation of any conflicts.