What do you do if your data architecture team struggles to collaborate effectively?

Gather Feedback: Utilize various methods like surveys, interviews, workshops, or focus groups to gain insights from team members and stakeholders. Ask Probing Questions: Focus on understanding project goals, challenges, roles, communication channels, data architecture lifecycle management, and performance measurement processes. You can conduct interviews with developers and business analysts to understand: Are project goals (data sources, target format) clearly defined and communicated? Do developers and analysts have a clear understanding of each other's roles and responsibilities? Are there established communication channels for sharing design changes and updates?

Identify Root Causes: Assess the current situation to determine the underlying reasons for collaboration struggles. Gather Feedback: Use methods like surveys, interviews, and focus groups to collect insights from team members and relevant stakeholders. Define Goals and Challenges: Understand the main goals and challenges of the data architecture project to align efforts effectively. Clarify Roles and Responsibilities: Ensure roles and responsibilities within the team are clearly defined and communicated. Evaluate Communication and Processes: Review communication effectiveness, information sharing, tools, and processes used in managing the data architecture lifecycle.

Improving collaboration with teams starts with understanding the root causes. Conducting surveys, drop-in sessions, and interviews can help. Typically, the issues can be categorized into three groups: 1) No proper communication channel 2) A communication channel exists, but it is not seen as useful, and 3) Both issues are present. After gathering the necessary details, develop a strategy based on the identified problems. If the first issue is identified, work on defining and establishing a communication channel that is accessible to all. If the second issue is identified, emphasize the importance of proper architecture and publicize its benefits. If both issues are identified, combine the strategies for the first and second problems.

Start with a simple root cause analysis to identify the specific issues your team is facing. Are there communication breakdowns, unclear roles or conflicting priorities? Does your tooling enable a collaborative environment to actually work? Talk to team members individually to get their perspectives on what is going wrong. Look at how work is being done and where the bottlenecks are. Consult, consult, consult. This will give you a clear picture of the underlying causes of the collaboration problems. Understanding these issues is the first step to figuring out how to fix them.

Specific: Define quantifiable goals (e.g., % data model alignment, data reduction %). Measurable: Track progress with KPIs (e.g., user surveys, development times). Achievable: Set realistic targets and break down large goals. Relevant: Align with business goals and user needs. Time-bound: Set deadlines and review progress regularly.

When your data architecture team struggles with collaboration, it is crucial to define desired outcomes with a focus on pragmatic and realistic goals. Move away from ivory tower architecture and embrace the idea that perfection is the enemy of good. Aim to establish a 'good enough' foundation that enables higher quality architecture over time. Architectural goals will always evolve, which can be demotivating, so focus on developing agile and modular architectures. This approach allows your team to adapt and improve continuously without getting stuck chasing perfection.

Communication: Regular meetings, transparent platforms, clear protocols. Collaboration Tools: Version control, data sharing platforms, project management software. Agile Methodologies: Break down projects, encourage feedback, utilize CI/CD. Data Governance: Standardized definitions, data quality management, security protocols. Recognition & Rewards: Acknowledge contributions, celebrate milestones, incentivize performance.

If your team is struggling to collaborate, implementing best practices can make a big difference, but best practice should always be aligned to your team and their specific circumstances. Start by evaluating the tools you use for creating data architecture. Ensure they facilitate collaboration and integrate seamlessly with other tooling. Streamline communication channels to keep everyone on the same page. Documentation is important, but it should not be a burden. Keep it concise and maintainable. Adopt modular thinking and agile methodologies to stay flexible and responsive to change.

Data Collection Methods: Utilize dashboards, reports, surveys, and reviews to gather feedback and assess progress towards collaboration goals. Evaluation Questions: Are collaboration objectives being met? Are the implemented tools and practices beneficial? Is the team satisfied with the collaboration process? What areas need improvement? Continuous Improvement: Based on the collected data, identify areas for improvement and refine your collaboration strategies for future success.

Periodically revisiting the proposed approach to streamline and enhance collaboration is important. What works today may not work indefinitely. Also, it is beneficial to gather feedback and conduct surveys to obtain firsthand information about whether individuals or teams face issues when collaborating or not receiving appropriate responses. Team alignment and structure change over time, and new teams or vendors are often onboarded, making it crucial to understand how robust your collaboration framework is. Analysis on the feedback can be a valuable tool for improving the framework and making it more accessible to everyone. If there is a defined SLA for resolving queries, it is essential to evaluate whether the SLAs are being met properly.

1. Set Clear Metrics: Define specific, measurable goals using the SMART framework. Track alignment, data redundancy, accessibility and development efficiency. 2. Regular Check-ins: Hold regular team meetings to discuss progress and any obstacles. This keeps everyone aligned and accountable. 3. Feedback Loops: Encourage continuous feedback from team members to identify issues early and adjust strategies as needed. 4. Use Tools: Implement project management and collaboration tools that provide visibility into progress and performance. 5. Review and Adjust: Periodically review goals and metrics. Be prepared to adjust them based on feedback and changing priorities.

Often lack of collaboration is due to differences in priorities or awareness of what others are doing. I find it very helpful to have visuals to help make sure everything is on the same page to align priorities. Something as simple as creating a high-level plan that clearly articulates goals and objectives, timelines, expected outputs, and roles and responsibilities. With this teams understand what to deliver, by when and their role to deliver the necessary outputs. A 3-4 slide brief can go a long way to align team members and get them moving in the same direction.

If your team is struggling to work together, you should also consider following points... Communication: Implement structured communication platforms such as Slack and regular meetings e.g. idea brainstorming to ensure a clear and consistent flow of information. Cross-functional training: Encourage training that covers different aspects of data architecture and data engineering so that team members better understand other's roles. Business objectives: Ensure that the team's goals are directly linked to the overall business goals. This increases motivation and emphasize the importance of collaboration.

To mitigate collaboration / working challenges, I would recommend developing a shared understanding on ways of working - a team charter might be a useful step to define a shared and agreed understanding. A team charter will help the team form and discuss how we show up everyday, what to do if we need to ask for help, how we work together, how is our work linked to business strategy. I would also encourage learning sessions between team members, to drive better collaboration and knowledge sharing across data architecture/engineering practices.