Data engineers and data governance personnel have trouble communicating. Are you contributing to the problem?

In my opinion, communication problems are not restricted to functions, but there are some ways to mitigate the lack of it, such as: Ensure that both data engineers and data governance personnel understand and share common objectives, Define and communicate clearly the roles and responsibilities of each team, Encourage cross-training sessions where data engineers learn about the principles and importance of data governance and vice versa, Document data management processes, workflows, and policies in a way that be accessible to both teams, Create an environment where both teams feel comfortable providing feedback to each other, and Promote a culture of collaboration and mutual respect within the organization.

The main point of communication is understanding. We should engage in open discussions to understand each other's priorities and constraints. Both roles data engineer and data governance personnel need to consider Efficiency vs. Data Quality; Innovation vs. Stability and Flexibility vs. Governance Policies. Finding a balance between efficiency and quality is crucial. Both teams need to be willing to compromise and understand that achieving perfect efficiency without compromising data quality is often challenging.

There can be multiple factors that can contribute to this communication gap. Some ex below: Different expertise and focus: Data engineers focus on technical aspects, while data governance personnel focus on policies and compliance. Lack of common language: Different terminology and jargon can create confusion and miscommunication. Different priorities and goals: Conflicting approaches due to prioritizing efficiency vs. data integrity and compliance. Limited collaboration and coordination: Separate teams or departments hinder regular collaboration and knowledge sharing. Lack of awareness and understanding: Limited knowledge of each other's roles and responsibilities hinders effective communication

The key aspect of effective communication is mutual understanding. Open discussions are essential to grasp each other's priorities and constraints. Both data engineers and data governance personnel must weigh Efficiency vs. Data Quality, Innovation vs. Stability, and Flexibility vs. Governance Policies. Striking a balance between efficiency and quality is paramount, requiring compromise and recognition that achieving perfect efficiency without compromising data quality can be challenging.

Clearing up misunderstandings, establishing common goals, and promoting open dialogue can bridge the communication gap. By fostering a collaborative environment where both parties feel heard and valued, I aim to contribute positively to resolving any communication challenges and driving the project forward effectively.

From my perspective, a Senior Data Engineer must have strong knowledge of Data Governance. So I think the way to bridge the gap between both roles is the ability to learn from each other. Data Engineers can understand more about data policies, how to protect data, standards, data catalog, and data permission. Data Governance personnel can understand more about data-driven, data pipelines and platforms. Understanding each other will improve our knowledge, help us to build an efficient platform, and secure the data platform.

In my view, a Senior Data Engineer's proficiency in Data Governance is crucial. Bridging the gap between both roles entails a mutual exchange of knowledge. Data Engineers can gain insights into data policies, data protection methods, standards, data catalogs, and permissions from Data Governance personnel. Conversely, Data Governance personnel can enhance their understanding of data-driven processes, data pipelines, and platforms from Data Engineers. This reciprocal learning fosters improved knowledge and collaboration, facilitating the development of efficient and secure data platforms.

To bridge the communication gap between data engineers and data governance personnel: 1. Hold regular discussions. 2. Provide mutual learning opportunities. 3. Involve both teams early in projects. 4. Focus on shared objectives. 5. Set up structured communication channels. 6. Blend skills in joint teams. 7. Exchange insights regularly. 8. Use intermediaries to smooth interactions. This approach enhances understanding and cooperation, streamlining both data management and governance.

Even with the provided strategies, challenges like data silos, security, and data changes may still arise when collaborating with data engineers or governance personnel. To address these, consider employing centralized data storage, integration tools, encryption, data masking, versioning, and testing tools to ensure smoother collaboration and data management processes.

A lot of time data engineering companies simply don't pay any attention to data compliance and governance guidelines such GDPR or HIPPA to get the job done for as easily, quickly and for as less monitory requirement as possible. This should be avoided as much possible.

Common challenges include: 1.Misunderstandings due to insufficient dialogue. 2.Different focuses on functionality vs. compliance. 3. Each side lacks insight into the other’s constraints. 4. Unwillingness to adjust processes for integration. 5. Absence of shared platforms for effective teamwork. 6. Isolation that prevents cooperation and understanding.

To bridge gaps between data engineers and governance personnel: 1. Ensure ongoing dialogue to address misunderstandings. 2. Align on objectives that benefit both parties. 3. Collaborate from the start of projects. 4. Educate each team on the other's domain. 5. Implement shared platforms for better teamwork. 6. Combine expertise in project teams. 7. Establish a loop for improvement. 8. Have individuals bridge understanding between teams. 9. Keep clear records of policies and practices. 10. Acknowledge successes to encourage collaboration.

A lot of time(not always) it happens because Data Governance Personnel are simply not competent. Not because they don't want to do their job but because they simply are not right person for the job. They act bureaucratic and authoritative a lot of time without actually understanding anything about the problem at hand which is frustrating more often than not for both data product and engineering teams. The Data Engineers are a highly specialized people and have some level of good logical ability to be able able to their jobs, which often is not true of Data Personnels and hence data personnels act and have unrealistic requests for the timeline. Its important for them to have some domain knowledge & technical feel for smooth implementation.