Organizations are investing in self-service capabilities to enable more associates to discover and analyze data, which creates new challenges for Data Engineers. Fundamental questions like "where is that dataset?" and "who owns it?" become more difficult to answer. Readily available modern tools enable Data Engineers to troubleshoot technical issues fairly quickly. However, now more than ever, Data Engineers need to be skilled at working collaboratively with other stakeholders, such as data scientists, business analysts, and executives, to address contextual issues promptly. In this blog, we explore common collaboration and communication issues between Producers (users who are curating datasets, such as Data Engineers in other domains) and Consumers (users who are analyzing data and/or building business intelligence solutions) and how to overcome their conflicting priorities and/or misunderstanding of the current state.
On the Producer's front, the tooling stack constantly changes, requiring intensive knowledge management to maximize its full benefits. Also, as the team grows, they are held to more rigorous practices around managing pipelines, including git, built-in testing, and even code reviews. Evolving standards and definitions require the producers to continuously align on effectively defining and maintaining their pipelines' quality. On the Consumer’s front, Data Engineers are expected to fully align with the business problem and context, including the various definitions of the data and what’s important to stakeholders. The common blocker for this is the long lead time to establish requirements and properly address the questions from these stakeholders, which can hinder solution delivery.
Your team can do the following to address issues on both fronts:
As we look to the future, the data and demand for insights will exponentially increase. Data Engineers will continue to face technical and non-technical challenges. The key to staying up to speed with the business is first to enable Data Engineers to interact and collaborate with business stakeholders early on. Second, enable Data Engineers to document and manage their knowledge in the flow of work, so it's both natural and efficient. AlignAI developed a platform that includes industry best practices and company-specific workflows to enable experts to capture their knowledge on how to build and use Data, Analytics, and AI products. Our platform serves as a single source of truth that pulls together resources on topics such as Data Ops and MLOps, empowering teams to collaborate and problem-solve more effectively. Schedule your demo today!