How Data Teams Can Eliminate Blind Spots and Improve Productivity

The blog discusses the challenges faced by data analytics and AI teams in realizing the full value of their enterprise data. Despite the availability of tools, technical solutions, and innovations, there are still operational obstacles and gaps in data coverage that impede progress. These blind spots include issues related to accessing the right data sets, defining key performance indicators (KPIs), conducting experiments, creating common dashboards, deploying models, and establishing analysis patterns. The blog emphasizes the importance of a holistic approach involving processes, mindsets, team design, and data maturity to overcome these blind spots. It also highlights various issues and concerns that hinder productivity for technical teams, such as inadequate documentation, complex data pipelines, improper understanding of data, uncorrected source changes, assumptions about roles and responsibilities, and misinterpretation of insights. The blog suggests addressing these productivity blind spots by fostering a culture of comprehensive data documentation, treating data as code, establishing clear roles and responsibilities, promoting literacy and shared understanding, and prioritizing effective team design.