Coaching for Data, Analytics, AI Leaders (11–20)
Principles and patterns to build winning performance+team
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If you are leading teams responsible for Data, Analytics, Data Science, or AI — this blog series is for you! Throughout my career leading teams in Data Engineering, Data Analytics/Science, and AI in both startup and large company environments, I have uncovered principles and patterns that have proven to be effective. In this series, I will be sharing these principles and patterns.
#11 Establish clear Service Level Agreements (SLAs) for data
For business-critical metrics/dashboards/models, ensure there are clear Service Level Agreements (SLAs) established with source data teams. SLAs need to include refresh lags, quality checks, incident response times.
#12 Establish a clear promotion process to solidify insights and models
Metrics and dashboards typically begin as prototypes, but eventually, the business begins to use them and rely on them for decision-making. To ensure accuracy and consistency, it is essential to establish a clear promotion process that enables insights to be solidified from prototypes to production before the business begins relying on them. Additionally, this promotion process can drive the standardization of metric definitions and eliminate multiple prototypes for the same metric.
#13 Empower teams to judge urgency of partner requests
Data analysts and scientists collaborate with various tech and non-tech partners who often have a sense of urgency in their requests. To ensure a healthy work-life balance, it is important to empower your analytics and data science teams to use their judgment on the urgency of these requests. For instance, they can assess whether a specific task truly requires burning the midnight oil or working on weekends.
#14 Build strong partnership between Analysts and Data Stewards
