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Prioritization for Data Work

Prioritization in data work is the selection of tasks that are both innovative and impactful. Adopting a scientific framework, including first principles approach and second order thinking, can help with breaking down problems and understanding their complexities. Consistency and documenting evidence enhances prioritization methodologies over time. Avoid focusing only on reporting or engaging in irrelevant tasks for optimal results.

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    The Practica AI Coach helps you improve in Prioritization for Data Work by using your current work challenges as opportunities to improve. The AI Coach will ask you questions, instruct you on concepts and tactics, and give you feedback as you make progress.

  2. Curated Learning Resources

    • Fanatics logo
      Prioritizing Data Science Work

      Jacqueline Nolis, Principal Data Scientist, Fanatics

      As a data scientist, you are constantly deciding what tasks to prioritize. There are many requests from stakeholders but not all have the same impact or innovativeness. Jacqueline recommends prioritizing projects that are both innovative and impactful as they have the greatest potential to change the business. Projects that are not innovative but still provide useful proof can also be valuable. Jacqueline advises against getting stuck doing interesting but irrelevant work or only reporting, as these contribute less to the company. Data scientists should aim to do work that both affects the company and is innovative.
    • Delivery Hero logo
      Prioritising the Scientific Way

      Shyam Sundar Dhanabalan, Director of Data, Analytics & Strategy, Delivery Hero

      Shyam proposes a scientific framework for prioritization consisting of a first principles approach and second order thinking. The first principles approach involves breaking down problems into fundamental components to remove biases. Second order thinking considers the consequences of consequences to uncover hidden impacts and complexities. Shyam outlines four types of complexities - structural, technical, temporal and directional. Complications are distinguished from complexities. A scientific prioritization process involves documenting evidence to improve the methodology over time. Consistency is key to allow positive effects to compound, and documentation helps transfer Accountability to the process rather than individuals.