Data-Driven Agility
summary
Claims that projects must raise informedness while cutting decision time by building a data ecosystem that uses historical project data, scenario workshops and reference class forecasting to forecast duration, a practical “project scan” model that shifts teams from reactive risk control to proactive prevention, and lightweight analytics (even PowerBI or Excel) plus premortem exercises. Evidence cited: a 2021 survey finding over 90% name culture as the main barrier. Promised outcome is clearer foresight, faster informed decisions, fewer last‑minute overruns.
AI-written summary · about
chapters (16)
- 0:00Data-driven agility intro
- 1:10Defining data-driven agility
- 2:36Why data skills matter
- 4:03Faster informed decisions
- 7:20Project management lag
- 9:54Control vs agility balance
- 12:25Aligning project management
- 16:59Cost of info flow barriers
- 20:56True agility beyond adaptation
- 23:45Simplified structures culture
- 25:50Project deep scan model
- 28:57Premortem risk management
- 31:07Diversity fuels innovation
- 33:16Data-centricity enhances agility
- 35:45Steps to data-driven practices
- 36:26Q&A: Measuring progress
related talks
43mAgile Strategies for High-Impact Data Science & Machine Learning Projects
44mMeasuring agility: Data analytics ex psychology to grow your teams agility
26mData For The 99%: Unlocking Data Curiosity Across Your Organisation
16mRedefining Agile for Data Science: A Team First Approach
44mPredictably Agile
21mAgile Delivery in an Uncertain Market
01h 04mIs Agile Still the Answer? Lessons from Real World Agility
16mPurposeful Transformation With Data-Driven Decisions