From Data Confusion to Data Intelligence
summary
Claims that executive hope for data vastly outpaces organizational support, and the fix is pragmatic: show don't tell, take the data and run, iterate to nail the landing. Concrete moves: build a skinny dashboard from one‑off extracts, run simulations of a microtransit algorithm, turn that into a sales demo that actually lands deals. Tech and hygiene: set up Posit Connect/Posit server, curated IML‑ready datasets, APIs and data pipelines, dbt and Tableau consolidation. Measured pain points include a 750,000‑cell export limit and two years to secure enterprise access.
AI-written summary · about
chapters (13)
- 0:00Introduction and context
- 1:10The gap in data science
- 5:30Key organizational needs
- 7:52Guerilla data science tactics
- 11:07Case study: Transit software
- 14:41Building sustainable foundations
- 16:55Overcoming data silos
- 20:00Knowledge architecture framework
- 23:58Data governance and literacy
- 28:32Creating sustainable pipelines
- 33:35Analytical architecture overview
- 37:22Conclusion and key takeaways
- 38:28Q&A session
related talks
01h 07mEmbracing The Data Leadership Nexus to Achieve Strategic Success
24mSecuring Executive Buy-In for Data Strategies
46mData-Driven Transformation: Organizational Changes and Leadership Insights
12mThe Analytical Imperative
44mPeople, Technology, Culture: How to make Data Science work
29mBuilding successful data teams in the realities of 2023
45mData Science 2.0: From Outputs to Outcomes
32mLeading with Data: Crafting an Effective Strategy for 2023 and Beyond