A blog about data, tools, and operating collaborative high performance cultures.
Exploring the recent buzz around "data apps", defining what data apps are, can be, and why we are excited about this future.
A post that talks about the power of segmentation, and how a business semantic graph helps collaboration between data and business teams.
This post outlines key evolutionary steps for a strong analytics practice. Machine learning is not always the pinnacle state for a data-centric culture - there is a lot of ROI in nuts and bolts analytics work.
A followup post diving deeper into the business semantic graph example and showcasing informational and operational use cases.
Inspired by the discussion on data contracts, we take a look at standardizing outputs for evolving use cases like A/B experiments, and segment contribution and impact analysis.
We take a look at an example representation of business semantics visualized via a graph, and explore its powerful implications.
A post about how we build narratives from data, taking the US mid-term elections as an illustrative example.
Learning and Unlearning - startup phase transitions are hard, surprisingly common and rarely discussed in public. But this post dives in.
A succinct listicle - just three key principles to operating a modern organization
Examining the tension between centrally governed data and flexibility for consumers
Examining the ascendance of product metrics and arguing for a course-correction for data and tech teams.
Categorizing the three types of analysis all orgs need to execute consistently
Taking chatGPT for a quick spin to see if it can role play as a data analyst!
Thoughts on wny dashboards are the go-to mechanism for organizations vs self-service workflows
A short post about data being elevated to a first class citizen but still lacking the cultural reach.
Some reflections on how crazy recent the cloud data platforms still are, the first wave of tooling on top, and what's needed next.
Some thoughts on data, ROI from investing in data teams and assets, scrutiny of data teams etc.
The engineering forces taking over the data domain and the good and the bad of it all.
It's time we embraced stronger modeling in the modern data stack. This post showcases the power of applications we can build on business semantics with a simple example.
How tools evolve and in particular, how the "dashboard"/"report" in the data domain is woefully inadequate for modern data-centric teams.
This article covers why this feeling exists, why it's growing among data-savvy operators, and what's the way forward. tl;dr: sql isn't the core problem or the solution.
