That’s right; I’m an aggregator now. Starting from today, I’ll share a selection of the most interesting content (mostly blog posts) about the data industry from the past month, as well as a recap of what I wrote.
What other folks wrote
In order of publication:
Katie Bauer: Elbows of data. How Data teams (and individuals) left on the sidelines of their businesses can force their way into making an impact.
Chad Isenberg: Data Teams as Support Teams. “Data teams tend to have weak operating models supporting questionable value propositions.” Here’s a post about how Data teams can become more efficient and effective at servicing data requests from their colleagues.
Robert Janezic: Is Tableau dead? As little patience as I have for the 🧵 emoji, this is a concise example of how Salesforce kills everything good that it touches, and why a lack of development is causing Tableau to feel increasingly like legacy software, with long-standing bugs that never get fixed.
Prukalpa Sankar: The future of the modern data stack in 2023. The Atlan co-founder makes some predictions about the state of data tooling in 2023, most of which are driven by a scarcity of cash (cloud cost cutting, data teams being measured on ROI, market consolidation, and the integration of the modern data stack with legacy platforms).
Mikkel Dengsøe: Data teams as a % of workforce. I’m not sure I agree with the author’s inference that the data he presents demonstrates the right ratio of data to non-data roles in a start-up/scale-up. All it demonstrates to me is the actual ratio, and I can’t be sure that all those teams need to be the size that they are. Regardless of my opinion on that topic, it’s an interesting data set, the cluster analysis is insightful, and it’s a worthwhile read.
David Jayatillake: Product Market Misfit. The story of data’s unfulfilled promises, broken operating models, and lack of exec-level representation.
Benn Stancil: The rapture and the reckoning. Is it time to throw away the teachings of the old masters of data modelling, and start building data models that are designed to be consumed by AI rather than humans?
Adam Roderick: The search for true value from data. On a similar theme to David Jayatillake’s post above, the author overlays Data (as a business function) with Gartner’s Hype Cycle.
What I wrote
The unhappy marriage of data stacks, modernity, and capital letters looks at how a bunch of SaaS vendors managed to plant a meaningless, hyperbolic name into the minds of data practitioners everywhere, without anybody seemingly batting an eyelid.
And finally a couple of SQL-related posts from that other site that I don’t talk about:
Happy reading!