Macroeconomics and the Data Industry — VC Effects and Value Scrutiny

Tristan Handy on how macroeconomic forces (specifically VC funding cycles) shape the data industry, and a call for data teams to prove their value.

VC money effects on an industry

Innovation in tools and practitioner workflows happen in symbiosis — more like an adversarial neural net where apps and infrastructure compete to make better versions of each other.

Downsides of VC money in your industry:

• Froth — rate of change and hype make it hard to track what's real vs. what vanishes in 2 years
• Brain drain — talented practitioners migrate to starting or working at tooling companies
• Culture change — startup/VC culture becomes interwoven into the practitioner community

The value scrutiny call

"There is too little scrutiny of the value of data teams today." Handy encounters data professionals who:

• Don't understand how their work maps to business priorities
• Measure output as ticket completion or model creation, not business problems solved
• Spend major chunks of time blocked on upstream dependencies

"You should want your company to care tremendously about the value you create."

Practical advice

• Know where your spend goes across the entire data stack
• Kill unused scheduled dashboard refreshes, dbt models, and Fivetran replications
• Install lineage to know what's used and what isn't

The "internal journalism" opportunity

There may be a whole set of undone activities: storytelling, culture formation, "internal journalism" — data teams positioned as the organization's sense-making function.

Connects to [[data team operations]], [[analytics craft]], [[sharing data insights]].

Open questions

• What does "internal journalism" look like as a data team function?
• How do you measure data team value without falling into the ROI self-reporting trap?