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The TL;DR from my session on AI and EngOps at AIDe...

This morning I had the opportunity to chat with software engineers and data scientists at the AI Dev World Conference on a topic I just happen to be v...

The Rise of Engineering Operations

Xerox is credited with building one of the first sales operations groups. Its leader, J. Patrick Kelly, said his responsibilities consisted of “all the nasty number things that you don’t want to do, but need to do to make a great sales force.”

Sales departments were early to the data game. Around the core business of closing revenue is an enormous amount of analytic work to understand what the numbers are saying—about forecast probability, market conditions, spend efficiency, team performance, and so on. To work in sales ops you need an excellent analytic brain, to be comfortable presenting to senior leadership, all while helping, supporting and enabling one of the most pressurized species on earth: the salesperson.

It shouldn’t take much squinting to see why a similar role has begun to materialize in software organizations. What do we have in engineering?

  • Many moving parts, generating vast quantities of data needing synthesis: work dependencies, sprint results, resource constraints, issue changes, priority shifts, etc. etc.
  • A centrality to any business operation. A company that doesn’t need or care about software is like a company that doesn’t need or care about sales: it’s either a monopoly, or headed for extinction.
  • Our own pressurized species needing support and enablement—the engineer.

Is this role—call it “engineering operations”—on the rise for software organizations? Yes. Anecdotally, every customer we work with has at least one person who fulfills this function. The job might be variously titled “program manager,” “team coach,” “process architect,” etc. I know of at least one company that has hired data scientists to help make sense of engineering data. These are all people who’ve been tasked with helping engineering become a smarter, more data-driven function.

And there’s hard data that shows the rise of engineering ops as well. In our survey of engineering leaders, more than 20% reported having at least one person dedicated to helping track and manage engineering performance.

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In talking with people in engineering operations roles, there appear to be a few broad, early use cases that act as catalysts for hiring engineering ops staff:

These are natural starting points. The truest realization of engineering operations harnesses data across virtually all engineering activity (and systems), providing diagnostics and recommendations for executives, managers, and engineers alike. It looks very much like sabermetrics for software engineering.

Sales operations was a precursor to an entirely new category, what came to be called Customer Relationship Management. But long before CRM was a well-known acronym, there were people in every sales department doing the heavy analytic work that systems like Salesforce would eventually automate. It seems clear something similar is happening across engineering organizations. The rise of engineering ops suggests we’re at the dawn of something at least as far-reaching as CRM—call it Engineering Performance Management.

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