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Music: Morcheeba - Rome Wasn’t Built in a Day (2000)

This week we look at similarities in platform engineering, data operations (DataOps), and revenue operations (RevOps) teams.

Getting Informed

As mentioned previously in Fudge Sunday, an Internal Developer Platform (IDP) can be thought of a uniquely custom stack or toolchain. The group of engineers that support that stack or toolchain are the platform team.

Happy When It Toolchains

This week we take a look at toolchains in platform engineering.

From my perspective, if you compare the goals and stacks of a platform team to the goals and stacks of a go-to-market (GTM) data operations (DataOps) or revenue operations (RevOps) team, the argument can be made that there are a particular set of learnings to be had from interlock. I also believe there are similarities in the advantages to managed alternatives to replace more manually intensive design choices.

In this day and age, it’s so easy to stress 🎶

Last week, Evan Dunn at Syncari asked me a simple question (paraphrased). Evan’s question led to an elongated issue of the newsletter.

Hi Jay. What are your observations on GTM data quality impact for revenue teams – especially with unicorns? – Evan

You could say this question really plucked a string or struck a chord (nerve?) — and so I had to respond — and I did so within seconds.

Evan – oh wow! This might be getting on a bit of a soapbox. The fragility of data stream is what I’ve noticed. For want of a nail… For example, it’s not uncommon for a unicorn to experience turnover in the key team members that created heroic pipelines when a systematic pipeline would be documented sufficiently enough to be survivable.

Jobs are so plentiful that you can’t fault the individual contributor for grabbing at the next brass ring. However, you can fault GTM leadership for not insisting on pipelines that are precariously held together by sheer manual efforts by their under-appreciated RevOps team members.

Result: You chase waterfalls in revenue, mirages of margin, and get distacted… squandering time through losses from opportunity cost that compounds when you are literally sticking a wet finger in the air to gauge GTM signal.

Also, I’ve been reading the newsletter of another Customer Data Automation Community team member, Greg Meyer. You should subscribe right now if my reply struck a chord with you too.

Data Operations | Greg Meyer | Substack

Data Operation is the intersection between sales operations, marketing operations, and customer operations. “Everything Starts Out Looking Like a Toy.”. Click to read Data Operations, by Greg Meyer, a Substack publication with hundreds of readers.

Naturally, I believe my opinionated reply was much faster because I have been reading Greg’s newsletter.

When I read Greg’s newsletter, I cannot help but see patterns in DataOps and RevOps that are closely aligned to the same goals in platform engineering pursuits. Indeed, there are pipelines that a DataOps and RevOps team must maintain for the GTM team just as their are CI/CD pipelines that a platform engineering team must maintain and streamline for maximum software engineering team development comfort and increasing productivity.

Raising a data pipeline or an IDP might require a village. But, does it have to be that way every time or all the time?

I’m having a daydream we are getting somewhere 🎶

Of course, adopting a managed to-be toolchain that replaces the manual as-is toolchain will have implications on the team composition and requires a focus on ruthless removal of annoyances associated with undifferentiated engineering¹. Then again, the adoption of a toolchain might start with one or more parts of a complex and precarious single-point-of-vacation-failure-engineer time intensive pipeline.

Speaking of pipelines that are survivable designs, I do believe that my attempt at a Wardley map last week points to a future where Cloud Service Providers (CSP) will have opinionated stacks that can replace portions of the custom layers within toolchains and, perhaps, those toolchains within an IDP.²

AWS Publishes Reference Architecture and Implementations for Deployment Pipelines

AWS recently released a reference architecture and a set of reference implementations for deployment pipelines. The recommended architectural patterns are based on best practices and lessons collected at Amazon and customer projects.

So, what will be the next big thing in platform engineering, data operations, and revenue operations?

Until then… Place your bets!

Work Plug

As a reminder, after a +25 year walkabout, I’m an IBMer (again). For 2023, in “Work Plug”, I’ll share a new link each week that is educational, accessible, and relevant to platform engineering from fellow IBMers³ in the wider IBM Community.

Also, I’ll be speaking with Rodrigo Flores about Platform Engineering Services and Cloud Cost Optimization on Tuesday 28 February 2023 12pm-1pm EST. You can register for the webinar here:

Disclosure

I am linking to my disclosure.