If you’re an ops guru, you’ve already heard of Agile/Lean methodology in the context of manufacturing and software development.
The core idea behind Agile is simple — speed, learning and adaptability will always win over perfect planning.
And this makes sense. The world is complex and unpredictable, so it’s much safer to navigate through it with lots of small/low-stakes, mistakes rather than betting everything on being exactly right the first time.
How does Agile apply to RevOps?
RevOps teams are constantly making tradeoffs between speed and perfection.
Here’s one of the most common ones we see:
Would I rather A) spend 30 hours doing my funnel analysis on this broken data in a one-time, unscalable way not knowing if I will get low confidence results, or B) spend 300 hours rebuilding the broken data foundation with a better data model, CRM flows and rep training, so that I can automate funnel reporting forever?
Now, if you’re a builder of systems and you hate doing repetitive, manual, error-prone work, you may be thinking option B is the clear choice. After all, this is an investment in the future and once the system is built, you’ll help your team become ‘agile’ by getting the reports they need in real time, right?
Well, that’s true if you know EXACTLY what you’re looking for ahead of time.
And the reality is that we often don’t know:
- That a picklist was the right data structure
- That the picklist options were the right ones
- That the reps understood in training what the options were
- That the reps would update those options as expected.
And that’s the Agile RevOps dilemma.
How do you deliver continuous value on a broken data foundation?
Option C – Agile RevOps Through Data Operations
What most people don’t know is that this problem has already been solved in the manufacturing, logistics and product analytics through a Data Operations framework called Process Mining.
The core idea is simple — because you don’t know what you don’t know, you need an analytics framework that will allow you to do three things:
- Capture large quantities of unstructured data
- Use Data Operations to add structure to this data “on-demand” to support your analytics needs
- Automate any process resulting from the analytics/insight (creating data, structuring data, executing actions from the data)
In this way, Data Operations helps you delay most data-related decisions to a later time where you have more information.
And best of all, this idea is completely compatible with Options A and B. Let’s say that you’re unsure whether or not you can extract the data you need through DataOps at a later time. You can still hedge your bets by having your reps update the CRM in parallel, then later use DataOps to compare the results between human and machine generated data, and finally choose the approach that makes the most sense for the reporting task at hand.
Making This Work For RevOps
Process mining is fairly simple to execute in industrial settings because Ops teams have so much control over the environment — most of the work happens in confined physical spaces (eg: factories, warehouses, containers), you have unlimited tracking capability through technology like smart cameras and RFID chips and almost all work is executed in a linear process (eg: assembly line)
Obviously, none of this works in a sales context — we have limited control over the environment (reps will use whatever it takes to win the deal), we can’t track our people with RFID chips and sales is not at all a linear process.
But the digitization of sales and advancements in AI have finally made process mining a possibility, by allowing us to not ‘follow the rep’ but instead follow the digital breadcrumbs they leave behind. With most sales shifting into digital channels (phone, text, meetings, email, whatsapp, slack, etc), we can capture this data, convert it into machine-generated information and store it in a way that makes process mining possible.
And that’s exactly what Truly makes possible, as the first DataOperations platform for Revenue Teams.