RevOps Framework Deep Dive: Data Foundation

Recently, we published the Hyperscayle RevOps Framework. Hyperscayle defines revenue operations as the design and execution of Go-To-Market (GTM) processes and systems across the lead-to-cash lifecycle.   

To us, this includes marketing operations, sales operations, customer success operations, channel operations, and finance operations. Ours is a holistic view, not thinking in terms of silos bolted together, but rather as steps of a unified process. We created the Hyperscayle RevOps framework to further structure our approach. 

The categories of our framework include leadership alignment, process definition, team structure, systems & tools, and data foundation. These five categories cover all facets of a holistic RevOps program, and Hyperscayle uses this framework to organize our efforts and prioritize the right things for our clients.  

Each of these categories is a weighty topic, so this article is a deep dive into the RevOps data foundation. 

Define Data Tables and Data Objects

What does your data mean to you?

One of the most important aspects of a solid data foundation and data strategy is often one of the most overlooked. As a first step, we always ask our clients to define the key data tables they are working with, but most struggle to go beyond the simple CRM definitions provided out of the box.

Try thinking through questions like this:

• Does your account database only represent companies that have a technology-decision maker that you can sell to?

• Do opportunities align directly with a signed contract or do you sometimes have multiple opportunities per contract signature?

These questions may sound super specific, but it’s important to define exactly what these data tables represent to put the right processes in place to maintain your data.

Take the time (early on if possible) to document specific definitions for all of your main data objects as they relate to your unique go-to-market business process. For example, when building out a duplicate management process and account hierarchy, it is important to fully understand the definition of an account in your database. If this has already been documented and understood, this project will be a breeze.

Build Your Foundation First

Defining, cleaning, and managing your data is NOT the most glamorous work. It doesn’t drive much immediate business value, so it’s easy to understand why it gets de-prioritized for other projects.

Management keeps kicking the can on your data foundation work; while pushing you to complete what they view as more impactful projects.

In reality, when organizations keep postponing data foundation work, they cost the company money. That’s because every single piece of functionality that you’ll build as you implement new tools needs to use your data to work. And if you don’t have a well-built foundation, that functionality is going to be more complex, take more time, and be less reliable. This is becoming even more important with time, as companies start to use AI tools on top of their internal data. Any AI tool is only as good as the data it’s being trained with.

For example, you want to implement a better lead routing mechanism that will get leads to the right rep quicker and convert to revenue at a higher percentage. You invest the time into the automation that will automatically send the leads to the right rep and everything works as expected. But when you push the functionality live, it doesn’t work as well as it did in your controlled test environment.

The logic is the same, but since your production data for leads and accounts is messy and inconsistent, it impacts the results of your perfectly built automation. So your management team is upset about the inconsistent functionality after they repeatedly de-prioritized the foundation work. Always push for FOUNDATION FIRST!

Make Data Capture Easy

There’s a calculation that every RevOps team needs to make about how much data you want to capture. Most assume that the answer is easy; “I want to capture ALL the data,” but even “free” data comes at a price. For every data point that you want to capture, there’s a click/action that you need to ask your end users to complete.

If you want any consistency in that data, you’ll need to install gates into your end users’ process to make sure they are inputting the right data. This all leads to frustrated end-users who feel like most of their jobs are administration and data entry.

Frustrated users don’t input good, accurate data.

To avoid this, always make sure that if you decide to add an additional click to your end-user process, you must have an actionable plan to use that data. With this plan defined, you can help justify the additional ask to your business stakeholders. Don’t be afraid to hold your users accountable from a data management perspective, but also, be sure to respect their time and justify every ask.

The Bottom Line on RevOps Data Foundations

In summary, it’s referred to as a data foundation for a reason. When you invest time and energy into building a well-defined, structured foundation, it makes everything easier, cheaper, and more impactful down the line.