So much data, so few answers: How to fix data chaos in RevOps

We all have a shared recurring experience (more like a nightmare) of getting an urgent message from our CRO or CFO while they’re in critical meetings, expecting an immediate answer. For many of these questions, such as “what are the Outbound Sales Development reps’ monthly quota” or “how much pipeline has been created quarter to date,” it's as easy as refreshing a report in Salesforce or Looker or referring to a document where we capture and maintain this type of data. But if the question is “how did all reps on ramp this fiscal year perform against the ramped quota by month” or “how did attrition for NA Enterprise impact quota on the street in Q3,” the sweat would start pouring out of our foreheads. 

How do you respond when you can’t answer the question or answer it immediately? How do you add this to the other dozen things that are active priorities and keep them all on track? In those cases, the back and forth would usually look like the below:

But why is this so hard?

Governance plan and data dictionary 

If the data doesn’t exist or you never decided to track it, there is not much you can do retroactively. But even in cases where you do have some data, the challenge has to do with the data chaos that exists in companies. Many mistakenly think that just adding an Analytics Team, a couple of RevOps Analysts, shoving a bunch of data into a database and deploying a data visual analytics platform will fix their data issues. The problem is that that doesn’t address the root cause. 

You have to make a deliberate decision to capture and track the data.  Unfortunately, even that is likely not enough. This is often due to a lack of a data governance plan and a data and KPI definitions dictionary. This is what defines the KPIs you want to track and measure. It specifies the data needed for those KPIs and the single source of truth for each data element. It also drives standard data models and nomenclature across systems and organizations. The data dictionary lays out the how and when data is updated, what values are expected and how to manage changes in a manner that maintains data integrity.

Even if a data dictionary exists, it doesn’t completely solve the problem. If the business objectives, requirements and KPIs are unclear, misaligned or undocumented, the data collected and the output will often be disconnected from the expectations and needs. 

Executive alignment and stakeholder buy-in

At one of my previous companies, my RevOps team initiated and led an effort to map out and fully define all funnel stages, entrance and exit criteria and the KPIs we wanted to track across the funnel. We quickly realized that this was more than just updating a few fields in our systems, creating a couple of new dashboards and voilá! job done.  At the onset, we didn’t even agree on the stages, let alone the exit criteria across all stakeholders. So, RevOps created a tiger team that included the Marketing, Finance and Data Analytics teams – and we didn’t start with the data. 

The absolute first thing we did was discuss and socialize the effort with our executives, who we asked to be formal sponsors. We documented the problems we were solving, the business objectives and the proposed KPIs that we wanted to measure.  And most importantly, we aligned on the questions we wanted to be able to answer as a result of this work. 

Design and execute change

Once there was executive alignment and stakeholder buy-in, we moved forward starting with whiteboarding the funnel.  We debated and aligned on stages, ownership, definitions, exit criteria, naming conventions, data sources and how and where we report the master official dashboard of our performance and history.  Most importantly, we resisted the urge to jump into technical work and stayed out of any and all systems!

Once we finalized the above, we engaged again with our executive sponsors to respond to any questions, obtain feedback and get the green light to move forward to the next phase. Now was the time to jump into our systems. We asked our Technical and Systems teams to translate the business requirements into a technical plan, with a timeline, resources needed, a QA phase and a cutover plan. After a few weeks we were ready to flip the switch, publish the new dashboards, enable our internal customers and now voilá! job done!

This effort was wildly successful because we treated it as a real program and not a quick fix.  We didn’t build junk on top of junk. We pulled together a tiger team that included representatives from all stakeholders who had the core, and now collective, responsibility to manage the data and systems to deliver the insights to the business. The fact that we started with the business objective and stayed outside of our systems until we had the business design and full alignment were key contributors to the successful outcome. The tiger team was fundamentally defining and enabling how we were going to measure our operating plan for the foreseeable future. Doing anything less than the above, cutting corners or rushing through this effort wasn’t an option given the magnitude of how important this was to the company.   

If you can’t provide insights in real-time, you can’t manage in real-time  

If you don’t have the data needed or it’s incomplete or inaccurate, you can’t provide data driven answers to critical questions in real-time. Lack of real-time answers results in less optimal decisions, by either delaying them or moving forward without all needed inputs to make a more informed one. It also wastes time for very busy resources in RevOps, Marketing and Data Analytics pulling them off of other priorities. Although executing a project like the above does take more time initially, it’s the definition of building for the long-term and will save a massive amount of time in the future. It allows the organization to be more scalable, proactive and agile. You can now identify signals and trend lines earlier, in real-time, and make better data-driven business decisions.  

Too often we don’t do it right, either because of a lack of resourcing or the team is not given the space to do so. If these insights are a priority then building the infrastructure to provide them also needs to be a priority. Build strategically, build for the long-term, build to be more proactive and the value that the company accrues will be well worth the effort expended.

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