Relying on what worked in past years simply won't work for the current B2B landscape. The good news is that there are plenty of pros out there adapting and finding what works best today.
Rhys Williams, Founder & Managing Partner of Domestique, and Cayden Bergeron, Solutions Engineer at Openprise, share why high-quality data is the foundation of any effective RevOps plan, five tactical projects to up your strategy game in 2025, and critical insights on how to master the latest industry shifts.
Your CEO doesn't care about data quality. However, they care very much about having the right data to make better decisions. They may not know it but they live and die by data quality!
The trick is convincing them to invest in the right resources by demonstrating how tactical data accuracy impacts strategic decisions.
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For example, let's take your definition of an ideal customer profile (ICP). Our experts say that many organizations have no idea what their ICP really is. Instead, business leaders rely on what their sales and marketing leaders tell them – which is probably based on gut instinct or anecdotal experience and may contradict the data we have in our systems.
By researching and pointing out any flaws with our ICP, we can spot missing information in our systems and refine our go-to-market approach.
In other words, data informs strategy, which should determine which data points we prioritize for enrichment and normalization. Effective RevOps planning starts with reliable, clean data, enabling accurate insights and strategic execution.
"As you're thinking about your FY25 plan and what to focus on, ask yourself, what are CEOs thinking about? I can unequivocally tell you that they do not care about data." Rhys Williams
Jump to the clip to hear Rhys explain why CEOs don't care about data.
Prioritizing which data to enrich and clean first can be difficult. It can be helpful to think through the answers to each of these questions:
When it comes to purchasing and contract data, organizations should strive to be close to perfection. It's business-critical that executives have access to accurate revenue and cost information.
However, perfection is impossible when it comes to some firmographic, technographic, and demographic information. It's important to align which information is the most important to determine whether an account fits your product or service and then work out less crucial information from that point onwards.
Because data is so challenging to get right, all companies go through stages of maturity with different types of data. These stages (pictured below) demonstrate how important it is for people to trust the data's accuracy, get it in a timely manner, and make the right decisions based on the data.
Being honest about where your organization lands in each data category is important. For example, your CEO may be confident that revenue and cost data are very accurate and that they are able to make critical decisions based on that data. At the same time, your organization may not trust information critical for determining whether an account is a target for sales and marketing.
"It's also an issue of trying to understand and analyze data through the noise, which is one of the main reasons we start by analyzing technical data quality. If I want to plan for next year, I've got to have insights that are harder to get to with defunct data sets." Cayden Bergeron, MBA
Jump to the clip to hear Cayden explain more about the data quality pyramid and how to think about it.
During the webinar, Rhys Williams recommended five tactical projects that will position you as a strategic partner. These projects are:
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These projects are selected because they demonstrate your team is able to anticipate what will happen and be proactive.
"One of the ways that we like to analyze the business [for RevOps Readouts] is the sales velocity equation. Number of opportunities times average selling price times conversion rates divided by sales cycle. These metrics help us see if there's a slowdown or issue. And if so, where do we need to dive in?" Rhys Williams
Jump to the clip to hear Rhys discuss the tactical projects in more detail.
One of the key analyst tenets is: Garbage in, garbage out.
If companies don't take the time to analyze and improve the state of their data, artificial intelligence will only spew nonsense.
Rhys and Cayden recommend establishing lifecycle stage reporting, working with the sales team to develop a repeatable sales process, defining company metrics for consistent reporting, and then working on the more complex but necessary reporting practices like capacity planning, attribution, and establishing a reliable source of truth.
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Companies should only begin to adopt AI into daily work streams once they have a reliable data ecosystem. Effective AI adoption requires clear objectives, robust data foundations, and thoughtful integration into existing workflows.
"Where I see people going wrong is wanting the big sexy answers at the top from AI when they haven't put the work in and in the data foundations. You want AI, but can the data foundations support any meaningful analysis?" Cayden Bergeron, MBA
Jump to the clip to hear them discuss the infrastructure necessary before AI can be implemented.
Watch the full video for more on topics like data security and AI or deeper dives into any of the categories mentioned above. Check out Openprise's data enrichment roadmap and the authoritative guide to RevOps data quality for a jumpstart into your new year!
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