“Nerd” is a term of pride in my household. My kids and I embrace the label for its appreciation of learning, its respect for trivia, and its … well … accuracy. Our friends know the topics that might get us nerding out and tread that ground carefully. (If you bring up strategy games in our house, pack a lunch.)
One downside of being a nerd is knowing that something I might find totally fascinating could seem to others … oh, what’s the word? Boring. In my business life, I nerd out on plenty of topics that talented colleagues may consider a snooze, but few topics show that difference more often than data management. I’ll admit: It takes a special kind of person to appreciate a deep dive into data and process, and I find that we’re even more effective when we can share that focus together as a team.
Most B2B organizations should have a data center of excellence (CoE) for revenue operations
At Forrester’s 2022 B2B Summit North America, my colleague Julian Archer and I will be nerding out on data and making the case for why we believe data experts from marketing, sales, and customer success should be brought together in a single team with responsibility for operational data across the revenue engine.
I recognize that may be a significant change for your business. The decision of whether or not to move from separate operations teams for marketing, sales, and customer success to a combined revenue operations function is one that needs to be considered carefully.
Data is the ideal use case for a revenue operations model
The Forrester Revenue Operations Range Of Responsibilities Model (available here to our Forrester Decisions for Sales Operations and Forrester Decisions for Marketing Operations clients) details common responsibilities for rev ops across planning, process, technology, data, and measurement. The benefits gained by moving to a combined rev ops structure may vary across each of those disciplines.
You may feel that some of your operations teams would benefit from maintaining close alignment to their specific business function and the mutual understanding and cooperation that creates. Measurement, for example, focuses on tracking and evaluating the distinct activities of each business function, not just their combined results. Data management, on the other hand, is ideally suited for a transition to rev ops, because the majority of operating data is a shared resource across functions.
Data that is acquired by marketing is of great value to a sales team and vice versa. The need for operational data like accounts, contacts, opportunities, product info, and purchase intent crosses functional boundaries. Looking across those boundaries is critical to understanding how the data you are managing will be used, along with the downstream impacts of your choices.
The CoE Model Builds And Promotes Innovation And Enablement
I recognize that talking about data isn’t for everyone. At the same time, it’s a resource that everyone needs to use, understand, and protect to do their job effectively. Data quality and data compliance require clear processes and consistent enforcement around data acquisition, storage, use, and deletion from all employees to succeed.
Most analysts, data scientists, or data engineers know what it’s like to try to engage around data quality or compliance just to see their colleagues start looking for the exits like the boring might be contagious. So, how do you put your data teams in the best position to enforce data processes while still winning greater engagement from their peers?
The key is that a CoE is not the same as a shared services model, which would seek to offload core data tasks from other functions into a centralized team to manage quality and drive efficiency. Members of a CoE are expected to innovate and drive best practices throughout their organization. As another of my colleagues, Susan Macke, points out in her blog, the scope of a CoE goes beyond operational execution: “Innovation and incubation are key points of distinction for CoEs. The CoE is a consultative service to its stakeholders — consulting and advising in its areas of expertise to improve business outcomes in a scalable manner.”
Leadership Can Empower Data Teams To Overcome The Boring Label
By recognizing that ongoing training and other forms of team enablement are central to the job of your data management team, you provide them with the mission and authority to move beyond just cleansing data and policing policy to drive adoption and deliver greater value to their end users. They should not just be experts on how data is handled but also on how best it can be used. Establishing goals around their ability to support more use cases and drive better business decisions will position them as strong partners to the rev ops teams they support.
When data conversations are taking place between parties with a shared interest in innovation, not just compliance, it doesn’t have to be boring. Data can be intriguing, enlightening — even funny! As proof, let me leave you with some classic data humor:
Can you describe the perfect date?
There are two types of people in the world: Those that can extrapolate from incomplete data
A machine learning system walks into a bar.
Bartender says, “What’ll you have?”
Machine learning says, “I don’t know; what’s everyone else having?”
(And yes, I spent over an hour tonight reading data jokes on Reddit. I told you up front what I was.)
The original article by Brett Kahnke, Forrester's principal analyst, is here.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/Nastco