A new framework for RevOps data quality
Last month, we talked about the invisible twin of tech debt: “data debt.” Data debt started to accelerate as a result of the SaaS boom and builds with each new point product you introduce into your tech stack. The symptoms of data debt include stale data, duplicate data, and an increasing number of data silos. Reports take more effort to build, business decisions are harder to make, and RevOps (along with most of the management team) secretly distrusts the database.
As we concluded, data bankruptcy is not an option: you may be able to scrap your tech stack and start over, but you can rarely do that with your data. Any data debt you accumulate will eventually need to be paid off. The easiest way to do that? Don’t accumulate it in the first place.
Magical thinking never solved a business problem
The last 10 years has been an exciting time in RevOps and go-to-market (GTM) functions in general, especially if you’re fond of technologies. The explosion in GTM tech has been nothing short of breathtaking, but as fun as the ride has been, there are two stark criticisms and they are related:
Aggressive vendor messaging and a general lack of data management expertise in RevOps has somehow made us believe that these new technologies can work as expected even when RevOps data is of poor quality and siloed.
RevOps data quality drives every go-to-market investment
The current tech recession is shining a light on this issue. CFOs are demanding verifiable ROI for every GTM tech and data purchase. “Must-have” projects like ABX and CDP are not showing expected returns because of bad data quality. Today, more companies are focusing on RevOps data quality for two reasons:
As the steward of GTM data, it is RevOps’ job to ensure that RevOps data is good quality and GTM-ready, as people, processes, and technology can’t work well without good data. To take control of data quality, RevOps must first have a deep understanding of what RevOps data quality means. If you simply Google the term data quality, you will get a very technical description of how IT would define data quality.?
However, data quality for RevOps is a much broader concept. In an upcoming “Guide to RevOps Data Quality” we outline a comprehensive data quality framework that RevOps can use to create a more scalable technology stack, streamline revenue processes, and deliver GTM metrics that can be counted on. Here is a sneak preview.
A three-tier data quality model
Because RevOps manages many processes and use cases, they often approach them as distinct challenges and buy technology to solve each problem independently. This ad hoc approach results in complexity; recent research shows enterprises use on average 91 pieces of technology in their GTM tech stack, producing crushing tech and data debt that inhibit efficient growth. RevOps needs a framework to tie common RevOps problems together: it should not be a surprise that RevOps data quality is the fabric spanning all of these use cases.
RevOps data quality is a much broader concept than the traditional definition of data quality that IT usually adopts. In RevOps, data quality can best be defined using a three-tier model:
There is hierarchical dependency among the three tiers. To achieve a high level of quality in the upper tiers, you must first achieve quality at the lower tiers.?
Technical quality - can you trust the data?
Technical quality is the most familiar definition, referencing the basic integrity of the data record itself:
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Conceptualize technical quality as making the data “trustworthy.” If you can’t trust the accuracy or integrity of your data, anything you do with that data results in questionable outcomes. This is basically the proverbial “garbage in, garbage out” phenomenon at work. If you can’t trust the data, then you can’t trust any business process, reports, or analysis that use the data.
For example, if the address on your lead record is bad and your sales territories are geographically based, then your lead routing process won’t work very well. A hot inbound lead may bounce around within the sales team, or worse yet, languish in a black hole because the sales rep receiving the wrong lead has little incentive to correct the routing mistake, especially if this is a frequent occurrence.
Operational quality - can you take action on the data?
Having good technical data quality is just a starting point. End users must be able to use it to perform the necessary tasks. Databases containing data that is not usable still have bad data quality.
Operational quality comprises:
The best way to conceptualize operational quality is that it’s about making the data ready for users to take action on. This does not mean users can make the right decisions, which is the next level of strategic quality, only that they are able to take the necessary actions.?
Strategic quality - can you make the right decisions with the data?
Competitively, technical quality and operational quality are table stakes. They simply allow you to have a seat at the poker table. Strategic data quality is what allows you to differentiate and win.?
Strategic quality is about appending insights to the data: how relevant is the account/person/engagement? How valuable is it to the business? How effective is the campaign/program/vendor?
Sifting through infinite data with finite resources is not a recipe for efficient and scalable growth. Over 90% of the data a company possesses has minimal value; achieving scale and efficiency requires RevOps to isolate and enhance the 10% that is strategic so the GTM team can make the right decisions. Adding strategic quality to your data enables business users to allocate resources properly and compete more effectively, which can include:
Here is one example to help you understand the difference:
Beef up your data quality chops
Data management is one of the key responsibilities of RevOps. Are you surprised by the scope of RevOps data quality? Connect with me here and share your thoughts. You’ll be one of the first to receive the “Guide to RevOps Data Quality.”?
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10 个月The proposed three-tier RevOps data quality model seems intriguing and could potentially offer a structured approach
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10 个月Great piece Ed King, thank you. I also find that prospects are trying to pilot as much tech as possible and collect as much data as possible in an attempt to be “revenue focused”. Inevitbly, they are feeling that tech isn’t doing what they want and because there is so much data; it’s so hard to actually use. This framework including data quality is so critical. I have gone back to my old Martech days and advising prospects and clients to start functional between Sales, Marketing and Service as a way to get teams to better understand what data, why and how. Too broad implementation seems to fracture quickly with different department having different views on data.