Article
Data Decay: What, Why and How?
People and companies change every day. Companies make acquisitions, people change jobs, and intentions are dynamic. This means your data changes every day – but is your database up to date? Is the data you use to drive your business as accurate as the day you procured it? Data Decay is an issue that every company must face at some point. Email marketing databases, for example, naturally degrade by approximately 23% every year according to Hubspot.
Data decay has especially accelerated during and after the pandemic. This is agreed on by 79% of Customer Relationship Management (CRM) users according to The State of CRM Data Health in 2022 published by Validity. As the business environment restructures itself in the post-pandemic era, a new symptom is quickly spreading among companies: millions of workers are still quitting their jobs in 2022. The “Great Resignation” is affecting even the most solid data-driven strategies for B2B marketers.
High-quality data is the fuel that makes the sales funnel engines spin. According to the Global Data Management Report, which considered responses from 700 data-centric business leaders around the globe, 84% of B2B companies saw increasing demand for data-driven insights within their organizations during the COVID-19 pandemic. The effects derived from the “Great Reshuffle” or “Big Quit” have accelerated this decay to levels not fully understood.
While the degree of decay is not yet fully understood, our response to it can greatly mitigate the effects the decay will have on our organizations’ successful use of data to drive decisions. Here are 6 ways you can address data decay to ensure your data is accurate, up-to-date and, most of all, insightful:
What Is The Hidden Cost of Lead-Based GTM?
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CRM Data Governance for RevOps: A Practical Framework
Your CRM only works when your data holds up under pressure. Once records sprawl across forms, enrichment tools, routing logic, and sales workflows, small errors turn into system-wide failures. That is why CRM data governance sits at the center of revenue execution.
If you lead RevOps, you need a framework that keeps records accurate, usable, and trusted across teams. You also need a Data Management System that supports governance in daily operations, not in policy documents that no one follows. The right structure improves Data Quality, strengthens Enterprise Data Management, and gives your team a cleaner path to scale.
This guide gives you a practical framework for CRM data governance. You will see what to govern, who owns it, which controls matter, and how to make your Data Management System support better execution.
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The Next Era of GTM: Why Data Architecture Is Now a Revenue Strategy
For years, companies treated data as something that supported go-to-market. Marketing generated it. Sales updated it. RevOps cleaned it up.
Now it determines whether go-to-market works at all.
According to Gartner, B2B buyers spend only 17% of their total buying journey meeting with potential suppliers, and that time is divided across multiple vendors. That means the majority of influence, research, and evaluation happens digitally and independently before sales is engaged.
At the same time, Forrester reports that the typical B2B buying group now includes 6 to 10 decision-makers, each consuming different information and interacting across different channels.
The implication is clear: GTM has become structurally more complex. And complexity without architectural discipline creates revenue drag.
The next era of go-to-market will not be won by louder campaigns or larger sales teams. It will be won by companies that treat data architecture as revenue strategy.
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The Hidden Revenue Tax: 10 Ways Enterprise GTM Teams Lose with Disconnected Data
Enterprise B2B go-to-market (GTM) teams don’t struggle because they lack tools. They struggle because their customer data lives everywhere, but doesn’t work correctly anywhere.
CRM. Marketing automation. Enrichment vendors. Intent platforms. Sales engagement. Data warehouses. Acquired company databases. Regional instances.
Each system holds a piece of the puzzle but none of them independently point towards the same truth. When customer data is fragmented, siloed, and static, the consequences surface exponentially.
Here’s what that really looks like inside an enterprise GTM organization.
