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:

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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.

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How GTM Teams Can Future-Proof Their Data Architecture for 2026 – 2030

B2B go-to-market teams are entering a new era defined by AI-driven execution, buying group complexity, and real-time buyer signals. Yet most GTM data architectures still rely on fragmented systems, static enrichment, and lead-centric models that cannot support modern revenue operations.

How GTM Teams Can Future-Proof Their Data Architecture for 2026–2030 explores the structural shift reshaping B2B GTM and outlines the data architecture required to support identity resolution, buying group intelligence, AI-ready workflows, and real-time signal activation.

This eBook provides a practical roadmap for building a resilient, enterprise-grade GTM data foundation without disrupting your existing CRM, MAP, ABM, or analytics stack.

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Why Data Governance Is Now a Revenue Function

For a long time, data governance lived in the background of the business.


It sat inside IT. Sometimes legal. Occasionally security... It was something you needed for compliance audits, privacy policies, and system hygiene, but it rarely gets associated with pipeline creation or revenue performance. If anything, governance was seen as something that slowed go-to-market teams down. It was an approval layer or process hurdle that prevented a campaign from launching this week.


But that mental model was built for a very different GTM environment than the one enterprise revenue teams are operating in right now.