Bad B2B data costs businesses an average of $12.9 million annually, according to Gartner. This breakdown covers wasted spend, lost productivity, and compliance.

The true cost of bad B2B data in 2024 is an estimated $12.9 million per year for the average organization, according to Gartner research. [1, 7, 11] This figure encompasses operational inefficiencies, wasted marketing and sales efforts, and significant compliance risks. Poor data directly impacts revenue, with studies from MIT Sloan showing it can cost companies 15-25% of their revenue. [15, 17, 22]

TL;DR

Gartner estimates the average annual cost of bad data for an organization is $12.9 million. [1, 7, 11]
Sales reps waste up to 27% of their time, or 550 hours per year, dealing with inaccurate data. [1, 6, 27]
B2B contact data decays at a rate of 22.5% to 70.3% annually, making databases quickly obsolete. [2, 4, 9]
McKinsey research indicates that data quality issues can cause a 15-25% loss in revenue. [7, 15]
Incumbent data providers like ZoomInfo and Apollo struggle to resolve verified contacts for local SMBs, a structural market gap.

The $12.9 Million Problem: Deconstructing Gartner's Data Quality Benchmark

Gartner's widely cited research establishes a stark financial baseline, estimating that poor data quality costs the average large organization $12.9 million per year. This figure, derived from a 2020 survey of 154 enterprise customers across 16 data quality vendors, is not a hypothetical risk but a tangible, recurring drain on corporate resources. The cost manifests as an ongoing operational tax, paid through thousands of hours of manual data correction, reprocessing of failed automated workflows, and the significant expense of reconciling conflicting reports between departments like sales and finance. According to research highlighted by the MIT Sloan Management Review, these costs arise as employees are forced to accommodate bad data by seeking confirmation from alternative sources and dealing with the inevitable mistakes that follow. This constant, low-level friction erodes productivity and inflates operational budgets, turning the data that should be an asset into a significant liability. The problem is pervasive, with a Forrester report noting that nearly one-third of analysts spend over 40% of their time just vetting and validating data before it can be used for strategic analysis.

Expanding from the organizational to the national scale, the economic impact of bad data is staggering, with a 2016 IBM report estimating the cost to the U.S. economy at $3.1 trillion annually. This colossal figure accounts for wasted resources, misallocated investments, and the productivity cost of knowledge workers spending up to 50% of their time simply hunting for data, correcting errors, and seeking confirmation for data they do not trust. While the IBM figure is from 2016, its continued citation underscores the magnitude and persistence of the problem. More recent analyses reinforce this, with Experian finding that bad data directly impacts the bottom line of 88% of American companies, costing the average company 12% of its revenue. This revenue loss is a direct consequence of flawed strategic decisions, failed marketing campaigns targeting incorrect customer segments, and sales teams wasting, by some estimates, over 27% of their time on leads with inaccurate contact information. The 2024 Total Economic Impact™ study by Forrester on a Dun & Bradstreet data management solution further quantified this, showing how a single organization could realize $17.5 million in benefits over three years simply by unifying fragmented customer and supplier data.

To make this multi-trillion-dollar issue more concrete for individual businesses, the cost can be broken down to a per-employee level. A 2023 analysis building on Gartner's work calculated the average annual cost of dirty data at $4,912 per employee. This calculation makes the expense tangible for businesses of any size, demonstrating that a 100-person company could be losing nearly half a million dollars annually to data inefficiencies. The cost is not distributed evenly, with data-intensive sectors like Information technology and Finance & Insurance seeing much higher per-employee costs, at $12,161 and $5,991 respectively. This granular view highlights how bad data acts as a silent saboteur of productivity. For example, the SiriusDecisions Demand Waterfall®, a model designed to align sales and marketing, is rendered ineffective when the underlying lead data is flawed, leading to poor hand-offs and wasted effort. As a Forrester report from March 2024 notes, 93% of business leaders agree that improved data collaboration is critical for revenue growth, a goal that is fundamentally unachievable without a foundation of clean, reliable data.

The consequences of inaction extend beyond mere financial waste, directly threatening strategic initiatives like artificial intelligence. As organizations race to deploy AI, the principle of 'garbage in, garbage out' becomes critically important. A 2025 prediction from Gartner, cited by Forbes, suggests that 30% of generative AI projects will be abandoned due to unreliable data and weak governance. This is not a future problem; one analysis noted that 42% of companies had already scrapped major AI initiatives in 2025 because their data foundation was insufficient to support the technology. The financial impact of such failures is immense, as seen when Unity Software reported a $110 million revenue loss in 2022 due to ingesting bad data from a single large customer. This demonstrates that the $12.9 million average cost is just a baseline. For companies heavily invested in data-driven operations and advanced analytics, a single data quality failure can trigger a cascade of errors, leading to flawed models, misguided business strategy, and catastrophic financial and reputational damage, as evidenced by the Equifax incident where inaccurate credit scores were sent for millions of consumers.

Research Source (Year)	Reported Annual Cost (Organization)	Reported Annual Cost (Macro/National)	Primary Impact Areas	Key Statistic or Finding
Gartner (2020 Survey)	$12.9 Million	N/A	Operational Inefficiency, Manual Corrections, Failed Workflows	Based on a survey of 154 large enterprise customers.
IBM (2016 Report)	N/A	$3.1 Trillion (U.S. Economy)	Wasted Knowledge Worker Time, Error Correction, Mistrust in Data	Estimates knowledge workers waste 50% of their time on data issues.
MIT Sloan Management Review (2017)	15% to 25% of Revenue	N/A	Accommodating Errors, Seeking Confirmation, Rework	Positions bad data as a direct drain on top-line revenue.
Experian (pre-2021)	12% of Revenue	N/A	Impacts Bottom Line, Decision Making	Found that 88% of American companies suffer bottom-line impacts from bad data.
DoubleTrack Analysis (2023)	$4,912 per Employee	$617 Billion (U.S. Economy)	Productivity Loss, Sector-Specific Inefficiencies	Calculates a tangible per-employee cost, with higher rates in tech ($12,161).
Forrester TEI Study for D&B (2024)	$17.5M in benefits over 3 years (by fixing)	N/A	Sales Reporting, Supplier Visibility, Customer Engagement	Quantified the ROI of a data management solution at 208%.

Wasted Spend: How Inaccurate Data Inflates Customer Acquisition Costs

A significant portion of marketing budgets evaporates due to flawed underlying data, directly inflating customer acquisition costs before a single campaign goes live. Foundational research from Commerce Signals, which analyzed the impact of ad impressions on actual sales, revealed that a staggering 47% of digital marketing spend is wasted. [23] This waste is not random; it is a direct consequence of targeting audiences based on inaccurate or incomplete information, leading to investments in ad impressions that generate no incremental sales. For a company with a $2 million annual marketing budget, this represents $940,000 in misallocated funds. [21] The problem has been compounded by increasing data fragmentation, with a 2026 Lifesight Industry Report noting that platform data can systematically overstate conversions by 20-40%, creating a false sense of security while budgets are directed toward underperforming channels. [24] This flawed feedback loop means that without a solid data foundation, marketers are often scaling the very campaigns that offer the lowest real return, perpetuating a cycle of inefficiency and making it impossible to discern which 47% of their budget is truly being wasted. [26]

The rapid decay of B2B contact data creates a significant and accelerating financial drain, particularly through email marketing. As of late 2024, email decay rates in fast-moving industries reached an alarming 3.6% per month, a substantial increase from the traditional 2.1% monthly average. [2, 3, 5] This accelerated decay means that a B2B contact list can lose over a third of its valid addresses annually, rendering a significant portion of a company's database obsolete. [2] The consequences are twofold: direct financial waste and long-term reputational damage. Every bounced email represents wasted spend on marketing automation platform fees and sending costs. More critically, high bounce rates directly harm sender reputation. [28] Mailbox providers like Gmail begin to penalize senders when bounce rates exceed 2%, leading to throttling and reduced inbox placement for all future campaigns, not just those sent to invalid addresses. [25, 30] According to a 2025 report from Validity, 76% of organizations admit that less than half of their CRM data is accurate, a deficiency that directly leads to lost sales opportunities, with an average of 16 deals lost per quarter due to unreliable data. [14]

Invalid traffic (IVT) and ad fraud represent a massive, direct tax on marketing budgets, with projections showing that advertisers could waste over $71 billion globally in 2024 due to fake engagement. [4, 6] This figure, representing a 33% increase from 2022, is the result of non-human activity like bots and click farms that artificially inflate impression and click counts. [4, 6] Research from Lunio, based on an analysis of 2.6 billion paid ad clicks, found that 8.5% of all paid traffic is fake, meaning roughly one in every twelve paid website visits is not from a real person. [4, 10] The issue is particularly pronounced on certain B2B-focused platforms; a 2026 analysis from Lunio found that LinkedIn recorded an average invalid traffic rate of 19.88%. [17] This fraudulent activity directly inflates customer acquisition costs by forcing companies to pay for engagement that has zero chance of converting, skewing performance data and leading to misguided budget allocations. [10] The financial damage is substantial, with some estimates suggesting that sophisticated invalid traffic can account for up to 11.5% of all clicks in Google Ads alone. [11]

Flawed attribution models systematically misdirect marketing spend, causing organizations to unknowingly over-invest in ineffective channels while starving those that genuinely drive growth. When attribution is broken, marketers may misallocate between 20-40% of their budget, a problem that stems from an inability to accurately track which touchpoints lead to conversions. [9] For example, last-click attribution, a common but outdated model, ignores the crucial upper-funnel activities that introduce and nurture prospects, giving full credit only to the final interaction before a sale. [12] This creates a distorted view of performance where top-of-funnel campaigns appear unprofitable and are cut, starving future growth. [12] The financial impact is immense, with research suggesting that broken attribution is a primary driver behind the 47% of marketing spend that is ultimately wasted. [19, 20, 24] This isn't just a reporting error; it's a strategic crisis that leads to a compounding loss. As one analysis from Linkrunner points out, companies not only waste money on underperforming channels but also suffer the opportunity cost of not scaling the channels that truly work. [9]

Lost Productivity: The Hidden Tax on Your Sales and Marketing Teams

The most immediate and quantifiable impact of bad B2B data is the immense drain on sales productivity. Research from ZoomInfo and Everstage reveals that sales representatives spend a staggering 27.3% of their time grappling with inaccurate contact data, which translates to approximately 546 hours, or nearly 14 full work weeks, wasted per representative each year. This is not passive administrative time; it is active, revenue-preventing work. This includes dialing wrong numbers, resending bounced emails, and researching contacts who have long since changed roles. The Salesforce "State of Sales, 6th Edition" report from 2024, which surveyed 5,500 sales professionals, corroborates this inefficiency, finding that reps spend only 30% of their week on actual selling activities. The remaining 70% is consumed by non-selling tasks, with a significant portion dedicated to manual data entry and prospect research, activities made exponentially more difficult and time-consuming by unreliable data. This operational friction means that for every hour a rep spends on a productive sales call, they may have already lost two hours just trying to find the right person to speak with, a hidden tax that directly erodes pipeline development and quota attainment.

Beyond the front lines of sales, bad data imposes a severe productivity penalty on the technical teams responsible for turning information into strategy. Data scientists and analysts, who are hired to extract valuable insights and build predictive models, are instead bogged down by preparatory work. A widely cited statistic, which originally appeared in a New York Times article, estimates that data scientists spend 50% to 80% of their time on the laborious process of 'data wrangling', which involves collecting, cleaning, and organizing unruly digital data before any analysis can begin. More recent surveys, such as Anaconda's annual State of Data Science report, show this figure has improved to 35% to 40% due to better tooling, but it remains the single largest portion of their workload. This bottleneck is also confirmed by a 2025 Kaggle survey, which found that over half of a data scientist's time is still spent on data cleaning and preparation. This time sink prevents organizations from fully leveraging their investment in analytics talent, as the most expensive, highly skilled employees are forced to perform janitorial work on data rather than generating the forward-looking insights that drive competitive advantage.

The cumulative effect of this widespread inefficiency is a corrosive impact on employee morale, leading to higher attrition and a reactive, problem-solving culture. When sales representatives consistently work with faulty CRM data, they waste effort on dead-end leads, which can be deeply frustrating for commission-based roles and erodes their confidence in the very systems designed to support them. This sentiment is mirrored in data teams, where the tedious, manual work of correcting data is often cited as the least enjoyable part of the job, contributing to burnout. This constant fire-fighting creates a state of reactive problem-solving, where teams are perpetually fixing past errors instead of proactively building future opportunities. The frustration and mistrust in company data can lead to employees quitting, damaging faith in the organization's leadership and creating a cycle of costly recruitment and retraining. Ultimately, this operational drag is more than just a productivity issue; it is a cultural one that hinders innovation and makes it difficult for a business to be agile and forward-thinking.

Role	Time Spent on Bad Data-Related Tasks	Primary Wasted Activity	Annual Productivity Loss (per employee)	Source / Report
Sales Representative	27.3% of time	Correcting CRM records, verifying contact info	~546 hours	ZoomInfo / Everstage Research
Data Scientist	35% to 80% of time	Data cleaning, wrangling, and preparation	~728 - 1664 hours	Anaconda / NYT / Kaggle
Marketing Professional	Up to 27% of time	Manually correcting data errors, managing bounced campaigns	~561 hours	Actian Corporation Analysis
IT Professional	53% cite as a barrier	Modernizing legacy systems, dealing with fragmented data	Significant project delays	IBM State of Salesforce 2025-2026
HR Professional	N/A (Task-Based)	Correcting payroll, managing inaccurate employee records	Increased compliance risk and employee dissatisfaction	Ingentis Analysis
Sales Manager	12-15% of time in meetings	Reviewing inaccurate pipelines, managing forecast calls	~249 - 312 hours in meetings	CloserBrief Analysis

Compliance and Reputation: The Escalating Risks of Data Negligence

Negligent data practices carry severe financial penalties under Europe's General Data Protection Regulation (GDPR), where maintaining data accuracy is a core principle. Fines for non-compliance can reach a maximum of €20 million or 4% of a company's total global turnover from the preceding fiscal year, whichever figure is higher. These upper-tier penalties apply to serious infringements, such as processing data without a lawful basis or violating the fundamental rights of data subjects, issues that are frequently rooted in poor quality B2B data. For example, using inaccurate contact information to send marketing materials can breach the principles of lawfulness and purpose limitation. The European Data Protection Board has established a clear methodology for calculating fines, ensuring they are not only proportionate but also dissuasive for each individual case. The sheer scale of these penalties was demonstrated in May 2023, when Ireland's Data Protection Commission imposed a record-breaking €1.2 billion fine on Meta for unlawful data transfers to the United States, an action that underscored the serious financial consequences of failing to protect data across borders. This precedent signals to all organizations that regulatory bodies are prepared to use their full enforcement powers, making data quality a critical pillar of any compliance strategy.

In the United States, a complex patchwork of state-level privacy laws creates a challenging compliance environment, with the California Consumer Privacy Act (CCPA) setting a formidable precedent. The CCPA, and its successor the California Privacy Rights Act (CPRA), can impose penalties of up to $7,500 per intentional violation with no statutory cap, meaning costs can escalate rapidly depending on the number of affected individuals. This was highlighted in the landmark August 2022 settlement where beauty retailer Sephora was ordered to pay $1.2 million in penalties. The action from the California Attorney General's Office stemmed from Sephora's failure to disclose it was "selling" customer data via third-party tracking pixels and for not honoring opt-out requests made through the Global Privacy Control signal. This case was pivotal as it was the first CCPA settlement unrelated to a data breach, establishing that inadequate privacy compliance alone is grounds for significant regulatory action. For B2B companies, this means that maintaining inaccurate records of business contacts in California or failing to manage their opt-out preferences correctly can lead directly to costly enforcement actions and mandated operational changes.

Beyond the direct financial drain from regulatory fines, the reputational damage caused by data negligence profoundly impacts customer trust and long-term brand equity. Poor data quality is a direct antecedent to data breaches and privacy violations, which are catastrophic for customer relationships. According to the Vercara Consumer Trust & Risk Report from December 2024, which surveyed 1,000 U.S. adults, 70% of consumers stated they would stop doing business with a brand after it suffered a security incident. Furthermore, a 2024 study from Cisco found that over 75% of consumers assert they will not purchase from a company if they do not trust its data practices. This erosion of trust has a direct correlation with revenue, as a damaged reputation deters both new customers and potential B2B partners who are increasingly scrutinizing the data-handling competence of their supply chain. The €1.2 billion GDPR fine against Meta in 2023 serves as a high-profile example where the financial penalty, while historic, was accompanied by immense public scrutiny and reputational harm that can have far-reaching consequences on user engagement and advertiser confidence.

The Data Decay Rate: Why Your CRM is a Ticking Time Bomb

Your CRM is not a stable archive; it is an actively degrading asset, losing accuracy from the moment data is entered. The most widely cited benchmark for this degradation, based on long-running research from MarketingSherpa, places the annualized decay rate for B2B contact data at 22.5%, which compounds at a rate of 2.1% every month. [2, 6, 12] This means that by the end of a single year, more than one-fifth of the contacts your sales and marketing teams rely on will be inaccurate in at least one critical field. This decay is not a result of poor data entry or system errors, but a direct reflection of a dynamic business world where professionals change roles, companies restructure, and contact information becomes obsolete. [4] For a mid-market company with 4,000 accounts, a 22.5% decay rate translates to 900 accounts having materially incorrect data by year's end. [2] This continuous erosion of data integrity is why a CRM, without constant maintenance, functions like a ticking time bomb, silently corrupting the foundation of your go-to-market strategy and creating a significant, often unmeasured, drag on revenue.

While the 22.5% annual decay rate serves as a critical baseline, the reality can be far more severe, with some analyses showing data degradation rates as high as 70.3% per year. [1, 8] This higher figure often represents the decay in fast-moving industries like technology or professional services, where employee mobility and organizational change are rampant. [4] According to a 2026 analysis from Landbase, this upper-end decay rate means that a CRM database starting the year at 100% accuracy could end it with less than 30% of its records being fully correct. [1] The acceleration is particularly pronounced in specific data types; for instance, a November 2024 analysis by RevenueBase found that business email addresses alone decayed at a rate of 3.6% in a single month, a figure that, when compounded, pushes the annual email decay rate over 35%. [3, 7] This variance underscores that data decay is not a uniform problem. High-growth companies with rapid hiring and turnover will experience a much faster rate of data corruption than more stable, established organizations, making a one-size-fits-all approach to data hygiene dangerously inadequate. [1]

The overall decay of a database is a composite of its individual parts degrading at different speeds, with contact details being particularly volatile. Research from data quality provider Data8 shows that approximately 18% of all business telephone numbers change annually, while other studies place the figure between 15-25%. [1, 5, 9] Email addresses, the lifeblood of digital marketing, decay even faster. A 2026 report from ZeroBounce, based on an analysis of over 11 billion emails, found that 23% of email addresses become invalid or risky each year. [6, 13] This is corroborated by a separate year-long study from MailCleanup, which measured a 23.79% decay rate on a list of over 900,000 addresses. [5] The primary driver behind this relentless churn is employee mobility. With the median job tenure for U.S. workers falling to 3.9 years as of January 2024, and some estimates putting the average B2B contact job change closer to every 18 months, a significant portion of any CRM is perpetually out of date. [2, 7] Each job change invalidates a title, an email, and often a phone number, turning a once-valuable lead into a dead end that wastes sales resources and harms campaign performance.

The Structural Gap: Why Incumbent Data Providers Fail at Local

Large data aggregators, including prominent platforms like ZoomInfo and Apollo.io, are structurally optimized for sourcing contacts at large B2B companies, not local small-to-medium businesses (SMBs). The core data collection methodologies of these giants rely on automated web crawling of public websites, professional networking profiles, press releases, and SEC filings. This approach is highly effective for identifying executives and knowledge workers at enterprise-scale companies who have a significant digital footprint. For instance, a March 2026 benchmark test of 1,000 B2B leads found that ZoomInfo achieved 84% email accuracy and a 67% mobile phone match rate, figures largely reflecting its strength in the enterprise sector where employees have standardized corporate contact information. However, this model fundamentally breaks down when applied to local markets. The owner of a plumbing business or a local restaurant is unlikely to appear in SEC filings or have an extensive, publicly updated professional profile, rendering the core discovery mechanism of these aggregators ineffective. One analysis notes that these traditional databases miss the vast majority of small businesses because they index sources like LinkedIn, while local businesses primarily exist on state license boards, permit databases, and platforms like Google Maps.

Public business directories and government records provide a more reliable, albeit more fragmented, starting point for sourcing data on local businesses like salons, contractors, and independent retailers. Unlike the automated crawlers used by large vendors that scan for digital professional profiles, the data for a local business is often found in non-digital or quasi-digital sources. For example, a new HVAC contractor's existence is first registered on a state regulatory board, a restaurant owner's identity is tied to permits filed with a city database, and a new dental practice appears on Google Maps and local review sites. These sources are ground-truth records of a business's operational status and location. While platforms like Dun & Bradstreet leverage unique identifiers such as the D-U-N-S Number to create corporate hierarchies, this system is most valuable for enterprise-level risk assessment, not for identifying the sole proprietor of a local service business. The challenge for sales teams is that this information is not centralized; it requires a different, more investigative approach to data collection that prioritizes local, official sources over a single, aggregated B2B database.

The data verification process for a local business owner is fundamentally different and often more manual than finding a vice president at a Fortune 500 company. Verifying an enterprise contact often involves cross-referencing digital signals: a professional profile, a mention in a press release, and a corporate email signature scraped by a user network. In a March 2026 analysis, ZoomInfo's data accuracy was attributed to a combination of AI, machine learning, and manual validation, which works well for contacts with a rich digital trail. In contrast, verifying the owner of a local auto repair shop may require a direct phone call to the business's single landline or a physical address check via Google Street View. The goal is not to navigate a complex corporate hierarchy but to confirm the existence of the business and identify a single point of contact who is often the owner, manager, and primary operator. This process, as described by one analysis, is about using authoritative channels and direct confirmation rather than relying on automated aggregation. This distinction is critical; while enterprise verification focuses on matching a name to a title within a known corporate structure, local verification is about confirming the entity itself and its key person from a patchwork of public records.

This structural capability gap means that sales teams targeting local markets often find that incumbent B2B databases have near-zero resolution for named decision-makers. While a platform like Apollo.io is considered a strong choice for SMBs due to its pricing and all-in-one functionality, its data collection model still relies on the same digital signals as its enterprise-focused competitors. One 2026 analysis bluntly states that prospecting tools like ZoomInfo and Apollo miss 90% of independently owned businesses because they have no LinkedIn footprint. For a sales team attempting to build a list of restaurant owners in a specific city, a query in a major B2B database will likely return a list of corporate headquarters for national chains, not the independent operators who make up the bulk of the local market. The result is wasted resources and significant opportunity cost. A March 2026 benchmark comparing Apollo and ZoomInfo noted that both vendors quietly degrade the freshness of SMB records, highlighting that this segment is not their primary focus. Consequently, go-to-market teams that depend solely on these platforms for local prospecting face a dataset that is not only incomplete but fundamentally unsuited for the task.

Frequently Asked Questions

What is the average financial cost of bad data for a business?

The average financial cost of bad data for an organization is an estimated $12.9 million annually, according to research from Gartner. [9, 21] This figure accounts for wasted resources, operational inefficiencies, and missed revenue opportunities. [4] Some research suggests the true cost is even higher, with studies from IBM indicating that poor data quality costs the U.S. economy $3.1 trillion each year. [5, 15]

How is the cost of bad data calculated?

The cost of bad data is calculated by combining direct financial impacts with operational and productivity losses. [4] A common framework is the 1-10-100 rule, which states it costs $1 to prevent a data error, $10 to correct it, and $100 if the error is not addressed and impacts customers or decisions. [9, 21] More advanced calculations aggregate costs from specific areas like data remediation, increased workload to fix errors, and the financial impact of flawed strategic decisions. [4, 5]

What percentage of B2B data is inaccurate?

Studies show a significant percentage of B2B data is inaccurate, with some research suggesting up to 70% of data in a typical CRM is outdated or incorrect. [15] In a Harvard Business Review study, 47% of newly created data records contained at least one critical error. [14] B2B sales teams themselves estimate that around 32% of their own CRM data is flawed, a figure published by Firmable in September 2026. [20] This inaccuracy is driven by a constant state of change, as contacts switch jobs, companies restructure, and phone numbers become invalid. [3]

How can I measure the ROI of improving data quality?

The ROI of improving data quality is measured by dividing the net financial gains by the total investment cost. [10] Gains are calculated by quantifying improvements in areas like operational efficiency, reduced marketing waste, increased sales productivity, and higher revenue from better decision making. [16, 18] For example, a Forrester study of a master data management platform found a 366% ROI driven by better targeting and reduced manual error correction, achieving payback in under six months. [10]

Why is local business data harder to source than enterprise data?

Local business data is harder to source because small and medium-sized businesses (SMBs) often lack the sophisticated systems and dedicated resources of large enterprises. Their data is frequently spread across disconnected platforms for accounting, sales, and operations, creating data silos. [30] Furthermore, over half of SMBs report they lack the internal knowledge or experience to manage data effectively, which contributes to higher rates of unstructured or incomplete records. [28] This fragmentation and lack of data sophistication make it difficult to aggregate and verify local business information at scale compared to enterprise data, which is often more centralized. [30]

What are the main causes of data decay?

The primary cause of B2B data decay is the constant rate of change in the business world. [3] People change jobs and roles, with B2B contact data decaying at an annual rate between 22.5% and 70.3%. [1] Other major causes include companies being acquired or restructuring, phone numbers and email domains changing, and a lack of internal data governance to clean and deduplicate records. [6] Without continuous verification, a database that is 100% accurate at the start of the year can become less than 30% accurate within 12 months. [1]

Last updated: September 2026

The True Cost of Bad B2B Data: A 2024 Breakdown