Gartner reports the average annual cost of poor data quality is $12.9 million. This guide explores the 2024-2025 financial impact of data decay.

The average annual cost of poor data quality for businesses in 2024 is $12.9 million, according to Gartner research. This financial drain stems from natural data decay, which sees B2B contact information become obsolete at a rate of 22.5% to over 30% per year. Inefficient sales and marketing operations, flawed analytics, and missed revenue opportunities are the direct consequences of this persistent data degradation.

TL;DR

Gartner reports the average annual cost of poor data quality is $12.9 million per organization.
B2B contact data decays at an annual rate of 22.5% to 30%, with some studies showing rates as high as 70%.
Sales teams waste up to 27.3% of their time navigating the consequences of bad data, such as verifying records and chasing dead ends.
Forrester reports that over a third of analysts spend more than 40% of their time correcting data issues instead of performing analysis.
Data providers like ZoomInfo and Apollo offer massive B2B databases, but often have structural gaps in coverage for local, service-based businesses.

The $12.9 Million Problem: Quantifying the Annual Cost of Bad Data

Gartner's foundational research quantifies the average annual cost of poor data quality at $12.9 million for an organization, a figure that has become an industry benchmark for the financial drain of data decay. This staggering sum is not the result of a single catastrophic failure but the slow, cumulative effect of persistent inaccuracies across business systems. The cost originates from a variety of sources, including operational time wasted by employees correcting errors, strategic decisions biased by incomplete analytics, and ineffective marketing campaigns targeting incorrect segments. For example, sales representatives are estimated to waste 27% of their time dealing with the fallout of bad data, costing an estimated $32,000 per rep annually. The original Gartner analysis, detailed in the 2020 report titled Magic Quadrant for Data Quality Solutions, surveyed large enterprises already investing in data solutions, indicating the problem's scale even among sophisticated organizations. These costs represent a significant and continuous leak of resources that directly impacts profitability and operational momentum, turning what seem like minor data entry errors into a multi-million dollar liability.

The financial consequences of poor data quality extend beyond operational waste to directly erode top-line revenue by as much as 15% to 25%, according to research from MIT Sloan. This revenue leakage stems from missed sales opportunities, damaged customer relationships, and an inability to execute go-to-market strategies effectively. The problem is deeply felt within sales departments, where trust in the foundational data required for their work is critically low. According to the Salesforce State of Sales 2024 report, only 35% of sales professionals state they have complete trust in the accuracy of their organization's data. This lack of confidence is a direct contributor to inefficiency; other Salesforce research indicates sales reps spend just 40% of their workweek on actual selling activities, with the rest consumed by administrative tasks often exacerbated by poor data. When a sales team cannot trust its CRM to provide accurate contact information or reliable deal insights, its ability to build a pipeline and close deals is fundamentally compromised, leaving a significant portion of potential revenue on the table.

The '1-10-100 Rule' provides a powerful framework for understanding the escalating cost of a single data error left unaddressed over time. Originally codified by George Labovitz and Yu Sang Chang in 1992, the principle states it costs approximately $1 to prevent an error by verifying a record at the point of entry, $10 to correct or remediate that same record later in a batch process, and $100 in downstream costs if the error is never fixed. The $1 cost represents proactive measures, like using real-time verification APIs in web forms. The $10 cost manifests as the labor and technology expenses for periodic data cleansing projects. The $100 cost, however, represents the significant business impact of failure, including wasted marketing spend, compliance fines for regulatory non-compliance, and reputational damage from poor customer experiences. In the modern context of artificial intelligence, this final cost can be even higher, as flawed data fed into AI models produces inaccurate outputs that lead to poor strategic decisions and abandoned projects, as detailed in research from sources like Datamatics.

Cost Category	Affected Business Function	Key Statistic / Methodology	Example Financial Impact
Sales Productivity Loss	Sales	Reps waste 27% of their time on bad data. (Source: Landbase.com)	An estimated loss of $32,000 per sales representative annually.
Revenue Leakage	Sales & Finance	Companies lose 15% to 25% of total revenue. (Source: MIT Sloan)	A company with $50M in revenue could lose $7.5M to $12.5M annually.
Data Remediation	IT & Data Operations	The 1-10-100 Rule shows it's 10x more costly to cleanse data than to verify it at entry. (Source: Multiple)	A record that costs $1 to verify proactively costs $10 to fix in a batch process.
Downstream Failure Cost	Strategy, Marketing, Legal	The 1-10-100 Rule estimates a 100x cost multiplier for uncorrected errors. (Source: Multiple)	A single error can lead to $100+ in costs from failed deliveries, compliance fines, or lost customers.
Wasted Marketing Spend	Marketing	An estimated 20-40% of marketing budgets are wasted on activities tied to poor data.	Failed direct mail, bounced emails, and targeting the wrong audience segments.
Flawed AI Initiatives	Data Science & Analytics	89% of data leaders with AI have seen inaccurate outputs due to bad data. (Source: Salesforce)	Abandonment of AI projects and strategic decisions based on faulty AI-driven insights.

How Fast Does B2B Data Actually Decay? 2024-2025 Rates

B2B data decay rates establish a clear, if alarming, baseline for the annual degradation of a company's most valuable asset: its customer and prospect information. The most widely cited industry benchmark, originating from MarketingSherpa research, posits that B2B data decays at a rate of 2.1% per month, which compounds to an annual rate of 22.5%. This means that for a database containing 10,000 contacts, 2,250 records will become inaccurate or obsolete within a single year due to factors like job changes, company acquisitions, and phone number reassignments. Over a two-year period, without any intervention, nearly 40% of the database would be unreliable. This steady erosion of accuracy directly impacts the operational efficiency of sales and marketing teams, who invest significant resources creating campaigns and outreach sequences. As detailed in a 2024 DemandScience report, when a substantial portion of an email list is outdated, these resources are squandered on contacts who will never receive the message, leading to diminished campaign effectiveness and distorted analytics that undermine strategic decision-making. The compounding nature of this decay requires a proactive, rather than reactive, approach to data hygiene to prevent a significant loss of operational capacity.

While the 22.5% annual decay figure is a well-established benchmark, more recent research indicates that for many businesses, the rate of data degradation is accelerating significantly. Some analyses now place the upper bound of annual B2B contact data decay as high as 70.3%, a figure noted in research cited by Forbes and others. This accelerated decay suggests that in certain high-turnover industries or for specific types of contact data, a database of 10,000 records could see over 7,000 of them become unusable within just 12 months. A November 2024 analysis from Landbase, for instance, found that email addresses alone decayed at 3.6% in a single month, a rate that nearly doubles the traditional 2.1% monthly benchmark and points to a more volatile data landscape. This higher velocity of decay is driven by a confluence of factors including macroeconomic shifts that influence job mobility, widespread corporate restructuring, and the continuous evolution of technology stacks within target accounts. For revenue operations teams relying on AI-driven workflows and real-time engagement, as noted by Cognism in a May 2026 analysis, such rapid data degradation poses a critical threat, risking the automation of flawed outreach and undermining the integrity of the entire revenue engine.

The primary driver of B2B data decay is the relentless pace of change in the professional world, specifically job changes, promotions, and corporate restructuring. People move between companies, get promoted to new roles with different responsibilities, or leave the workforce entirely, rendering previously accurate contact information obsolete. The U.S. Bureau of Labor Statistics provides a clear macroeconomic lens on this churn; in its September 2026 Employee Tenure Summary, the agency reported that 20.6% of all U.S. wage and salary workers had been with their current employer for 12 months or less as of January 2026. This single statistic underscores how a fifth of the workforce represents a moving target for B2B marketers and sellers each year. This churn is not evenly distributed, with median tenure varying significantly by age and occupation. For example, the BLS data from January 2026 shows that workers aged 25 to 34 have a median tenure of only 3.0 years, compared to 9.6 years for those aged 55 to 64, making data for younger professional cohorts decay much faster. Every job change invalidates a work email, a job title, and a direct-dial phone number, severing the connection between a vendor and a potential buyer.

Micro-level studies of contact record changes reveal just how pervasive data decay is within any given 12-month period. One analysis of business contact records found that a remarkable 70.8% of them experienced at least one significant change over the course of a year. This high rate of change demonstrates that the majority of contacts in a typical, unmaintained CRM will have at least one piece of outdated information annually. The study broke down the specific fields that were altered, finding that job titles were the most frequent point of decay, with 65.8% of contacts having a title change. This specific type of decay is particularly damaging, as a change in job title often signals a change in purchasing power or role-based responsibilities, making prior segmentation and messaging irrelevant. Furthermore, the same research found that 42.9% of phone numbers and a significant percentage of email addresses also changed, directly impacting the ability to connect with the contact. Even if an email address remains valid after a promotion, the context of the outreach is lost if the seller is referencing a prospect's old role. This illustrates that decay is not merely a matter of deliverability but of relevance and accuracy across every field in a contact record.

Impact on Sales: Wasted Hours and Missed Quotas

Sales representatives lose an estimated 27.3% of their time to unproductive tasks stemming directly from poor data quality, a figure that translates into a significant drain on any sales organization's resources. Research from a 2024 Gartner survey highlights that reps spend a substantial portion of their workweek navigating stale contact records, including bounced emails and disconnected phone numbers. This lost time, which some analyses calculate at over 500 hours per sales rep annually, is time not spent on revenue-generating activities like discovery calls, relationship-building, or closing deals. According to the Salesforce "State of Sales, 6th Edition (2024)" report, which surveyed 5,500 sales professionals, reps spend only about 30% of their week on actual selling activities. The remaining 70% is consumed by administrative tasks, internal meetings, and manual data entry, much of which is a direct result of working within systems populated by decayed, inaccurate information. This inefficiency directly impacts a company's bottom line, as reps are forced to become part-time data administrators instead of full-time sellers, constantly fighting their own tools to find and engage qualified prospects.

Inaccurate CRM data directly sabotages sales outreach by causing high email bounce rates, which severely damages a company's sender reputation and overall deliverability. When a sales team sends emails to invalid addresses pulled from a poorly maintained CRM, internet service providers (ISPs) and mail providers register these as hard bounces. A high hard bounce rate is a primary indicator to providers like Google and Microsoft that the sender is not practicing good list hygiene. Consequently, they may begin to throttle the sender's domain or automatically route all messages, even legitimate ones intended for valid prospects, to the spam folder. This damage is cumulative; a few neglected invalid records can quickly erode a domain's reputation, making every subsequent campaign less effective. This problem goes beyond wasted effort on a single email, as it jeopardizes the deliverability of the entire sales organization. Research shows that B2B contact data can decay by as much as 70.3% per year, with 23-30% of email addresses becoming outdated annually, ensuring that without proactive data management, sender reputation will inevitably degrade and undermine future sales and marketing efforts.

The high rate of job turnover within sales teams is a significant, yet often underestimated, engine of B2B data decay. According to January 2026 data from the U.S. Bureau of Labor Statistics, the median tenure for workers in management occupations was 6.1 years. In stark contrast, the tenure for roles more aligned with frontline sales is much lower; for instance, workers in food preparation and serving had a median tenure of just 2.0 years in 2022, illustrating the volatility in customer-facing positions. While the BLS data does not provide a single direct comparison for all B2B sales reps, this disparity highlights the rapid personnel changes that make CRM data obsolete. Every time a contact changes jobs, their email address becomes invalid, their phone number is disconnected, and their buying influence at their former company vanishes. This churn creates a cascade of flawed data, leading to wasted effort as sales reps pursue contacts who have long since moved on. This directly impacts the accuracy of lead scoring and sales forecasting, as models built on this decayed data will mis-score contacts and present a pipeline that is fundamentally unreliable. Without a systematic process for tracking job changes and updating records, sales teams are perpetually chasing ghosts, wasting resources on leads that no longer exist.

Impact on Marketing: ROI Erosion and Budget Waste

Flawed customer segmentation, driven by decaying and inaccurate data, directly erodes marketing return on investment and wastes significant budget. When B2B databases contain incorrect firmographics, job titles, or contact information, meticulously planned campaigns are sent to the wrong audiences, leading to lower engagement, higher bounce rates, and diminished lead quality. According to research from SiriusDecisions, companies have estimated that 10-25% of their marketing budget is squandered specifically due to poor data quality. This financial drain is not just theoretical; it manifests in tangible ways, such as paying for ad impressions served to irrelevant prospects or investing in personalization engines that, fed with bad data, alienate customers with incorrect details. For example, a high-value prospect at a new company might receive a welcome email as if they were a first-time customer, or a sales team might waste days pursuing leads from a recently acquired company using outdated contact details from their former employer. These errors, which stem directly from data decay rates that can reach 2-3% per month, prevent marketing teams from building a coherent and effective single customer view, a foundational element for modern, data-driven strategies.

The consequences of poor data quality extend beyond immediate campaign failures, creating systemic issues that undermine strategic marketing decisions and damage brand reputation. When analytics and reporting are based on flawed datasets, the resulting insights are unreliable, leading leadership to make incorrect business decisions about market expansion, product development, and budget allocation. This issue is widespread; a 2024 report from Forrester noted that a majority of data professionals lack full trust in the data used for decisions within their organizations. This lack of trust is justified, as bad data directly impacts the customer experience. A database with duplicate records might cause a single prospect to receive the same promotional email multiple times, while outdated information can lead to personalization that feels tone-deaf or simply wrong, eroding customer trust and decreasing lifetime value. In the context of B2B, where relationships and reputation are paramount, these seemingly small errors compound into significant brand damage and operational drag, as teams spend valuable time cleaning data instead of focusing on strategic initiatives.

Emerging technologies like generative AI, which promise to revolutionize marketing personalization and efficiency, are particularly vulnerable to the pitfalls of poor data quality. Gartner predicts that by the end of 2025, at least 30% of generative AI projects will be abandoned after the proof-of-concept stage, with poor data quality being a primary cause alongside unclear value and inadequate risk controls. This is because AI models are only as effective as the data they are trained on. When fed a diet of incomplete, inconsistent, or inaccurate information, a common problem when only 12% of organizations are estimated to have data of sufficient quality for AI, these models cannot generate reliable outputs. The result is AI-driven content that is irrelevant, personalization that misses the mark, or analytics that produce misleading 'hallucinations.' For marketing teams, this means a significant investment in promising technology yields no return, actively creating risks rather than solving problems. Without a foundational layer of clean, structured, and continuously verified data, the transformative potential of AI in marketing remains locked away, amplifying errors instead of opportunities.

Marketing Area	Impact of Poor Data	Key Metric Affected	Estimated Negative Impact	Source (Year)
Email Marketing	Incorrect contact details and outdated records.	Email Deliverability & Bounce Rate	23% of email lists degrade annually.	ZeroBounce (2025)
Digital Advertising	Flawed audience segmentation and targeting.	Wasted Ad Spend	10-25% of marketing budget wasted.	SiriusDecisions (2024)
Lead Management	Incomplete or duplicate lead profiles.	Sales & Marketing Productivity	91% of CRM data is incomplete or stale.	Salesforce Research (2024)
Generative AI Projects	Unreliable or biased training data.	Project Abandonment Rate	30% of GenAI projects abandoned by 2025.	Gartner (2024)
Overall Revenue	Inability to make data-driven decisions.	Annual Revenue	Costs organizations $12.9M annually.	Gartner (2020)
Customer Personalization	Outdated customer journey information.	Customer Lifetime Value (CLV)	Leads to fragmented and poor experiences.	Multiple (2024)

Industry Responses: From Data Warehouses to Verification APIs

The data quality solutions market is undergoing a significant transformation, driven by a decisive shift toward AI-augmented platforms that automate core functions. The 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions highlights this trend, noting that the market is now dominated by solutions leveraging AI, metadata, and knowledge graphs. This evolution emphasizes reducing manual effort by automating processes like data profiling, rule discovery, and matching. According to Gartner's analysis, vendors are increasingly embedding generative AI and machine learning to empower business users to handle complex data quality tasks, such as interpreting profiling results and developing validation rules. This move toward automation is not merely a feature enhancement; it represents a fundamental change in how data quality is managed. Gartner predicts that by 2027, 70% of organizations will adopt modern data quality solutions specifically to support their AI and digital business initiatives, moving from reactive fixes to proactive governance. Vendors like Informatica are leveraging proprietary AI engines to automate the identification and remediation of data quality issues at petabyte scale, signaling a new standard where augmentation is an essential capability, not just an add-on.

Major B2B data providers, including ZoomInfo and Apollo.io, have built their platforms around a 'people at companies' data model, which excels at compiling vast databases of professional profiles from larger enterprises. These platforms serve as powerful prospecting tools for teams targeting mid-market and enterprise accounts, with ZoomInfo's database containing over 300 million professional profiles and Apollo.io's holding over 275 million contacts. However, this large-scale aggregation model shows significant limitations when applied to the local small and medium-sized business (SMB) sector. While a comparison of Apollo vs. ZoomInfo shows ZoomInfo generally has higher data accuracy (75-85%) compared to Apollo (65-80%), both platforms can struggle with the nuances of local businesses. These prebuilt databases often contain generalized, outdated information that fails to provide the specific, actionable insights needed to connect with small business owners. The core issue is that their data collection and verification processes are optimized for corporate hierarchies, not for the fluid and often informal structures of local plumbers, salons, or independent restaurants, creating a distinct capability gap for companies targeting this segment.

A critical capability gap exists between providing data for large corporations and sourcing verified owner and contact information for local businesses. While enterprise-focused platforms are rich with data on employees at established companies, they are often unreliable for identifying the correct decision-maker at a local SMB. The challenge stems from a fundamental difference in data structure and verification requirements. Finding a marketing director at a Fortune 500 company is a straightforward query for a large database, but identifying the current owner of a specific local restaurant or plumbing service is a much more difficult task that requires a different verification methodology. This gap forces sales and marketing teams targeting the SMB market to contend with high data decay rates, which can be around 30% per year, rendering static lists ineffective. As a result, outreach campaigns suffer from low-quality leads and wasted resources. Research shows that up to 40% of leads can be off-target or unreliable, a problem exacerbated by inaccurate data at the top of the funnel. Without tools designed specifically to verify ownership and contact details for this fragmented market, companies are left flying blind, unable to effectively connect with a substantial part of the economy.

Modern solutions are increasingly emphasizing real-time verification and enrichment APIs over the traditional practice of purchasing static data lists to combat rapid data decay. B2B contact information degrades at a significant rate, with some estimates suggesting a decay of 22% per year, meaning a list of 1,000 contacts could have 220 outdated records within twelve months. Real-time APIs address this problem by verifying contact details at the point of use, such as during a form submission or just before an email send, a process that can take as little as 100 to 500 milliseconds. This approach ensures that sales and marketing teams are always working with the freshest data possible, directly improving campaign performance and protecting sender reputation by reducing bounce rates. Unlike static databases, which are only as accurate as their last scheduled refresh, real-time verification protects the front door where data enters the system, preventing bad data from ever polluting the CRM. This shift is also recognized by industry analysts, with firms like Forrester highlighting the importance of real-time interactions and data integration in their 2024 Wave reports for customer data platforms, underscoring the market's move toward dynamic, on-demand data validation.

Mitigating Decay: Strategies for Maintaining Data Hygiene

Implementing a regular, quarterly data audit is the foundational tactic for arresting the financial drain of data decay, a problem that costs organizations an average of $12.9 million annually according to 2024 Gartner research. A quarterly cadence provides sufficient frequency to identify and correct degradation before it compounds, without making data maintenance a full-time role. The process begins by defining a clear "minimum viable data" standard, which for a typical B2B SaaS company might include a verified email, first and last name, job title, company name, and company size. An effective audit segments the database to run targeted quality checks for completeness, freshness, and duplication. For example, a completeness audit would pull a report of all records missing required fields like job title or direct email, allowing teams to systematically close these gaps. A freshness audit filters for contacts that have not been engaged or updated within a specific timeframe, flagging them for re-verification. Finally, a deduplication audit must use fuzzy matching logic, as simple exact-match tools will miss near-duplicates like "Jane Smith" at "Acme Corp" and "J. Smith" at "Acme Corporation," which distort pipeline reporting and lead to redundant outreach.

Adopt tools that provide a real-time deliverability percentage for email addresses, moving beyond a simple 'verified' checkmark which often creates a false sense of security. A 'valid' status merely confirms an email's format is correct and the domain exists; it does not guarantee the message will reach the inbox. This distinction is critical because mailbox providers like Google now penalize senders for bounce rates exceeding 2%, a threshold that can be easily crossed with lists that have not been properly vetted for deliverability. True deliverability tools, such as those integrated into platforms or offered as standalone services, simulate an actual delivery by making an SMTP connection to the recipient's mail server to confirm the mailbox can actively receive messages. This process, which can be executed in under 500 milliseconds via API for real-time form validation, prevents bad data from entering the CRM at the point of capture. For existing data, bulk verification using these same advanced methods is essential for cleaning large lists before a major campaign, protecting the sender's reputation and preventing the waste of marketing resources on undeliverable contacts.

Utilize a data provider that contractually guarantees data quality and offers a fair bounce credit system, ensuring incentives are aligned and you only pay for data that works. The B2B data market is crowded with vendors making similar claims of "95% accuracy," but the verification methodologies and actual performance vary dramatically. Top-tier providers like UpLead, for its 2026 95% accuracy guarantee with real-time export verification, and Cognism, with its phone-verified Diamond Data tier claiming 98% accuracy for European contacts, set a high bar. When evaluating vendors, it is crucial to move beyond marketing claims and inquire directly about their verification process, the frequency of re-verification, and their policy for crediting accounts when email bounces exceed a guaranteed threshold. A reputable provider will offer clear, unambiguous contractual commitments to their data quality, with some like i4b even allowing customers to cancel their subscription if contractual key performance indicators for accuracy are not met. This level of accountability ensures the provider is financially motivated to maintain fresh, deliverable data, directly combating the 22.5% to 30% annual decay rate that plagues B2B databases.

Prioritize acquiring 'plain-facts' data, such as verified contact information and objective firmographics, over subjective and often unreliable 'AI-slop' signals like algorithm-generated fit scores or predictive 'why-now' narratives. The foundation of any successful go-to-market strategy is a database of accurate, verifiable facts: company name, industry, employee count, geography, and the technologies a company uses. This firmographic data is essential for fundamental tasks like territory planning, total addressable market (TAM) sizing, and basic segmentation. While AI-powered sales platforms can effectively analyze these factual signals to identify promising accounts, their output is only as good as the underlying data. When a scoring model is trained on stale or inaccurate firmographic inputs, it will inevitably prioritize the wrong accounts. The most effective B2B teams build their strategy on a bedrock of continuously verified factual data, using it to identify companies that are hiring, have received new funding, or are expanding into new markets, signals that reliably indicate a potential need for new products and services.

Frequently Asked Questions

What is the average rate of B2B data decay per year?

The average rate of B2B data decay ranges from 22.5% to over 70% annually, depending on the data type. For example, email addresses can decay at a rate of 3.6% per month, while job titles change at a rate of 25-35% per year. This degradation is caused by natural business changes like employees switching jobs, company acquisitions, and phone number updates. Consequently, a CRM database that is not regularly maintained can lose a significant portion of its accuracy and effectiveness within a single year.

How is the $12.9 million cost of poor data quality calculated?

The $12.9 million average annual cost of poor data quality, a figure from Gartner research, is calculated by assessing several areas of financial impact. This includes quantifiable losses from wasted employee time spent manually correcting data errors and operational inefficiencies that disrupt business processes. The calculation also factors in the cost of missed revenue opportunities, flawed strategic decisions based on incorrect analytics, and damage to customer relationships. These direct and indirect costs accumulate to create a significant financial drain on the organization.

Which departments are most affected by bad B2B data?

Sales and marketing departments are the most directly affected by bad B2B data. Inaccurate or outdated contact information leads to failed outreach, wasted marketing spend, and low-quality leads, with some research indicating up to 40% of leads may be unreliable. Beyond go-to-market teams, bad data creates friction for revenue operations, which struggle with pipeline visibility and forecasting, and analytics teams that must spend more time reconciling data than generating insights. Ultimately, these data issues create company-wide operational drag and hinder reliable decision making.

What is the difference between data enrichment and data verification?

The primary difference is that data enrichment adds new, missing information to a record, while data verification confirms the accuracy of existing information. Data enrichment enhances a dataset by appending details like company size, job titles, or industry codes to an existing contact. In contrast, data verification is the process of checking if a recorded phone number is still in service or if a contact's job title is still current. Performing verification before enrichment is critical, as enriching a record based on an outdated or incorrect piece of information multiplies the error.

How do data providers like ZoomInfo or Apollo handle data accuracy?

Data providers like ZoomInfo and Apollo use a combination of automated and human-led processes to manage data accuracy. ZoomInfo employs machine learning, a large team of human researchers, and third-party data to verify its contact profiles. Third-party tests and user reviews suggest ZoomInfo often has an edge in the accuracy of direct-dial phone numbers and capturing recent job changes. Apollo also uses a mix of methods and claims high accuracy, though independent tests show it can vary, sometimes trailing ZoomInfo in email deliverability and phone data but remaining a strong competitor. Ultimately, accuracy depends on the specific market segment, geography, and data type being evaluated.

What is the 1-10-100 rule for data quality?

The 1-10-100 rule is a principle that quantifies the escalating cost of dealing with bad data over time. Originally developed by George Labovitz and Yu Sang Chang, the rule states that it costs $1 to prevent a data error by verifying it at the point of entry. If the error is not caught, it costs $10 to correct it later in a batch-cleaning process. If the error is never corrected and is allowed to impact customers or business operations, the cost to the business becomes $100 or more.

Last updated: October 2026

The Cost of B2B Data Decay: 2024-2025 Benchmarks