Data-Ready or Dead Weight: The 2025 Sales-Data Health Check

Preface 

Sales organizations live and die by their customer relationship management (CRM) data. High-quality data is the lifeblood of efficient sales processes and AI-driven insights – but poor data can turn a CRM from a valuable asset into dead weight. Is your sales data ready to power growth, or is it dragging down your team’s productivity? 

This report provides a comprehensive 2025 health check of sales data, focusing on key data quality dimensions and practical steps to ensure your CRM is an engine for revenue rather than a liability. We draw on fresh 2024– 2025 research, expert commentary, and real-world case studies to guide RevOps leaders, CROs, SDR managers, sales enablement professionals, and data teams in assessing and improving their CRM data quality.

Chapter 1 

The Four Pillars of CRM Data Quality: Completeness, Decay, Duplicates, and Accuracy

A “healthy” sales database can be measured along several dimensions. We focus on four critical pillars of CRM data quality: 

Data Completeness: Are all important fields (emails, phone numbers, job titles, industries, etc.) filled out for leads, contacts, and accounts? Data Decay: How quickly does data become outdated or inaccurate over time (due to job changes, moves, etc.)? 

Duplicate Records: How much duplicate data exists (e.g. the same contact or account entered multiple times) and is it being managed? Enrichment Accuracy: If you enrich your records with third-party data (like firmographics or phone numbers), how accurate and reliable is that added information? 

Each pillar has a direct impact on sales effectiveness. We will examine each one in depth with current benchmarks and examples. 

Data Completeness: 1 Plugging the Holes in Your CRM 

Data completeness refers to having all necessary information present for each record. Unfortunately, most CRMs suffer from significant gaps. A recent Dun & Bradstreet analysis found that a staggering 91% of data in CRM systems is incomplete, with crucial fields missing in the majority of records. Sales teams feel this pain: 

45% of salespeople say their biggest data challenge is incomplete data in the CRM. Missing emails, phone numbers, or company info means SDRs waste time hunting down contact details instead of selling. It also undermines marketing segmentation and personalization. 

Why is data so often incomplete? Common culprits include sales reps failing to fill out fields, leads coming in with scant info, or legacy records missing updates. For example, sales reps frequently add new contacts on the fly but might omit the industry or phone number due to time pressures. 

Over time, these small omissions compound into large  blind spots. One 2024 survey of CRM administrators found that 25% of admins say less than half of their CRM data is accurate and complete – indicating pervasive incompleteness. 

The costs of incomplete data are tangible. Outreach campaigns flop when key contacts lack email addresses, and pipeline reviews become guesswork if deal records miss close dates or values. In one anonymous case study from a B2B SaaS startup, an audit revealed over 30% of leads lacked a contact phone number, contributing to low connect rates. By implementing required fields and an enrichment tool to auto-fill missing data, the company saw call connection rates improve and an increase in pipeline creation within one quarter.

If completeness is about filling data gaps, data decay is the gradual erosion of data accuracy over time. B2B data is not static – people change roles, companies rebrand, phone numbers get reassigned, and emails go dormant. Without continuous maintenance, even a once-pristine database will decay into irrelevance. How fast does this happen? Industry research indicates it’s alarmingly fast: 

Gartner research (via Forbes) suggests **B2B contact data can decay at up to 70% per year in extreme cases. More typical estimates show about 30% of CRM data becomes outdated annually. 

This aligns with observed workforce churn: roughly 30% of people change jobs in an average year, and 25% of job titles change, meaning a large chunk of your contacts won’t be in the same role or company by year-end. 

Even within months, decay is noticeable. For instance, email addresses decay at ~22.5% per year and phone numbers at ~18% per year, as employees leave or change numbers. One quarter’s delay in updating can turn a valid contact into a bounce or a wrong number.

Figure

CRM Data Decay Curves – Without intervention, a significant percentage of data becomes outdated over time. Even with a moderate 30% annual decay rate, less than half of a dataset remains accurate after 24 months. Higher churn scenarios (50%+ per year) can wipe out the majority of useful data within a year, underscoring the need for continual data refresh. 

The impact of data decay is often felt in pipeline metrics. If a sales development rep (SDR) calls a list of leads from last year’s trade show, many calls will fail: people have moved on or emails bounce. Chasing “ghost” contacts wastes time, burns sales rep energy, and can cost real revenue. In fact, a Validity study found 44% of companies lose over 10% of annual revenue due to CRM data decay-related issues. 

As one revenue operations leader put it, “we burn time, credibility, and pipeline chasing ghosts” when data isn’t kept fresh. 

The longer the sales cycle, the more decay hurts – consider enterprise deals that last 12+ months; by the end, key stakeholders might have changed, or their info did. Regular data hygiene (e.g. quarterly contact validation, using data enrichment services to refresh fields, or automated job-change alerts) is necessary to combat this inevitable attrition. We’ll discuss how AI and tools can help keep data “evergreen” later in this report. 

Duplicate Data: 2 The Hidden CRM Clutter

Duplicate records – when the same person or company appears multiple times in the CRM – are another major quality issue. Duplicates skew forecasting, waste sales effort, and can embarrass your team (imagine two reps unknowingly calling the same prospect). Unfortunately, duplicates proliferate easily when multiple reps or systems input data. If left unchecked, between 15–30% of a CRM’s records could be duplicates according to data quality experts. Dun & Bradstreet’s research similarly found an average of 18% of CRM records are duplicate entries. 

Duplicates hurt every department’s effectiveness. Marketing might double-email a contact (hurting engagement or triggering unsubscribes). Sales could have opportunities split across duplicate account records, obscuring the true picture. Customer success might not see all interactions if they’re split between duplicate contact entries. Moreover, analytics and forecasting become less reliable when the same entity’s data is fractured. 

The causes of duplicates range from inconsistent data entry (e.g. one rep enters “IBM” and another enters “International Business Machines” – now you have two accounts for one company) to lack of integration (leads from different sources that aren’t merged). Human error during data entry is a big culprit – without guidelines, users freely abbreviate or misspell names, creating variations that the system doesn’t catch. For example, “ACME Corp.” vs “Acme Corporation, Inc” might slip through as separate accounts unless you have duplicate detection in place. The good news: modern tools and AI can dramatically reduce duplicate data. Advanced matching algorithms can automatically identify potential duplicates by comparing multiple data points (name, email, company, etc.), flagging or even merging them. Many CRM platforms (Salesforce, HubSpot, Dynamics, etc.) include duplicate detection features, and third-party solutions (Cloudingo, DemandTools, Insycle, etc.) offer bulk deduplication with customizable rules

Later, we will outline workflows – including AI-driven ones – that continually keep duplicates at bay. The key is establishing a routine: an initial cleanup to merge existing dupes, followed by preventive measures (like duplicate checks on data entry and routine scans). Companies that institute ongoing duplicate management see immediate boosts in sales productivity, because reps no longer waste time double-checking which record to update or which colleague owns the contact.

Enrichment Accuracy: 3 Trust but Verify

Data enrichment – augmenting your records with additional information from third-party sources – has become standard practice to achieve a 360° view of customers. Tools can append firmographics (industry, size), technographics (tech stack), direct dials, and even social media links to your CRM records. However, enrichment is only valuable if the data appended is accurate. “Enrichment accuracy” refers to the correctness and reliability of these added fields. 

In 2024–2025, a plethora of data vendors promise high quality, but results can vary. Common issues include outdated info (e.g. an enrichment tool adds a contact’s title, but they changed jobs last month), incorrect firmographics (misclassified industries or employee counts), or mismatched identities (attaching data to the wrong person with a similar name). Sales and marketing teams need to trust the enrichment data; otherwise, bad data may mislead segmentation and personalization efforts. 

The ideal strategy might combine both: use a primary provider known for broad & quality coverage, and have a secondary method to fill gaps or double-check critical records. 

Ultimately, enrichment should enhance CRM data, not pollute it. Monitoring enrichment accuracy through spot checks and feedback loops is important. Many teams incorporate an “enrichment QA” step for high-value accounts – e.g. SDRs confirm that the enriched HQ location and phone actually connect to the right place. In the next sections, we will compare leading enrichment vendors and discuss how AI can assist in keeping enriched data accurate.

Reputable vendors often cite accuracy metrics – for example, G2 reviewer data shows ZoomInfo’s customers rate its company data accuracy slightly higher than Seamless.AI’s (8.2 vs 7.9 on a 10-point scale). But no provider is perfect. It’s wise to “trust but verify”: use multiple sources or verification steps for critical fields. For instance, if enrichment adds a phone number, an automated phone validation or a quick manual dial can confirm it. If industry and revenue are appended, cross-check with the company’s LinkedIn or recent press releases for sanity. 

Enrichment accuracy also depends on the freshness of the provider’s dataset and their methods. Some providers update data in real-time via web scraping or APIs, while others rely on databases updated quarterly or via user contributions. Real-time search approaches (like Seamless.AI’s model of live web queries) may yield fresher data for new contacts, whereas big static databases (like traditional ZoomInfo) might have more breadth but risk staleness.

Chapter 2 

2025 Benchmarks: 

The State of Sales Data Health

How healthy (or unhealthy) is the typical sales database as of 2025? Recent industry research paints a concerning picture. Data quality issues are widespread, and in many cases worsening, which is why RevOps leaders are sounding the alarm. Below we highlight some 2024–2025 benchmarks that illustrate the state of CRM data: 

Figure: Prevalence of CRM Data Quality Issues – Surveys indicate that incompleteness, duplication, and decay are rampant in modern CRMs. An estimated 91% of CRM records are missing key fields, ~18% are duplicated, and roughly 70% of data becomes outdated annually without proper maintenance. These benchmarks underscore the importance of regular data hygiene. 

Incomplete and Inaccurate Data is the Norm: As mentioned, 91% of CRM data is estimated to be incomplete (Dun & Bradstreet). Furthermore, an alarming survey result from Validity in 2024 showed only 25% of CRM admins believe that even half of their CRM data is accurate and complete. In other words, three-quarters of those managing CRMs know that over 50% of their data is faulty. This is a stark wake-up call: for most companies, the majority of CRM data cannot be fully trusted. 

Rising Revenue Impact: Poor data isn’t just an operational nuisance; it hits the bottom line. The average company now loses at least 20% of its annual revenue due to poor data quality, according to Validity’s 2024 “State of CRM Data Management” report. This is up significantly from prior years. Factors like missed sales opportunities (due to bad contacts) and marketing inefficiencies contribute to this loss. Another study found nearly half of companies (44%) report losing over 10% of revenue from data issues. For any CRO, these numbers justify immediate investment in data quality initiatives. 

Data Decay Accelerated by Volatility: The past couple of years (2024–2025) have seen high job market volatility and organizational change, which accelerates data decay. Gartner’s high-end estimate of 70% annual decay may apply to fast-changing sectors or regions. Even a “conservative” decay rate of ~30% yearly means a constant churn in contact data. One contributing factor: the remote and hybrid work boom has led to more frequent job changes and relocations, making contact data expire faster than before. Economic shifts in 2023–2024 (layoffs in tech, growth in other industries) also meant many contacts changed roles or companies.

Duplicate Data Remains a Challenge: Despite better CRM features, duplicates are still a headache. HubSpot, Salesforce and others have introduced duplicate alerts, yet many orgs still have double digit duplicate percentages. The typical range reported is 10–20% duplicates in CRM, though some orgs with mergers or siloed teams have higher rates. The problem often ties back to governance – without a defined owner for data quality or strict input rules, duplicates creep in. 

AI Adoption Hesitation: Interestingly, that Validity survey noted about one in three CRM admins are not using AI yet and some are actively avoiding it. A key reason cited is lack of trust in their data foundation – admins fear automating processes on top of bad data could make things worse. This highlights a paradox in 2025: AI is available to help with data quality, but many teams feel they must first fix data quality to fully leverage AI. We will explore this dynamic further in the AI section.

In summary, the industry benchmarks indicate most companies have significant room for improvement in CRM data quality. The awareness is higher than ever – as one expert said, “admins are reaching their breaking points” seeing the CRM out of control – and the willingness to invest in solutions is growing. The next sections of this report will dive into those solutions: how organizations are tackling data decay, what tools and AI techniques are emerging, and how to build a culture that prevents data from rotting in the first place.

Chapter 3 

The Cost of Dirty Data: 

Productivity and Pipeline Drain

Poor data quality isn’t just a theoretical problem; it has real consequences on day-to-day operations and strategic outcomes. Let’s examine some of the key impacts and illustrate them with examples: 

Wasted Sales Effort and Missed Opportunities: When sales reps have bad data, they lose precious selling time. Consider an SDR team doing call downs of a contact list where 1 in 3 phone numbers is wrong – that’s 30% of dials yielding nothing but frustration. One study found sales reps can waste over 27% of their time dealing with inaccurate data (e.g. calling wrong numbers, writing emails that bounce) – time that should have been spent engaging real prospects. Missed follow ups are another hidden cost: if a key contact left a target account and your CRM wasn’t updated, your rep might unknowingly keep emailing someone who’s no longer there, instead of finding the new decision maker. 

Campaign Inefficiency: Marketing campaigns suffer when CRM data is unreliable. Segmentation fails if industry or role fields are wrong or empty. Personalized outreach can backfire if, say, the contact’s name or gender is mis-recorded (no one likes an email addressing them incorrectly). Email bounces and low deliverability hurt sender reputation – if 20% of your CRM emails hard-bounce because they’re outdated, future emails (even to good addresses) may land in spam. In a Validity study, companies with poor data quality saw email campaign performance plummet, directly tying data hygiene to marketing ROI. 

Inaccurate Forecasts and Analytics: Sales forecasts and pipeline analytics are only as good as the underlying data. “Flawed data leads to flawed insights,” as one data expert bluntly put it. For example, if 10% of the opportunities in your pipeline are duplicates or ghost opportunities (associated with contacts who left), your forecasted revenue might be overstated by 10%. CRM project failure rates are notably high – Harvard Business Review has noted anywhere from 18% to 69% of CRM implementation projects fail to meet objectives. A significant contributor is data integrity issues causing user adoption problems and lack of trust in the system. Essentially, if the reports coming out of CRM are wrong due to bad data, sales leaders will start doing side spreadsheets, undermining the CRM’s role as a single source of truth.

Direct Revenue Loss and Costs: We’ve cited earlier the striking figures – millions in revenue lost annually due to bad data. To break that down: revenue loss can come from missed deals (sales never contacted a viable prospect because of a bad email), delayed closes (e.g., contract sent to wrong address causing a quarter-end slip), or customer churn (support didn’t have updated info and made a mistake with a key account). Additionally, there are hard costs to cleaning up messes – paying for data cleaning services, consultant fees, or just the salary hours of ops teams fixing data. Gartner pegged the average cost of poor data at $15 million per year for organizations, factoring all these inefficiencies. It’s a huge drag on profitability that often goes unquantified until a formal audit is done. 

Trust and Morale: Bad data erodes trust – both internally and with customers. Internally, if salespeople don’t trust the CRM, they’ll circumvent it, leading to even messier data (a vicious cycle). Morale can suffer as reps grumble that “the CRM is garbage.” Externally, mistakes like reaching out to a client who has already asked to be removed, or calling a customer who passed away (it has happened), can damage brand reputation and relationships.

AI Initiatives Falter: A very timely impact is on AI projects. Many sales orgs are trying AI-driven lead scoring, predictive analytics, or generative AI assistants. But if you feed bad CRM data into AI, you get bad outputs – the old “garbage in, garbage out” maxim. Experts have observed AI initiatives fail simply due to dirty CRM data: models learn from historical data that has errors or biases, so their predictions are skewed. In one cautionary note, the Financial Brand warned that rushing to apply AI to disorganized CRM data can lead to “data chaos” and even unintended discriminatory outcomes, especially in sensitive sectors like finance. For example, an AI lead scoring tool might consistently mis-score leads from a certain industry if many of those records in CRM were incomplete or mislabeled – leading to biased results. Some companies have had to pause or scrap promising AI tools because their underlying data wasn’t AI-ready. In short: AI amplifies data problems. One RevOps director quipped that deploying AI on bad CRM data is like “putting a rocket booster on a garbage truck” – it just helps you deliver garbage faster.

Real-World Example – Uber’s Costly Data Error: Data quality issues can afflict even tech-savvy companies. A notable (and public) example occurred at Uber in 2017: a data integration error in their systems led to an over-calculation of commission, causing drivers to be underpaid. Uber had to pay back tens of millions of dollars (about $900 per driver affected) when the error was discovered. While not a CRM sales scenario, it underscores how data errors directly translate to financial loss. Imagine a similar scenario in a sales context – e.g., if your CRM had incorrect contract terms or renewal dates for customers, it could result in revenue leakage or penalties. 

These impacts underscore why cleaning up CRM data isn’t just housekeeping – it’s a strategic imperative. Companies are increasingly treating data quality as a continuous process rather than a one-time project. In the next section, we will explore how organizations are leveraging technology, especially AI and automation, to prevent these costs by keeping data clean and up-to-date.

Chapter 4 

Case Studies: 

Successes and Failures on the Data Quality Journey

To illustrate the concepts above, let’s look at a few anonymized examples of companies that tackled CRM data quality – some triumphs, and some cautionary tales: 

Success – AI-Powered Data Enrichment at a SaaS Startup: A scaling SaaS company (“TechCo”) had a lean sales team where reps were too busy closing deals to manually update CRM records. As a result, contact data decayed quickly and many new contacts from sales meetings never made it into the CRM. TechCo implemented an AI driven data capture and enrichment workflow. They connected their reps’ calendars and email (with a tool similar to Clari’s Autocapture) to automatically capture meeting attendees and email correspondents, adding those as new contacts in CRM. According to an analysis of millions of sales interactions, around 70% of buyer contacts engaged in the sales cycle were not being added to CRM before – representing a huge completeness gap. The new AI system closed that gap, feeding all those missing contacts into CRM. They then layered on an enrichment API (from Clearbit) so that whenever a new contact was auto-captured, it was instantly enriched with title, company, LinkedIn URL, etc.. The result was a dramatically fuller database without burdening reps. Within a quarter, TechCo saw improvements: email engagement rates rose (since the database had more current contacts), and marketing could now include those previously missing contacts in campaigns. This case shows how AI can proactively maintain data hygiene – capturing changes (new people, updated titles) in real-time and keeping CRM complete. 

Success – Continuous Data Maintenance at an Enterprise: A large enterprise (“GlobalInc”) made data quality part of its culture by establishing a “CRM Data Steward” team within RevOps. This team’s mandate was to run a CRM health audit every month and fix issues. They used a combination of tools: an automated duplicate merging tool to eliminate dupes weekly, an email verification service to remove bad addresses, and quarterly refreshes from a data provider for fields like industry and employee count. They also utilized AI-powered data profiling tools that would scan the CRM for anomalies (e.g., an address field that didn’t match the city, or a contact whose email domain didn’t match their company) and flag them. Over a year, GlobalInc’s data steward team was able to raise key data quality KPIs: contact completeness went from 60% to 85%, duplicate rate fell below 2%, and sales reps reported significantly fewer “wrong contact” situations. Notably, the company tied a portion of sales ops bonus to data quality metrics – reinforcing the importance of clean data. GlobalInc’s story is a blueprint for larger organizations: dedicate ownership and resources to data quality, and treat it as an ongoing operational process.

Failure – AI Project Derailed by Bad Data: Not all attempts have happy endings. A mid-market financial services firm (“FinServe”) invested in an AI-based lead scoring and sales forecasting tool in 2025. The promise was that machine learning would prioritize the best leads and forecast sales more accurately than managers. However, the project hit a wall when the AI vendor found the client’s CRM data was full of holes and biases. Many leads had missing industry fields, and past sales data was skewed because reps often only logged deals they won (losing deals were under reported). The AI model, trained on this data, started giving odd recommendations – e.g., favoring leads in the “Other” industry (because so many records were miscategorized there) and severely underestimating certain product line forecasts (because half the losses weren’t recorded). As G2’s industry analysts noted, AI will use whatever data you give it – if it’s wrong or biased, the AI will happily incorporate those errors. In FinServe’s case, the AI initiative had to be paused while they went back to basics, conducting a data cleanup and implementing stricter data governance. The lesson learned was AI is not a shortcut around data quality; you must have solid data foundations first, or else the fanciest algorithms won’t deliver useful results.

Failure – The “One-Time Cleanup” Trap: Another anonymous example involves a B2B manufacturer that realized their CRM was a mess after years of neglect. They hired a consultant for a massive one-off cleanup project. The consultant spent two months merging duplicates, deleting junk leads, and enriching old accounts. The CRM was pristine on project completion day. Fast forward six months – the database was again riddled with duplicates and stale info. Why? The company had no ongoing maintenance plan or user training. Reps went back to old habits, and new data from various sources poured in unvetted. This highlights a common pitfall: treating data quality as a one-time project rather than an ongoing discipline. Without process and culture changes, the entropy will return. It was a costly lesson (the expensive cleanup provided only temporary benefit). After this, the company instituted monthly data audits and invested in automation to continuously clean incoming data, finally seeing sustained improvement.

These cases reinforce several key themes: the importance of automation/AI in scaling data quality efforts, the need for continuous maintenance, and the critical role of culture and processes to support tools. Next, we’ll look at the landscape of tools and technologies available to help with data enrichment and quality, including how they compare, so you can assemble the right toolkit for your organization.

Chapter 5 

The Data Enrichment & Quality Tool Landscape (2025)

The good news for RevOps and data teams is that a robust ecosystem of tools exists to help tackle CRM data problems. From large data vendors to nimble startups, there are many options. Below we provide a detailed comparison of popular data enrichment and data quality tools, beyond 

the well-known trio of Clay, Apollo, and Lusha. We’ll cover their strengths, specialties, and ideal use cases: 

ZoomInfo: The heavyweight data provider. ZoomInfo offers a massive B2B contact and company database with deep information on decision makers. It’s known for having the broadest data coverage and generally high accuracy of contact info (especially direct dials and verified emails). ZoomInfo’s strengths include advanced filtering, organizational charts, intent data, and technographic details. It’s often favored by enterprises for its scale and depth – but it comes at a premium price. If you need a one-stop shop with tens of millions of contacts and can afford it, ZoomInfo sets the standard in data breadth. 

Users often praise its quality, though even ZoomInfo has gaps and outdated entries in niche markets. One notable feature is ZoomInfo’s contributory network that updates data via users’ email signatures and activity, which helps keep data fresher. Ideal for: Mid-large companies that need a comprehensive sales intelligence platform with robust search capabilities and aren’t as price-sensitive. 

Apollo.io: Database + engagement platform. Apollo started as a data provider and evolved into a sales engagement tool combined with a prospect database. It offers a large contact database (hundreds of millions of records claimed) and is particularly popular for its affordable pricing and all-in-one approach. Apollo provides email addresses, direct dials, and even a built-in dialer and sequence tool for outreach. While its data quality is generally good, users note that ZoomInfo tends to have an edge in accuracy and coverage (especially for certain verticals), but Apollo’s cost-value ratio is a major draw. Apollo’s interface is user-friendly and it allows advanced filtering when searching for leads (by job title, industry, etc.), though perhaps not as granular as ZoomInfo in some areas. Ideal for: Small to mid-market teams on a budget who want both data and a tool to act on that data (since Apollo merges prospecting with CRM integration and sequencing).

Clay: The workflow innovator. Clay is a newer entrant that takes a unique approach: instead of just giving you a static database, Clay lets you connect to 100+ data sources and APIs, including social media and public databases, and build automated workflows. Think of Clay as a flexible “Swiss Army knife” for enrichment – you can feed it a list of companies or people and have it pull various attributes from different sources, then pipe the enriched data into your CRM or a spreadsheet. Clay also has AI research agents to find info that might not be in a traditional database. For example, you could use Clay to take a list of target accounts and fetch their latest news headlines or find employees at those companies who fit certain criteria. It’s highly customizable and geared toward ops teams who want to create personalized, multi-source enrichment workflows. The flip side is it requires some setup and experimentation; it’s very powerful but not as plug-and-play as ZoomInfo. Ideal for: Tech-savvy teams (often startups or growth stage) that want to leverage multiple data sources and automate unique data gathering processes – e.g., for account-based marketing research or building very tailored lead lists.

Clearbit: Real-time enrichment via API. Clearbit is well-known for its enrichment API that many SaaS companies integrate into web forms and CRMs for instant data appends. Clearbit specializes in 

real-time data processing and high-quality company and contact intelligence delivered via API. For instance, when a new lead fills out your website form, Clearbit can immediately fill in company size, industry, job role, and social links before the lead even hits your CRM. It has a strong focus on privacy-compliant, GDPR friendly data, and often emphasizes quality over quantity (its database might not be as huge as ZoomInfo, but it’s quite clean). Clearbit also provides products for outbound prospecting (like a Prospector tool) and has strengths in SMB data which some others lack. Ideal for: Teams that need on-the-fly enrichment (e.g., marketing teams qualifying inbound leads) and developers who want a seamless API to enrich records. Also favored by companies that care about up-to-date info on their leads at the moment of capture.

Cognism: EMEA and compliance focus. Cognism is a sales intelligence platform similar to ZoomInfo but with a particular strength in European data. They boast a large global database and put an emphasis on GDPR compliance and local legalities of data. Cognism’s data is known for being strong in EMEA regions where some US-centric providers are weaker. They also have features like mobile dial numbers (including in Europe where it’s tricky) and a “Diamond Verified” stamp for contacts that have been phone-verified by humans for accuracy. Cognism integrates AI in its platform for things like recommended contacts. Ideal for: Companies doing a lot of prospecting in Europe or globally, who need a reliable and compliant data source. Also, those who want an alternative to ZoomInfo with potentially more flexible contracts – Cognism often positions itself as a more agile, modern solution (and sometimes more cost-effective depending on needs).

Lusha: Simple and budget-friendly contact finder. Lusha started as a browser extension to find contact info (emails/phones) on LinkedIn profiles and has grown into a freemium contact database. It’s often praised for its simplicity and affordability, especially for individual reps or small teams. Lusha provides direct contact details and has a straightforward interface. It might not have the gigantic database of a ZoomInfo, but it covers many common contacts and is highly cost effective (even offers free credits). Many startups start with Lusha for quick wins on finding phone numbers or emails for known prospects. Ideal for: Startups and small businesses with limited budget who need an easy-to-use tool to get direct contact info. Also useful as a supplementary tool (e.g., if you have ZoomInfo but a contact isn’t there, check Lusha, or vice versa).

Seamless.AI: Real-time search for leads. Seamless.ai differentiates itself by performing live searches for contact info rather than relying solely on a static database. It scours the web in real time to find emails and phone numbers for the targets you specify. In theory, this yields fresher data and can sometimes find niche contacts others miss (like very new startups or specific professionals). Seamless also offers a Chrome extension and integrates with LinkedIn for one-click searching. Users report that Seamless can be hit-or-miss depending on the query – when it finds data, it’s great, but sometimes it returns less than a curated database would. Its accuracy is decent, though user reviews note that ZoomInfo generally still wins on accuracy and breadth of data (especially for direct dials). Ideal for: Those who want an alternative approach to data gathering, who value data freshness and are willing to use a more interactive search-based tool. Also good as a supplement if you’re looking for contacts not found in your primary database.

People Data Labs (PDL): The raw data powerhouse. People Data Labs isn’t an end-user application but rather a data provider and API for those who want to build data into their products or workflows. They offer a massive dataset (over 1.5 billion person profiles and tens of millions of company profiles) available via API. PDL data is used under the hood by many other tools and enterprises. With PDL, you could, for example, submit an email and get back a rich person profile (with education, work history, skills, etc.), or submit a company and get firmographics and people associated. The key aspect is you need technical implementation to use it effectively. Accuracy and coverage are generally strong given the scale, but as a raw data provider, it’s up to you to handle things like deduplication and choosing which fields to trust. Ideal for: Data teams and engineers who want to embed enrichment into their own systems or products, and who need bulk data at scale for machine learning or large-scale analytics. Also useful if you want to perform large match operations (e.g., enrich 100k leads automatically via API). PDL shines when integrated into custom solutions.

SalesIntel: Human-verified contacts for accuracy. SalesIntel is another competitor in the B2B data space, distinguishing itself with a layer of human verification. They claim to have a network of researchers who regularly call and verify contact details, ensuring high accuracy (hence their marketing as having “human-verified, 95% accurate” data). SalesIntel’s platform also offers Technographic and Intent data. It may not have the same quantity as ZoomInfo, but it often has very accurate direct dials and emails (with verification timestamps), which can be gold for phone centric sales teams. Ideal for: Teams that absolutely require top accuracy – for example, if you run call campaigns and need high connect rates, SalesIntel’s vetted phone numbers can yield better results. Also a good option for mid-market companies that want quality but don’t need the full scope of ZoomInfo’s features.

RocketReach and UpLead: Accessible alternatives. RocketReach is known for a user-friendly interface and an API, combining public web info with user-contributed data. It’s often used by recruiters and sales alike to find emails. UpLead offers a clean interface, credit-based pricing (including pay-as-you-go), and solid filtering for SMBs. These tools have smaller databases than the giants but are generally well-regarded for ease of use and cost flexibility. Ideal for: Small to mid businesses that want a straightforward contact finding service with transparent pricing. For example, UpLead is often recommended for SMBs because of its reliable data and simple monthly plans.

In summary, the landscape spans from do-it-all platforms (ZoomInfo, Apollo) to specialized tools (Clearbit, PDL) and cost-effective picks (Lusha, UpLead). The best approach for an organization is often a combination: e.g., use a large database for broad prospecting, plus an API enrichment for inbound leads, and perhaps a verification tool for critical data. There is no one-size-fits-all – it depends on budget, region focus, technical integration, and specific data needs. We recommend evaluating tools on data accuracy, coverage of your target market, ease of integration, and compliance with privacy laws (especially if operating in Europe, where GDPR should make you favor providers like Cognism or those with clear consent practices). One more consideration: many CRMs and sales engagement platforms are adding native data quality features or partnerships (for instance, HubSpot has integrated data enrichment for companies, Salesforce has native duplicate rules and even offers “Data.com” (now retired) replacements via partners). Keep an eye on what your existing platforms might offer or bundle, but also be cautious of assuming those are enough – a dedicated tool often goes much deeper.

Chapter 6 

The Role of AI in Data Hygiene and Automation

Artificial intelligence is revolutionizing many aspects of sales and marketing, and data management is no exception. In the context of CRM data hygiene, AI plays a dual role: it can help maintain data quality automatically and also ensure that advanced AI analytics built on CRM data produce trustworthy results. Here, we focus on how AI and automation can keep your CRM data clean, complete, and up-to-date with minimal human intervention, using real-world examples and workflows. 

Automated Data Entry 1 and Capture:

One of the simplest but most impactful uses of AI is capturing data that humans forget to enter. We saw in the case study how AI tools like Clari’s Autocapture add missing contacts from emails and calendar invites to CRM. Similarly, AI can monitor sources like email signatures or public profiles for updates. For instance, an AI script could parse incoming email signatures and update a contact’s phone number if it detects a new one. Salesforce and other CRMs are incorporating AI-driven activity capture that logs calls, meetings, and even suggests new contacts to create based on those interactions. By automating data entry, AI reduces the burden on reps and closes the gaps (like those 70% of contacts that never got entered manually). 

AI-Powered Data 2 Cleansing: 

AI algorithms are excellent at pattern matching and can be deployed to identify and correct data errors at scale. Modern AI data cleansing tools scan large volumes of CRM data to find inconsistencies, errors, and duplicates, and then correct them using predefined rules or learned patterns. For example, an AI might notice that “Acme Incorporated” and “Acme Inc.” are likely the same, and merge or relate those records. It might identify that a phone number “1234567890” is missing formatting and standardize it to “(123) 456-7890” for consistency. These systems use reference data and context – e.g., if a state field is “CA” but country is UK, it flags a conflict. Advanced matching algorithms can even catch non-exact duplicates (like “Jon Smith” vs “Jonathan Smith” at the same company) that simple filters would miss. Vendors like Informatica, Talend, and even new AI startups offer solutions that continuously monitor and cleanse CRM data, often integrated directly so the user barely notices (except that reports become more accurate).

Always-On Data Enrichment 3 with AI:

Instead of periodic batch enrichments, AI allows a shift to continuous enrichment. AI can monitor external data sources (social media, news, public filings) and update CRM records proactively. For instance, let’s say a key contact’s LinkedIn shows they got a promotion – AI can catch that and update the title field in CRM before a sales rep even hears the news. Tools like Nektar.ai highlight how automation can gather additional info from sources like social media profiles or third-party providers, ensuring customer records stay relevant and up-to-date AI can also handle “waterfall enrichment” – meaning if your primary source doesn’t have the data, it automatically tries a second source, and so on, until it fills the gap. The result is richer data without manual research. Over time, this builds a far more complete picture of each lead and account, aiding personalization and segmentation. 

Intelligent Change Detection 4and Alerts:

Another AI capability is change detection. For example, AI can periodically scan your contacts against web data and alert you if it finds that “Contact X’s email is no longer valid” or “Company Y just moved headquarters”. This could be done by integrating with APIs like People Data Labs or using web crawling. Instead of the team discovering a bounce or a returned mail piece, the AI preemptively catches it. Some platforms now offer “data health scores” that use AI to predict which records are likely outdated or incorrect by analyzing usage patterns, fill rates, and cross-field validation. Reps or ops can then focus attention on those flagged records. 

De-duplication and Entity 5 Resolution:

We touched on duplicates – AI takes it further with entity resolution, which is the process of determining when two records refer to the same real-world entity. Machine learning models can weigh many fuzzy factors (name similarity, email similarity, company hierarchy, etc.) to decide if Bob at Acme and Robert at Acme Co. are the same person. AI-driven dedupe can run continuously in the background, merging records or presenting suggested merges to an admin for approval. This greatly reduces the manual labor of deduplication. Some CRMs now incorporate such ML models natively; for instance, Microsoft Dynamics 365 has AI duplicate detection that improves over time. The Nektar.ai blog described how AI can save valuable time by automating duplicate identification with high accuracy, so ops teams only review edge cases. 

Natural Language Processing 6(NLP) for Data Entry & Extraction:

Sales reps often enter unstructured data – notes, call logs, etc. AI can interpret these notes to enrich structured fields. For example, if a rep writes “Talked to Jane, she mentioned they use Salesforce and have 500 employees,” an NLP system could automatically update Jane’s contact record to note CRM = Salesforce and Company Size = 500. This is an emerging area, but as conversation intelligence tools (like Gong, Chorus) transcribe calls, they could feed data back into CRM (e.g., detecting a change in buying timeline mentioned on a call and updating a field). We’re seeing early signs of this integration, where AI doesn’t just analyze conversations for coaching, but also picks out factual nuggets to keep CRM up-to-date. Some chatbot and voice assistants are also being deployed to let reps update CRM by voice (“Alexa, update the deal stage to Proposal”), which uses AI speech recognition and then inputs structured data. 

Preventative Data 7 Governance via AI:

AI can enforce data governance rules by validating entries in real-time. For example, if a rep tries to input a new address, an AI service can verify it against postal databases instantly to correct typos. Or if a new contact is added, an AI could prompt: “This looks similar to an existing contact, are you sure it’s not a duplicate?” Essentially, AI acts as a smart gatekeeper at the point of entry, catching errors and enforcing standards before dirty data enters the system. This reduces the need for later cleanup. 

All these applications of AI lead to one overarching benefit: keeping CRM data hygiene on auto-pilot. The goal isn’t to remove humans entirely – rather, AI handles the heavy lifting of maintenance so that humans can focus on strategic analysis and selling. Of course, implementing these AI solutions requires picking the right tools and possibly integrating them with your stack. Some, like activity capture, are turnkey with certain vendors. Others, like custom NLP, might need more IT investment. It’s also vital to monitor the AI-driven processes initially to ensure they’re performing as expected (trust but verify the AI, too!). When deployed well, AI can be your 24/7 data steward that never tires or overlooks a detail. 

Real-World Automation Workflow: To make this concrete, here’s an example workflow leveraging multiple AI components – 

A new lead fills out a web form. AI (Clearbit) immediately enriches it with company details (size, industry) and social links. 

The lead is routed to an SDR. Before the SDR calls, an AI email verification has checked the email validity (ensuring no bounce). 

The SDR sets up a meeting with the lead. The calendar invite is auto captured; AI adds the lead’s colleague who was CC’d on emails as a second contact in CRM. 

After the call, a conversation intelligence tool transcribes it. NLP picks out that the lead mentioned “we have an upcoming re-org in September”. The AI creates a note of “Potential org change in Sept” and flags the account for the AE to follow up around that time. 

Months later, if that lead hasn’t had activity, AI monitors LinkedIn and sees the lead changed jobs. It updates their status to “Left Company” and prompts the SDR to find a new contact for that account (possibly suggesting names using the data provider). 

Meanwhile, every weekend, the AI dedupe service runs and merges any new duplicates, and an AI analysis updates a “Data Health Dashboard” that the RevOps manager checks on Monday.

This kind of orchestration might sound futuristic, but nearly all components exist today in some form. It’s a matter of integration and configuration. The companies who embrace these AI workflows early are turning data quality into a competitive advantage – their sales teams operate on fresher, more reliable data and their AI analytics yield sharper insights because the input data is clean. 

Before implementing AI, ensure you have clear data governance goals and that your team is on board. AI is powerful, but it works best in tandem with human oversight and a culture that values data. Speaking of which, let’s turn to how to run a CRM health audit and build that clean data culture to sustain these improvements.

Chapter 7 

Conducting a CRM Health Audit: A Practical Framework

Taking a systematic approach to assess and improve your CRM data health is crucial. Here we present a practical framework for running a CRM data health audit and cleaning up your database. This framework can be scaled to the size of your organization and will lay the foundation for ongoing data management. 

1. Define Data Requirements and Quality KPIs: Start by clearly defining what “good data” means for your business. Identify the key fields that every record should have (e.g., for leads: name, email, company, phone, industry, lead source; for contacts: role/title, etc.). These are your completeness requirements. Next, establish data quality KPIs to measure – for example: % of contacts with phone numbers, duplicate rate (% of records identified as dupes), bounce rate of emails, data fill rate for each important field, record age (time since last update), etc. Also decide thresholds or targets (you might say, we aim for 95%+ of active contacts to have an email and phone, duplicate rate under 2%, etc.). These KPIs will guide your audit and be baseline metrics to improve. Essentially, you can’t fix what you don’t measure – so set up dashboards or reports for these metrics if possible. 

2. Assess the Current State (Data Profiling): Perform an initial diagnosis of your CRM data against those KPIs. Modern CRM systems or BI tools can help you query this. For example, run reports: how many contacts have missing emails? How many accounts have no industry or revenue info? Use duplicate detection tools or simple Excel matching to find dupes. Calculate what percentage of emails in your system bounced in the last campaign (as a proxy for decay). This profiling step might require exporting data to analyze in a spreadsheet or using a data quality tool’s assessment module. Some CRMs also have data health dashboards or can install packages (e.g., Salesforce has an AppExchange data quality analysis tool). At this stage, you might find stats that mirror industry benchmarks (or sometimes worse!). Be prepared for “shocking” numbers – it’s better to know the truth. You might discover, say, 25% of your accounts have no assigned industry, or that 800 contacts appear to be duplicates. Document these findings. This is your “data health report” showing areas of concern.

3. Prioritize and Plan Remediation: Not all data issues are equal – prioritize them based on impact. For example, missing emails on leads might be a higher priority to fix than a missing address. Duplicates involving open opportunities might be more urgent than duplicates of old leads. Create a remediation plan that addresses each dimension: 

Completeness: Plan to fill in missing critical fields. This could involve an enrichment tool to append data in bulk, or internal efforts (e.g., have SDRs research top 100 missing-phone leads on LinkedIn). Identify which fields you’ll target for enrichment and which tool or method to use. 

Duplicates: Decide on a deduplication approach. Many teams do a one-time dedupe project using a tool (like DemandTools, Insycle, or even Excel for smaller sets) to merge obvious duplicates. Outline the rules (e.g., keep the most recently updated record as primary, merge notes, etc.). If it’s complex, break it down by object: dedupe contacts first, then accounts, etc.

Decay/Accuracy: Plan to validate and update stale data. For emails, consider an email verification service to quickly flag bad addresses. For contacts not touched in >1 year, consider a campaign or sequence to verify if they’re still there (even a mass “hey is your info correct?” email). Purchase an updated list from a provider for key accounts if needed. Also, remove truly dead weight – e.g., if a lead has bounced and phone is wrong, perhaps archive or delete it after attempting enrichment. 

Process fixes: Note any obvious process issues. If sales reps aren’t entering data properly (as revealed by inconsistent data), plan a training or introduce required fields. If you found many opportunities with missing close dates, for example, that’s a process/training issue to address.

4. Execute the Cleanup: Now roll up sleeves and clean the data according to the plan. This might be a combination of automated and manual work. Tips for execution: 

Use tools for efficiency. For example, if using an enrichment service, export the records that need filling, run them through the service (or integrate via API), and import the updated fields. For dedupes, use a tool’s automated merge if confident, or a manual review if needed for each dup cluster. 

Stage and backup data. Always backup your CRM data before a massive cleanup (export a full dump or use sandbox environments) – you may need to retrieve something. When merging or deleting, be cautious and perhaps do it in batches to ensure nothing critical is lost. 

Validate as you go. If you enrich 10,000 missing fields, do a spot check on a sample to ensure the data makes sense. If a tool suggests merging two accounts, quickly verify they truly are the same. This prevents the cure from being worse than the disease.

Engage the team if needed. Some cleanups benefit from a “all hands day” where reps or interns verify data. For instance, a phone blitz day to call a list of older contacts to see if they still work there – you update status based on responses. It can be time-consuming but yields direct accuracy. 

For example, one company ran a “Database Spring Cleaning Week” where each SDR spent one hour a day for that week validating a chunk of accounts and leads assigned to them – updating titles, marking bad leads inactive, merging duplicates – guided by an ops-provided list. This not only cleaned data but also made reps more aware of data hygiene. As an ops leader, you might incentivize this (prizes for the most updates or accuracy checks done).

5. Implement Preventative Measures (Clean Data Culture): Once the heavy lifting is done, you want to avoid reverting to old ways. Put in place the ongoing measures which we’ll cover in the next section on culture. This includes things like: 

Setting up duplication rules in the CRM (so users get warned if they try to enter a contact with an email that already exists, for example). Required fields or validation rules (e.g., you can’t mark an opportunity closed-won without a value in the “Industry” field of the account). 

Scheduling regular audits: maybe set a calendar reminder quarterly to rerun the data health report and see if any metrics are slipping. 

Utilizing automation/AI: turn on features or tools that continuously clean (as we discussed in AI section – enable those email-to-CRM captures, set up an enrichment to run daily or weekly on new records, etc.).

6. Monitor and Iterate: Treat this like an ongoing program. Track your data quality KPIs over time. Celebrate improvements (e.g., “We increased lead phone number coverage from 50% to 90% after enrichment” – that’s a win). If some metrics stall or worsen, investigate why. Perhaps a new integration is dumping in junk leads – you might need to adjust that process. The key is to never let the data swamp build up again; small, continuous corrections are easier than giant cleanups every 5 years. Some companies even include data quality in quarterly business reviews, especially if data drives key decisions. By following this framework, you can methodically rehab a poor CRM database and set the stage for maintaining high data quality. The first full audit and cleanup can be an intensive project (spanning weeks or a few months), but it pays off through all the efficiency gains discussed earlier. Now, let’s delve into how to build a culture that supports clean data – because tools and processes alone won’t suffice if the people aspect is neglected.

Chapter 8 

Fostering a Clean: 

Data Culture in Your Sales Org

Leadership Sets the Tone: When CROs and sales leaders emphasize data quality in their messaging, it legitimizes the effort. Leaders should talk about data as a strategic asset. For example, a CRO might share in a team meeting: “Our data is part of our competitive advantage. With accurate data, our win rates improve and we hit our numbers – so I expect everyone to do their part in keeping the CRM clean.” Leadership can also back the initiative by allocating budget for tools and training, showing that the company is investing in data quality, not just paying lip service. 

Tie Data Quality to Outcomes (Make it Personal): Often reps and even managers won’t care about data rules until they see how it benefits them. Communicate success stories: e.g., “Rep A updated all her key contacts last quarter and as a result had 30% more conversations (fewer bounced emails) and closed 2 extra deals – clean data = more commission.” Conversely, share cautionary tales (anonymously) like, “We lost a renewal because the account owner never updated the client’s new CFO info and we failed to engage the real decision-maker.” Making the impact tangible creates intrinsic motivation to maintain good data. 

Establish Clear Data Entry Guidelines: As the Enlighten DQ research highlights, human error in data entry is a leading cause of CRM data problems. Standardize how information should be entered. For instance: 

Use proper casing (not all caps or all lower). 

Follow a format for names (no nicknames unless in a designated field). 

Phone numbers in international format or a consistent format. No using placeholder text like “N/A” or “asdf” in required fields (yes, it happens). 

Provide picklists for fields like industry or state to prevent misspellings. These guidelines should be documented and easily accessible. Run training sessions or create short how-to videos for new reps on “CRM Data 101 – how we do things here.” When everyone enters data consistently, it reduces duplicates and errors at the source.

Accountability and Ownership: Make it clear who owns data quality – which is actually everyone at some level. Sales ops/RevOps can own the overall program (audits, tool setup, etc.), but reps should own the accuracy of data on their accounts/leads. One idea is to incorporate a data quality check in pipeline reviews: managers ask reps not just about deal status but also spot-check if fields are filled. Some organizations even include data hygiene as part of performance evaluations (e.g., an operations KPI might be “less than X% of rep’s accounts have missing key fields”). You can also assign “data stewards” in each team – e.g., one person in each regional sales team who is the go-to for data issues and helps enforce standards among peers.

Incentives and Gamification: People respond to incentives. Consider small rewards for maintaining clean data. For example, run a quarterly contest where the rep or team with the highest data completeness score or lowest error rate gets recognition or a prize. Conversely, you might implement gentle penalties, like if a deal is lost because of a preventable data issue, treat it as a learning moment for that rep (and maybe require them to re-train on data entry). Gamification can be fun – a dashboard that scores each team’s data hygiene can spark friendly competition.

Integrate Data Quality into Workflows: Make it as easy as possible for reps to do the right thing. If your CRM allows, mark required fields (but keep them to what’s truly necessary, to avoid annoyance). Use automation (like we discussed with AI) to fill data so reps aren’t doing tedious stuff. Provide cheat-sheets – e.g., a quick reference guide for acceptable abbreviations, or common mistakes to avoid. If reps find the CRM cumbersome to update, they’ll avoid it, so consider their UX. Sometimes investing in a better CRM UI or a third party tool like Scratchpad (which makes updating Salesforce easier) can remove friction and thus improve data quality indirectly.

Regular Training and Communication: Data maintenance isn’t a one-time training at onboarding. Provide refreshers every so often, especially if you introduce a new tool or process. When you do an audit or if you notice a pattern (like lots of contacts missing phone numbers), address it in a team call: “Hey everyone, we noticed phone number completion dropped. Reminder: always add a phone if you can. We’ve added a phone verification tool to help – see the guide I emailed.” Keeping data quality as a recurring topic in meetings or newsletters keeps it top of mind. 

Lead by Example: If managers and executives keep their records up to date, it sets an example. For instance, if the VP of Sales uses the CRM notes and logs activities diligently, the team sees that and is more likely to follow. On the flip side, if leadership bypasses CRM and uses spreadsheets, it undermines the culture. Consistency at the top is key.

Address Data Issues at the Source: Whenever a data issue is found, don’t just fix it – ask “how did this get in here?” If a particular integration is creating bad data (e.g., a form that isn’t mapping fields correctly), fix that mapping. If a certain team or individual consistently has errors, retrain them. The idea is to plug the leaks, not just mop the floor continuously. 

Building a clean-data culture takes time, especially in organizations where bad habits are ingrained. But incremental improvements add up. Celebrate those improvements, and frame data quality as part of the company’s commitment to excellence. Many companies declare slogans like “Clean Data, Clean Deals” or run internal campaigns to make it a positive, team-building endeavor. 

Finally, remember the why – reinforce that this is ultimately about selling smarter and serving customers better. Clean data means sales calls that hit the mark, campaigns that reach the right people, and predictions the company can count on. It’s not just an ops obsession; it’s a revenue driver and a competitive differentiator.

Chapter 9 

Tailored Playbooks: Strategies by Team Size and Maturity

For a Scrappy Startup (Small Team) 

In a small startup or sales team (say 1-10 sales reps, and maybe no dedicated ops person yet), resources are tight and everyone wears multiple hats. Here’s how a startup can maintain good data without a big budget: 

Keep it Simple and Define the Essentials: Identify the absolutely key fields you need for your sales process (maybe email, phone, company, and a short note of qualification). Don’t over-engineer the CRM at this stage with dozens of fields – that will overwhelm the team and lead to incomplete data. Keep the CRM form lean so reps are more likely to fill everything. 

Make Data Entry a Habit Early: Instill habits from day one. For example, require that every new lead from a rep’s prospecting must be entered in the CRM before they reach out (so contacts aren’t living in personal spreadsheets). Founders/heads of sales should model this too. One founder made it a rule: “If it’s not in the CRM, it didn’t happen” – meaning if a rep closes a deal but it wasn’t tracked, they don’t get to claim it fully. This might be extreme, but it set the tone that CRM updates are part of the job, not optional. 

Use Affordable/Free Tools: You might not afford ZoomInfo, but there are free or cheap ways to enrich data. For instance, Apollo and Lusha offer free credits monthly – sign up and use those to get missing emails or phones on a limited basis. Tools like Clearbit have a free “Clearbit Reveal” for forms and a free tier for enrichment up to a certain number of leads. Take advantage of these. Also, use LinkedIn – it’s a manual but free way to research contacts and companies to fill in gaps (LinkedIn Sales Navigator, if budget allows, can be a very worthwhile investment for a small team as both a data source and outreach tool).

Leverage CRM Built-in Features: Many modern CRMs (like HubSpot, which has a free tier) have some built-in enrichment and duplicate checks even in lower tiers. For instance, HubSpot will automatically attempt to fill company info if you have a domain, and alert on duplicate contacts. Use these features – turn them on. If using Salesforce, consider using the free Data.com replacement – Salesforce has “Data integration rules” that can connect to sources like ZoomInfo (if you have a subscription) or others to auto-fill fields. Even if you can’t afford an add-on, use validation rules (like ensure an “@” is in email field, etc.).

Manual Reviews and Cleanups: In a small database, manual work is feasible. Perhaps once a month, the team lead does a quick scan of new records for obvious issues (like weird duplicates or missing fields) and nudges the responsible rep to fix it. Or have a 30-minute session monthly where the team collectively checks the pipeline and account list for any data that looks off. It can be framed as “pipeline grooming” that naturally includes data grooming. 

Outsource Minor Tasks if Possible: If budget permits, consider a virtual assistant or intern who can do periodic data cleaning tasks, like researching and updating a list of outdated contacts. There are firms or freelancers that specialize in data cleansing on an hourly basis – you could have someone spend 10 hours to clean a few thousand records rather than tying up your salespeople.

The startup playbook is about being scrappy: use free resources, build good habits, and nip issues early. The volume of data is smaller, so you have the advantage that a single person can often eyeball it. Taking care of data hygiene from the start will save a young company from the much harder task of cleaning thousands of records later. 

For a Scaling Mid-Market Team 

A mid-market company (say 50-500 employees with a dedicated sales ops/RevOps person or team, and a growing CRM) needs more formal processes. At this stage, data quality issues become more noticeable because the database is bigger and more people interact with it. Here’s a playbook: 

Assign a Data Owner: Ensure someone in RevOps or marketing ops has explicit responsibility for data quality oversight. This doesn’t mean they fix everything themselves, but they monitor the health metrics, coordinate cleanups, and champion data initiatives. It could be part of a CRM manager’s role or a specific “Data Steward” role. Invest in a Data Tool or Two: At mid-market, it’s worth spending on at least one quality/enrichment tool. This could be a data enrichment service (like purchasing a ZoomInfo license or a smaller competitor if budget is lower, or using a service like UpLead for periodic enriches) and a duplicate management tool (many mid-sized orgs invest in something like DemandTools or Cloudingo especially if on Salesforce, to handle duplicates and mass updates safely). These investments pay off in sales efficiency. Also consider an email verification tool subscription (they are relatively inexpensive and can be used ad-hoc to batch-verify a list of emails before a big campaign).

Establish Regular Data Maintenance Routines: For example, set a monthly schedule: Week 1 of the month, ops runs a duplicate report and cleans them; Week 2, run an enrichment on new leads added last month to fill missing fields; Week 3, audit a random sample of records for compliance with standards; Week 4, report data quality KPIs to sales leadership. Whatever the cadence, make it recurring so it doesn’t slip through the cracks during busy quarters. 

Integrate Data Quality in Onboarding/Training: As new sales reps, SDRs, or marketers join, train them on the CRM processes and the importance of data. Provide a playbook or handbook that covers how to input data properly. If you use specific tools (say, everyone should use Apollo for contact sourcing), ensure new hires get accounts and training on that too. Consistency is key as you scale the team. Use Dashboards and Reports to Show Progress: Build a simple “Data Quality Dashboard” visible to ops and leadership, tracking things like overall completeness %, duplicates, etc. This creates visibility and accountability. You can even show it company-wide occasionally – for instance, at quarter-end, show that “we improved data completeness from 80% to 88% this quarter; this correlates with our improved outreach results.” Tying data KPIs to business KPIs in reports can reinforce to all why it matters.

Clean Data Before Big Initiatives: When you’re about to roll out something significant – like a new marketing automation system, an account-based marketing program, or a predictive lead scoring – allocate time to clean the segment of data that will be involved. For example, if marketing is doing an ABM campaign on 100 target accounts, do a thorough check that those accounts’ data is up to date (contacts, industries, etc.) before launching. This ensures new programs start on a solid foundation and sets a precedent that data prep is part of any project launch. 

Consider Data Enrichment as Part of Workflow: At mid-market, you likely have enough volume that manual research is burdensome. So integrate enrichment: e.g., every new lead that comes in is automatically enriched via API, or you schedule a nightly job that enriches anything new added that day. If direct integration is complex, a simpler hack: assign an SDR or intern weekly to run new leads through an enrichment web tool and update them. The key is not letting records sit stale for too long after creation. Monitor User Compliance and Address Issues: With more reps comes more variance. If certain reps or teams lag in keeping data updated, address it. Managers should reinforce expectations. Sometimes, identify why – is the process too slow? Maybe the UI isn’t great, or reps are unsure how to do something. Solve those pain points (perhaps by adding a quick-edit tool or simplifying forms). Mid-sized teams can even convene a monthly “data council” meeting with reps from sales, marketing, CS to discuss data pain points and improvements. 

Overall, mid-market playbook is about formalizing and scaling the practices: using appropriate software tools, having clear ownership, and making data maintenance a routine part of business operations.

For a Large Enterprise Organization 

Enterprises (thousands of employees, large global CRM deployments) face complexity but also have more resources. Here the playbook is about robust governance, specialized roles, and heavy automation: 

Formal Data Governance Program: Enterprises should have a CRM data governance committee or task force. This might include members from RevOps, IT, marketing, and compliance. They set policies (like how often to purge stale leads, standards for data entry, rules for data sharing between systems). They also align CRM data policy with broader company data governance (e.g. ensuring GDPR compliance, handling of personal data, etc. which is critical at this scale). Governance also means defining data ownership across regions or divisions (who “owns” a global account’s data? the US team or the regional team? – such questions need clarity to avoid dupe or inconsistent data). 

Master Data Management (MDM): Many enterprises invest in Master Data Management solutions that create a “single source of truth” for core data like accounts, products, etc. If your company has multiple 

systems (CRM, ERP, marketing DB), an MDM tool can sync and cleanse data across them using identification rules. This is a big investment but pays off by ensuring, for example, that an account is represented uniformly across sales and finance systems. If an enterprise hasn’t yet, evaluating an MDM strategy for customer data is wise once you reach a certain complexity.

Dedicated Data Quality Team or Role: It’s not uncommon for a large org to have one or several people whose full-time job is managing CRM data quality. They might run large-scale merges, import data sets when the company acquires a new lead list, etc. Hiring data specialists (or leveraging consultancies) for periodic deep cleans can be part of the plan. For instance, some enterprises do an annual “CRM data health check” with an outside vendor to audit and improve data, complementing internal efforts. 

Enterprise-Grade Tools: At this level, using top-tier tools is justified. That could mean: 

Enterprise licenses for data providers (ZoomInfo’s higher-tier packages or multiple providers to cover different regions). Data quality software like Informatica Data Quality, IBM InfoSphere, or Precisely which can automate complex validation rules, parse and standardize addresses globally, etc. 

Real-time data integration tools that ensure CRM is fed by other internal systems (for instance, if a customer changes their address in the billing system, it automatically updates CRM). AI-driven platforms custom-tuned to your data: some enterprises might even build machine learning models to predict and flag data errors specific to their business logic. 

Continuous Monitoring and Alerts: Set up automated alerts for anomalies. For example, if duplicate count spikes one week, alert the ops team. If a batch of leads came in with suspiciously similar emails (could be spam/test data), flag it. If the completeness percentage for a certain region drops, notify the regional ops. These automated monitors act like a “data smoke alarm” so issues are caught early. They can often be implemented via the CRM’s workflow or external scripts.