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Business analyst reviewing performance dashboards to address the GTM data latency problem and improve decision-making speed

The Data Latency Problem: Why GTM Teams Lose Pipeline to Stale Insights

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The GTM data latency problem is not about wrong data, it’s about late data. Most organizations assume poor decisions trace back to inaccurate information. The real culprit is timing: intelligence that arrives after the window to act has already closed.

This timing problem is a hidden risk in Revenue Operations and Go-To-Market systems. Their analytics focus more on deep reporting rather than how quickly data can be delivered. Monthly pipeline reviews inform weekly execution calls. SDR teams receive intent signals after buying windows have cooled. Forecasting models reflect historical snapshots instead of live pipeline movement.

A 2024 RevOps survey found that 57% of critical GTM decisions are made before fresh data is even available. A 2025 benchmark of 68 revenue-focused organizations found that 79% of revenue-critical systems were still fed by batch-based pipelines, with a median end-to-end latency of 26 hours.

In B2B environments where buyers move from anonymous research to vendor shortlists in days, a 26-hour intelligence lag is not a minor inefficiency. It is a structural competitive disadvantage.

Why the GTM Data Latency Problem Is Now a Revenue Liability

Modern B2B buyer behavior moves faster than legacy reporting cycles were built to handle. Buying committees form and evolve rapidly. Budget priorities shift within weeks. Leadership changes open or close pipeline opportunities with no advance notice.

A 2024 study of high-intent accounts found that 54% of accounts signaling strong buying intent converted to meetings within 48 hours, and 78% showed no active signal beyond the 72-hour mark. GTM teams often act on outdated data, one to three days old, which means they target buyers who’ve moved on and rely on signals that have already expired.

Research by HBR shows that firms responding to inbound leads within an hour are seven times more likely to qualify those opportunities than those who take even a little longer. Still, most revenue stacks take 8 to 24 hours to provide insights. This delay can be a real issue. For example, a lead generated on Monday might not reach a sales rep until Wednesday, accumulating about 54 hours of delay. Since the chances of converting a lead fall off sharply after just an hour, that backlog turns into missed opportunities.

You can read more about data prioritization in B2B here.

Four Structural Sources of Data Latency in GTM Stacks

The GTM data latency problem is not a single bottleneck. It compounds across four interconnected layers.

Batch Processing Pipelines

Most enterprise data warehouses still run on batch-oriented ETL cycles. Raw events are grouped and processed on hourly or daily schedules. Even when event collection happens in real time, batch transformation introduces hours of processing lag before any signal reaches an operational system. A 2025 audit found the median end-to-end latency across these systems at 26 hours.

Fragmented System Architecture

On average, businesses use 8 to 12 tools for their revenue stack. When a high-intent signal comes in, it needs to go through web analytics, marketing automation, a data warehouse, enrichment services, a scoring engine, a CRM, and a sales engagement platform. With each step taking 30 to 90 minutes, the urgent lead from the start of the day can become outdated by hours. And it gets worse – integration issues don’t just add time; they multiply delays.

ETL Transformation Delays

Transformation logic introduces additional lag through complex joins between Salesforce, CDPs, enrichment providers, and intent platforms. Late-updating reference tables and territory mappings create a patchwork state where some attributes are current and others are weeks old. This partial staleness is operationally worse than plain latency because it produces misleading intelligence rather than a visible gap.

Manual Reporting Cycles

A significant share of business intelligence still runs through human-generated analysis. Analysts export data, clean it in spreadsheets, and prepare slides for leadership. This cycle often adds 8 to 12 hours on top of system latency. One 2025 case study found that 41% of urgent pipeline and forecasting requests were not completed until two calendar days after the triggering event.

Revenue Consequences of Unresolved Data Latency in GTM Systems

Each of the following consequences traces directly to unresolved data latency in GTM systems.

Missed Buyer Windows

High-intent signals lose conversion value rapidly. A 48-hour delay in routing intent-driven accounts was estimated in one RevOps stack to cost 19% of potential pipeline from that cohort. Organizations technically possess the right intelligence. They operationally fail to act before the opportunity expires.

Structurally Inaccurate Forecasting

A 2025 RevOps benchmark found that models updated once per week had 16 to 22% higher error rates compared to models ingesting data within 12 hours. Models trained on daily snapshots overestimated close rates by an average of 13% because they lagged short-cycle velocity changes. These errors cascade into quota allocation, territory design, and budget decisions built on a pipeline state that no longer exists.

Operational Waste Across GTM Teams

Stale data forces reactive behavior. Sales reps spend hours researching accounts based on last week’s profiles, unaware of executive changes or competitive tool adoptions that occurred 48 hours prior. Customer success teams identify churn risk after intervention windows narrow. The waste is invisible in individual workflows but accumulates into measurable capacity loss across the organization.

Framework: The Intelligence Velocity Matrix

Not all data requires real-time processing. Solving the GTM data latency problem starts with identifying where latency directly degrades revenue outcomes and where batch processing remains sufficient.

A practical model evaluates each data flow across two dimensions: decision frequency and value decay rate.

Tier 1: Real-Time Critical. High frequency plus fast decay. Examples include inbound lead routing, buying intent signals, and product trial engagement. These require sub-15-minute latency and justify event-driven streaming infrastructure.

Tier 2: Near-Real-Time Operational. High frequency plus slower decay. Examples include account-level engagement scoring and contact enrichment. These benefit from 15 to 60-minute refresh cycles through incremental processing or change data capture.

Tier 3: Strategic Analytical. Low frequency, used for planning. Examples include quarterly business reviews and territory design. Daily or weekly batch processing is appropriate here.

This tiering prevents over-engineering and aligns infrastructure cost with revenue impact.

How to Build a Low-Latency GTM Intelligence System

Event-Driven Architectures for Critical Signals

Instead of waiting for those scheduled syncs, event-driven architectures catch signals right when they happen and instantly send them off. So, when a prospect visits a pricing page or hits a product usage milestone, an event record gets sent through something like Apache Kafka. Then, downstream systems can grab that info in seconds, not hours. In one case, a RevOps team used this method to assign enterprise accounts with sudden interest spikes to a dedicated SDR pod within minutes. This led to a 23% higher close rate compared to accounts processed through batch pipelines.

Incremental Processing as a Middle Path

For organizations not quite ready for full streaming, incremental processing offers major upgrades without an extensive revamp. Rather than refreshing whole datasets nightly, systems now update only what’s changed, every 5 to 15 minutes. Plus, platforms like Snowflake, Databricks, and BigQuery make this easy via change data capture and materialized views. So, a firm dealing with 100,000 daily CRM updates could go from that big 24-hour lag to just 15 minutes – all with minor infra tweaks.

Automated Alerting and Decision Triggers

Low latency only matters when paired with automation. Top performers link smarts right into their workflows, sending real-time notifications for intent spikes, kicking off plays when pipelines lag, and routing stuff automatically based on fit. Advanced setups take it up a notch too. They can pause campaigns or switch to retargeting when conversions slow down, and adjust the timing and channels based on actual engagement.

Practical Recommendations for RevOps Leaders

First, audit the current latency and then make those infrastructure changes. Document when data hits source systems, when it gets to GTM platforms, and when decision-makers see it. You’d be surprised how many delays are lurking around that nobody tracked before.

So rank the high-value data flows first. Give each one a score based on decision criticality, market dynamics, and latency tolerance. After that, focus on upgrading streaming or incremental processing for the top two to four data flows. Also, aim to get latency down from 24 hours to less than an hour first. Then, you can target reducing times from one hour to under one minute.

Set clear goals for how fresh your data needs to be. For top performers, key revenue signals should hit action inlets in under 10 minutes, lead scoring updates within 30, and opportunity data sync’d up in an hour. Companies acting ten times slower are working with stale info that could mislead decision-making.

Also, keep an eye on how long it takes for an event to affect sales actions. Treat end-to-end latency as a crucial performance indicator right next to pipeline and conversion rates.

Conclusion: Speed Determines Whether Insights Have Value

In modern GTM systems, accurate information alone is no longer sufficient. Timing determines whether intelligence creates competitive advantage or becomes operational hindsight.

The future of B2B intelligence will not be defined solely by data quality or data volume. Closing the GTM data latency gap will define which organizations convert signals into decisions.

Insights that arrive too late are, in every practical sense, indistinguishable from no insights at all.

Digital analytics dashboard visualizing performance metrics and insights related to B2B data cost efficiency and revenue optimization

The B2B Data Cost Efficiency Problem: Why Data Spend Does Not Correlate with Revenue Outcomes

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B2B data cost efficiency has become one of the most overlooked levers in modern revenue operations. The average B2B company now spends $178,000 annually on data solutions and subscriptions; 34% more than three years ago, while pipeline velocity has dropped 8% and conversion rates have flatlined.

In a benchmark study conducted in 2025 among 150 data-driven B2B organizations, it was discovered that only 28% of firms which had a 42% rise in data expenditure were able to improve their revenues per sales rep. This is not an execution failure. It is a structural one.

The B2B data market has expanded into a fragmented ecosystem where the average company manages 15 or more separate data vendors. When 72% of sales teams pay for data features they never activate and 76% of RevOps organizations do not track revenue impact by data source, the problem is not a lack of information. It is a lack of efficiency.

The core issue: organizations optimize for data volume while ignoring data utility, activation rates, and alignment with actual go-to-market (GTM) execution.

Why Higher Data Spend Kills B2B Data Cost Efficiency

Each of these failure modes degrades B2B data cost efficiency in a distinct way.

The Three Recurring Failure Modes

Higher spend concentrates around three recurring failure modes, each compounding the others.

Redundant vendor purchases inflate costs without expanding coverage. Most revenue organizations run six to twelve data tools simultaneously: a primary database, enrichment API, technographic layer, intent platform, email verification service, and point solutions for specific verticals. These platforms frequently pull from the same upstream data brokers. A 2025 analysis of 40 sales-stack audits found that 73% of companies had at least two independent enrichment vendors, and 56% had two or more intent platforms. Yet only 31% could confidently identify which vendor drove materially better outcomes.

One middle-tier SaaS firm with Cognism, ZoomInfo, and Apollo running simultaneously saw that 47% of its target account contacts were common to all three platforms. The combined annual spend on these tools totaled $143,000. The unique coverage achieved because of this overlap: 8%. The additional spend amounted to $67,000 annually with zero pipeline value.

The Utilization and Alignment Gap

Low activation rates silently destroy ROI. The industry average for database utilization sits at 12% to 18%. Organizations purchase enterprise licenses based on projected coverage needs but activate only a fraction. A healthcare technology firm was able to quantify this in great detail – its $92,000 yearly investment in enrichments equated to 840,000 API calls. Post-enrichment analysis revealed that 340,000 API calls were enriching contacts who were scoring below the client’s minimum ICP threshold, and 190,000 calls were enriching contacts tagged “do not contact” or in dormant segments for 18 months. Utilization: 37%

GTM misalignment converts quality data into expensive noise. Data purchases often proceed independently of strategy changes. Sales teams buy databases optimized for outbound at scale while the company pivots to account-based strategies. Marketing acquires intent signals for accounts that SDRs are not assigned to. When data outputs live in dashboards instead of playbooks, routing rules, or performance targets, spending on data without changing behavior changes nothing.

Volume vs. Accuracy: The Data Efficiency Trade-Off That Shapes Pipeline ROI

The instinct to purchase larger databases is understandable but consistently counterproductive. Volume creates an illusion of capability while masking the cost of poor quality.

Consider two scenarios. A company with 80,000 CRM records spending $90,000 annually on data, with a typical usability rate of 35%, pays $3.21 per usable record. A smaller database of 40,000 records with 55% usability costs just $2.50 per usable record. Volume is not value. It is a larger surface area for the same underlying problems.

This trade-off intensifies with intent signals. Intent data is one of the biggest drivers of modern data spend and one of the least efficiently used. Organizations ingest thousands of unstructured third-party signals weekly without internal filters. An enterprise software firm might receive an alert that a target account is searching for “cloud security,” but if the provider cannot identify which business unit is executing that search, sales teams cold-call generic contacts. The result is low conversion, wasted capacity, and data spend that drives activity without driving revenue. You can read more here.

High-accuracy, lower-volume datasets consistently outperform high-volume generalized databases in pipeline contribution. One financial services company spent $67,000 on an 18,000-contact healthcare vertical database. Six months later, it generated $180,000 in pipeline from 11 accounts, all of which were already known and engaged. Meanwhile, their underfunded enterprise motion produced $4.2M in pipeline from a $12,000 investment in executive intent data targeted at existing customers in expansion phases. Precision won by a factor of 35 on pipeline ROI.

The Data Activation Matrix: Measuring B2B Data ROI Across Every Vendor

Most organizations lack formal efficiency metrics for data investments. That gap prevents optimization. Three metrics, tracked together, that form the foundation of any B2B data cost efficiency framework and expose where value erodes.

Cost Per Usable Record (CPUR) reveals the true price of quality. The formula accounts for total investment against records that actually meet accuracy and delivery standards:

CPUR = (Tool Cost + Labor Cost + Decay Cost) / (Number of Records * Usability Ratio)

An organization investing an extra $14,000 in tools for improving data quality but lowering the cost associated with labor by $10,000, as well as boosting usability from 55% to 78%, will lower its cost per record usage from $2.50 to $1.89. This is compounding data infrastructure.

Cost Per Activated Account (CPAA) measures GTM alignment. For an account to count as activated, it must enter a meaningful sales motion, not just exist in a list or scoring dashboard. This metric exposes inefficiencies where data is purchased at scale but activation rates remain low, or where one vendor shapes actual plays while another is ignored despite similar contract value.

Pipeline Impact Per Dollar of Data Spend is the most strategic metric and the hardest to calculate. It requires tagging pipeline by data source and comparing performance against a baseline. Organizations that attempt this consistently find that a minority of vendors drive the majority of incremental pipeline, and that some high-cost “table-stakes” products produce no measurable lift when removed.

A useful triage framework maps vendors across two dimensions: value (pipeline generated per dollar) and efficiency (CPUR or CPAA). Vendors that are low-value and low-efficiency are candidates for elimination. High-value but low-efficiency vendors merit closer scrutiny and usage optimization. High-value, high-efficiency vendors deserve consolidation of spend and negotiating leverage.

Four Shifts That Recover 25-40% of Wasted Data Spend

Organizations that improve B2B data cost efficiency follow a consistent approach.

Focus on a core-with-specialties approach. Replace the patchwork of multi-vendor systems with a layered approach consisting of one general vendor that provides coverage overall, plus one or two specialty vendors with differentiating capabilities such as deep verticals, international reach, or intent signals. By focusing on two vendors as opposed to seven, a logistics software company was able to lower its costs from $156,000 to $89,000 while improving contact uniqueness coverage by 12%.

Route inbound leads through a low-cost validation layer first: check for valid email format, filter personal domains, remove duplicates. Only verified enterprise leads proceed to premium API enrichment. This ensures high-cost API credits are spent on viable, sales-ready opportunities rather than junk sign-ups or personal email addresses.

Build performance-based vendor evaluation into renewal cycles. Measure accuracy rate, data freshness, and pipeline correlation for every vendor, updated quarterly. Vendors scoring below a defined threshold enter performance review. Vendors that cannot demonstrate measurable pipeline contribution should not receive automatic renewals. This approach prevents incumbent inertia, the tendency to renew underperforming vendors due to switching friction.

Embed data into GTM workflows rather than adjacent to them. Scoring, routing, sequencing, and playbooks must be driven by data, not informed by it after the fact. A 2025 case study found that a 25% increase in the fraction of SDR time spent on data-driven accounts led to a 19% increase in pipeline per salesperson with no change to the underlying data budget. The efficiency gain came from usage, not volume.

Data Cost Efficiency Is the GTM Competitive Advantage

The modern GTM environment is not short on data. It is short on economically effective intelligence systems.

Organizations that keep increasing their number of records, suppliers, and data signals without getting better at activating and making decisions based on data will experience increasing expenses and inconsistent commercial results. Organizations focused on B2B data cost efficiency; not just data volume, consistently recover 25-40% of wasted spend.

The recovered capital, often $50,000 to $150,000 annually, funds higher-impact revenue initiatives with measurable ROI.

Data cost efficiency is not a procurement metric. It is a revenue strategy. The question is not whether your data budget is large enough. It is whether your data investment is actually changing decisions, improving execution, and driving pipeline that closes.

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B2B Data Procurement Framework: How to Evaluate, Compare, and Select the Right Data Sources

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Most B2B organizations believe they have a data problem. In reality, they have a B2B data procurement problem.

Data is often purchased reactively. A VP of Sales needs more leads. A budget is carved out. A contract is signed based on total record count. Six months later, the investment sits unused. The accuracy was lower than promised. The refresh cycle was too slow. SDRs burn through premium databases only to find 48% bounce rates and conversion rates below 2.5%.

The cost is severe. The loss incurred due to poor data quality by firms stands at an annual $12.9 million, mainly due to inefficient outreach efforts, reduced sender credibility, and low productivity. The sales force ends up wasting 500 hours each year verifying data integrity. This amounts to 25% of the sales effort that should have been utilized for creating a sales pipeline.

This is not an issue created by the vendor; rather, it is the fault of procurement practices. Firms that procure their data well can create efficient and highly performing go-to-market systems.

What B2B Data Procurement Actually Involves

Strategic B2B data procurement begins with understanding the vendor landscape. Not all providers serve the same purpose.

Static database providers sell pre-built contact lists organized by firmographics, industry, and job function. They excel at providing broad coverage but struggle with maintaining real-time accuracy. Their business model incentivizes volume over verification.

The main aim of enrichment tools is to enrich current CRM database entries with additional data fields like contact emails, phone numbers, and technographics. This tool works effectively when there is some available data that needs to be completed, especially when dealing with inbound marketing leads.

An intent or signal solution detects user behavior related to actual purchase intentions. It monitors web visits, downloads, technology usage patterns, and employment trends. Modern tools combine firmographics, technographics, and intent signals to provide comprehensive account intelligence. The team can focus on contacting customers with higher purchase intentions.

The procurement decision extends beyond vendor type to engagement model. One-time database purchases may seem cost-effective initially, but they ignore decay reality. The contact decay rate is roughly 2.1% per month or 22.5% per year. E-mail addresses have an even higher decay rate, ranging from 23% to 30% per year. The accuracy rate of a data set can fall from 95% at inception to just 70% after twelve months.

Continuous data models with refreshes ensure alignment of incentives with data quality by keeping contacts up-to-date as organizational restructuring takes place.

Key B2B Data Vendor Evaluation Criteria

Evaluating data vendors calls for going beyond simplistic measures to grasp the underlying dynamics that distinguish top-tier vendors from bottom-rung vendors.

Coverage vs. Accuracy: The Core B2B Data Trade-off

While a vendor offering 500 million contacts seems very appealing, it turns out that 70% of businesses find their marketing and sales efforts hindered by inaccuracies in their data sets. Premium data vendors provide contacts with 97% accuracy or more, whereas industry standards hover at 50%.

Measure usable contact rates, not the number of contacts. With 97% accuracy, 50,000 contacts become 48,500 contacts that work. With 50% accuracy, 200,000 contacts yield only 100,000 usable contacts. Economics are clearly on the side of accuracy over sheer quantity when the cost of wasted effort is considered. You can learn more about it here.

Data Refresh Rate: Why It Dictates Campaign Success

With decay rates above 2% per month, annual updates ensure outdated data throughout the bulk of the subscription period. Quarterly refresh rates are essential, with monthly or even real-time validation required for critical segments. Leading vendors now offer continuous enrichment that updates records as changes occur rather than on fixed schedules.

Data Source Transparency Signals Vendor Reliability

How does the vendor collect and verify data? Reputable providers disclose their methodologies: public sources, partnership networks, human verification processes, and algorithmic validation. Opacity often indicates compliance issues or data aggregation from questionable sources. Ask for sample data matched against your existing CRM to benchmark accuracy before committing to large purchases.

Geographic and Industry Coverage: Verify Depth Before Buying

Broad databases often sacrifice depth in specific markets. If your ideal customer profile includes healthcare organizations in Germany or financial services firms in Southeast Asia, verify the vendor’s actual coverage in these segments. A dataset strong in North America may have limited coverage in APAC.

GDPR and Data Compliance: Non-Negotiable Standards

Following GDPR and CCPA, 45% of B2B companies have erased more than 20% of legacy data, while 89% of B2B marketers have marked compliance and privacy as their high priorities. The non-compliance penalty includes fines amounting up to 4% of the annual global turnover. Ensure your vendors have compliance and privacy practices in place for collecting data.

Common B2B Data Procurement Mistakes to Avoid

The gap between data spend and data value often traces to predictable mistakes that compound over time.

Choosing based on volume, not quality, drives poor vendor selection. Organizations prioritize cost per contact over usable contact rate, then wonder why campaigns underperform. High-accuracy providers delivering 97% or higher verification actually cost 16.5% less overall than low-accuracy alternatives despite charging higher per-contact prices. This counterintuitive economics stems from dramatically reduced waste and 66% higher conversion rates.

Ignoring decay and refresh cycles guarantees degrading quality. Purchasing annual database licenses without continuous updates means your database loses value the moment you load it. Organizations need ongoing verification rather than static purchases.

Overlapping vendors with redundant data creates budget waste. Many organizations pay multiple vendors for the same underlying contact records, simply repackaged through different platforms. Multi-source strategies make sense when vendors provide genuinely differentiated data. But redundant coverage of the same contacts offers no strategic value. Without a clear vendor hierarchy, companies often accumulate overlapping tools that increase complexity and cost.

Lack of internal validation removes accountability. Organizations that skip pre-purchase testing have no baseline for vendor performance. Sample a subset of vendor data and verify accuracy against known contacts. Track bounce rates, response rates, and conversion rates by data source to quantify which vendors deliver ROI.

Building a Strategic B2B Data Procurement Stack

Effective B2B data procurement entails gathering complementary capabilities, not just depending on one vendor.

The complete stack should have a primary contact database for extensive reach, an enrichment tool to fill in the details of incoming leads, an intent data vendor for focusing on accounts that demonstrate purchasing intent, and specialized vendors for difficult-to-reach segments. This ensures redundancy in areas of importance without redundant capabilities.

Use a primary vendor for baseline contact coverage, especially those with high accuracy in key market segments. Use other vendors to address deficiencies in specific niches or regions.

Many teams now use platforms that combine real-time company insights, technology usage tracking, and buying signals into unified account intelligence. These next-generation solutions reduce the need for separate point solutions by providing comprehensive data within a single platform.

Map your data stack to real-world use cases. If your sales development representatives need email and phone verification for outbound prospecting, make sure that your main vendor is top-notch when it comes to contact quality. If you need account-based marketing data, incorporate technographics and intent data to help prioritize accounts without creating another contact list.

Measuring B2B Data Procurement ROI Post-Purchase

Data procurement doesn’t just finish once the contract is signed. Continuing the measurement is what differentiates the successful campaigns from the costly errors. Keep an eye on the usability rate. Among the contacts you have purchased, what percentage is truly usable? Identify the hard bounces, the invalid phone numbers, and the outdated job titles. In case you are buying 10,000 contacts but only 5,000 can be used, your actual cost per contact has gone up by 2 times.

Tracking Activation and Pipeline Contribution

Monitor activation rate in campaigns. How many purchased contacts make it into actual campaigns? Low activation rates signal either over-buying or poor ideal customer profile alignment. Segment activation rates by source to identify which vendors provide campaign-ready contacts versus those requiring extensive cleaning before use.

Measure pipeline contribution. Measuring ROI total will give you a cost per data dollar generated by pipeline tracking, as well as the multi-touch attribution (which provides an equal share of credit from each source) so you can see what vendors drove the closed deals vs. what vendors drove activities but did not impact your revenue. Track metrics by source of data, including the number of meetings booked, total number of opportunities created, and total number of deals closed.

B2B Data Procurement as Competitive Advantage

The B2B market will continue to grow at an enormous rate, reaching $863.2 million dollars in 2024 and projected to grow to over $3.2 billion dollars by 2030. With the explosive growth of the B2B market, we see a fundamental change in how companies purchase customers and intelligence about the marketplace.

Companies that treat data procurement as a strategic discipline are able to capture significant value. They build multi-sourced architecture that has been proven and continuously refreshed. They demand transparency and rigorously measure their vendors’ performance while aligning their data purchasing decisions with their respective go-to-marketing initiatives.

Companies around the world are struggling with collecting accurate data on their customers based on reports indicating that 94% of companies do not believe their customer lists are accurate; therefore, developing data that is valid and accurate becomes differentiators between competitors. By having good data as a foundation for success, we achieve on-target marketing by using good, accurate quality of contacts that provide more opportunities for generating greater revenues through an increase in successful responses.

Building a Long-Term B2B Procurement Strategy for Data Quality

Your method for obtaining good-quality data will dictate whether or not you travel down either of two paths: a good data path that will allow for the creation of positive growth through the strategic integration of good-quality data into your systems; or the development of bad contacts that will likely result in negative deliverability rates, wasted marketing resources directed towards blocked senders, and negative perceptions from potential customers and businesses when evaluating your products/services while offering their own products or services.

To develop a long-term strategic procurement strategy when acquiring good-quality data through different vendors, keep in mind several critical areas that you need to address;

(1) When verifying contact information, ask detail-oriented questions to ensure that the vendor suppliers are utilizing the best verification methods;

(2) Verify how frequently you update the supplier’s data and the accuracy of the supplier’s data for key verticals;

(3) Analyze whether or not the supplier’s data integrates with your CRM systems;

(4) Validate how soon you conduct contact verification, and

(5) Ensure compliance with data processing regulations. In addition, use pilot samples before committing to utilizing the supplier’s data, monitor performance benchmarks after purchase, and think of Data as an Infrastructure Asset that you will continue to support.

    Businesses which are successfully accelerating their B2B sales growth understand how the compounding effects of poor data quality affect their overall growth trajectory. As a result, developing a procurement process is critical in determining how strategically acquired good-quality data via vendor partners will provide the greatest opportunity for developing long-term successful business operations.