Business data is a major investment for any company, and for good reason. Data is the fuel for your company’s revenue engine, which means that only the highest-quality data will deliver the results today’s markets demand.
Of course, many B2B data providers claim to have “the best” data. But how do you determine which providers are actually the best fit for your unique business needs? Experienced partners can make all the difference.
“This process goes far beyond simply checking boxes on a feature list,” says Joseph Santos, director of the data advisory team at ZoomInfo. “It’s about finding a true partner who can elevate your data strategy and contribute to your organization’s success.”
Like any business product or service, B2B data providers are not a one-size-fits-all solution. Whether a data vendor is a good choice for you depends on the type of data you need, the industry you work in, their feature set, your price point, and much more.
This guide will walk you through the steps and criteria for evaluating third-party data vendors, so you can make an informed decision and find the right partner.
1. Check for Alignment with Your Business Goals
Every company has a unique set of goals and requirements for their business data. Unfortunately, not every B2B data provider will be able to meet your particular needs. Before getting into the specifics of data sourcing and quality, you first need to ensure that there’s a basic alignment between the data vendor and your business goals.
According to Santos, evaluating this fit means diving deep into your specific use cases, understanding your unique business challenges, and verifying that the vendor’s data can directly address your needs.
“It’s not about having the most data, but having the right data that aligns with your objectives,” he says.
As you begin evaluating potential B2B data providers, make sure to clearly define your business goals and that their data aligns with your business use case.
Here are a few important factors to consider:
Types of Data
Think about the different types of data you and your team need to access. Some data providers only supply contact information or firmographic data for companies and accounts, while others provide technographic data and demographic data. Get a clear understanding of the types of data each provider offers and determine whether or not they have what you need.
Data Accuracy
B2B data decays quickly — as fast as 70.3% per year. Because of this, it’s important to ask vendors how often their data is updated, how they ensure accuracy, and whether or not they have a policy about handling inaccuracies. Make sure that their answer is a good fit for your data quality needs.
Depth and Breadth
Assess if the vendor provides data that covers the depth (wide range of data points) and breadth (coverage of companies and contacts that are relevant) for what you need. Do they have the data for the industries and data points you want? Do they go deep enough on companies and contacts to be the right provider for you?
2. Make Sure They Check the Right Boxes
Once you’ve found data vendors that fit your specific goals, take a closer look to make sure they meet the qualifications of a good quality data provider.
Here’s a list you can use to guide your evaluation:
Data Vendor Evaluation Checklist
- Data Sources: Take a good, hard look at how vendors collect their data. What is the diversity of sources? What are their collection methodologies? How they source their data has a direct effect on the quality, consistency, and compliance of what you’ll be getting from them.
- Data Quality and Accuracy: Set clear benchmarks for data quality based on your needs and industry standards, and the balance of data accuracy with the scale of data you need. Ask if the vendor has a policy about handling inaccuracies.
- Data Freshness: If you want data to be reliable, make sure it’s constantly refreshed. Discuss data refreshes and update schedules with the vendor to understand their data freshness. Make sure to distinguish between static and dynamic data sets.
- Match Rate: Your first-party data is only as valuable as your ability to understand it and make it actionable. You need to be able to match it to the vendor’s data for a more complete story. As you evaluate data vendors, verify how well your first-party data can match their data universe.
- Sample Evaluation: Without a sample, it’s nearly impossible to judge the quality of a vendor’s data. Ask for a free trial or data sample to give it a test drive and make sure it is accurate and appropriate for your use case.
- Scale and Future Growth Potential: Evaluate the current volume of a vendor’s database. Is it large enough for your continued growth? Will the vendor be able to scale with your future needs?
- Reputation and Track Record: Look for references, past clients, and customer experiences, particularly with organizations similar to yours.
- Integration Capabilities: Ensure the data meets your own ingestion capabilities and determine how much assistance a vendor offers during data integrations.
- Privacy and Compliance: Verify that the vendor is compliant with relevant data protection regulations and industry standards like GDPR, CCPA, ISO 27701, SOC 2 Type II, and more.
- Customer Support and Service: Make sure you understand the level of ongoing support you can expect from the provider and whether it matches your expectations and needs.
3. Deep Dive into Data Sourcing and Quality
Once you’ve ensured a vendor’s alignment with your business goals and adherence to best practices, it’s time to dive deeper into data sourcing and quality. Understanding the nuances of how their data is acquired, updated, and aggregated is critical.
Santos says, “Insight into the vendor’s methodologies is crucial for assessing data quality, reliability, and freshness. It allows you to anticipate potential limitations and plan for how the data will integrate with your existing systems and processes.”
Here are the key aspects to evaluate:
Unique IDs for Entities
Unique identifiers keep entities (such as companies, websites, or users) distinct within your dataset. When you’re evaluating and integrating third-party master data, a best practice is to use their unique entity identifiers as a foundational element to your own internal data processes.
This practice significantly enhances data consistency and streamlines data integration across your various internal systems, such as your CRM, Master Data Management (MDM), and Enterprise Resource Planning (ERP) platforms.
When evaluating this aspect, consider:
- Stability, Persistence, & Coverage
- Ensure IDs remain constant over time, even if other attributes (such as job role) change.
- Verify their policy on reusing or retiring IDs.
- Granularity & Cross-Reference Capabilities
- Confirm that the level of ID granularity (such as company, location, or individual) matches your needs.
- Understand how the vendor handles corporate hierarchies.
- Evaluate how easily you can map the vendor’s IDs to other industry-standard identifiers and cross-reference them.
- Integration, Scalability, & Historical Tracking
- Review the vendor’s API capabilities for ID lookup and data retrieval.
- Assess how easily you can integrate the vendor’s IDs into your existing data flows and processes.
- Ensure the ID system can accommodate your data volume needs, both current and anticipated.
- Understand how the vendor handles IDs in the case of mergers, acquisitions, and divestitures.
- Check if the vendor provides a change log or history for each ID.
Confidence Level Gradations
Data confidence gradations help you make informed decisions about data usage, prioritize data quality efforts, manage risks associated with data-driven decisions, and ensure compliance with relevant regulations.
Using confidence level gradations allows for more sophisticated data governance and quality management practices. Making sure that a vendor’s confidence gradations align with your company’s data standards is key to maintaining the consistency and integrity of data across your business.
Key aspects include:
- Scoring System
- Look for transparent, comprehensive scoring methodologies.
- Granularity
- Prioritize attribute-level confidence scores for precise data quality management and more nuanced decision-making.
- Integration & Customization
- Evaluate how easily you can integrate and customize confidence scores in your data management processes.
- Look for API support that includes confidence scores in data queries.
- Consider vendors with customization options so you can adjust confidence scores based on your experience with the data.
Fields & Attributes
Clearly define your “must have” and “nice to have” attributes based on your business needs. Which are essential? Which are beneficial, but not critical? By prioritizing attributes you can effectively compare each vendor’s overall value proposition.
Evaluate:
- Attribute Mapping
- Create a detailed list of all of your “must have” and “nice to have” attributes.
- Map vendor attributes to your requirements, noting any gaps.
- Consider creating custom taxonomies to map to a vendor’s taxonomies for data attributes like industry and sub-industry.
- Completeness Analysis
- Request fill-rate statistics and compare the completeness of your must-have attributes.
- Compare vendors based on the completeness of your must-have attributes across their entire database.
- Evaluate completeness within certain segments that are relevant to your business (such as industries, company size).
- Test a sample of the vendor’s data to ensure you receive actionable data.
- Accuracy Verification Process
- Request details on how the vendor measures accuracy.
- Conduct sample tests to verify a vendor’s accuracy claims.
- Assess update frequency and verification processes.
Matching Capabilities
Vendor matching is crucial for effectively integrating third-party data. Whether you use it as a primary method or a supplement to a more comprehensive match/merge process, the quality of a vendor’s matching capability can significantly affect the value of their data.
Focus on:
- Algorithm Sophistication and Flexibility
- Look for vendors that offer multiple matching methods (deterministic, probabilistic, fuzzy).
- Consider how the vendor handles incomplete information and variations in data format and spelling.
- Quantitative Completeness
- Compare data vendors based on the level of detail of their match scores.
- Look for vendors that offer attribute-level match scores.
- Check whether the vendor provides clear confidence levels or probability metrics for each match.
- Performance Metrics
- Evaluate potential vendors based on their matching accuracy rates, including precision and recall metrics.
- Consider the scale at which the vendor can perform matching operations efficiently.
- Look for vendors that continuously improve matching results based on customer feedback.
Hierarchy Framework
A vendor’s company data hierarchy framework impacts how well the data can support various business use cases. There are three traditional hierarchy types: commercial, legal, and domain (url/brand). Each of these serves a different purpose to align with specific business needs.
Ideally, a data vendor should closely match your definition of account and company clustering. If not, they should offer flexibility to align their hierarchy with your first-party data and definitions.
Evaluate:
- Hierarchy Framework Comprehensiveness & Flexibility
- Determine which of the three hierarchy types (commercial, legal, domain) the vendor offers and its flexibility to adapt to your use cases.
- Assess the depth and breadth of hierarchy structures, including the number of levels, global coverage, and the ability to cluster smaller companies within a larger company conglomerate.
- Consider the vendor’s ability to customize or blend hierarchy models to match specific business needs.
- Linkage Capability, Quality, & Maintenance
- Examine the vendor’s processes for building and maintaining hierarchy relationships.
- Assess how frequently data is updated and how quickly real-world corporate structure changes (like mergers, acquisitions, and divestitures) get reflected in the data.
- Evaluate whether the hierarchy links are based on legal ownership, go-to-market focus, or domain/brand focus.
- Integration & Alignment
- Evaluate the vendor’s tools for mapping their hierarchy to your account structures.
- Assess how well the vendor’s solution can align hierarchies with large volumes of first-party data.
- Review how the vendor resolves discrepancies between their hierarchies and your existing data.
4. Look Ahead to Data Partnership
In addition to evaluating a data vendor’s current offerings, consider the potential for a long-term partnership. “Perhaps the most important aspect of vendor evaluation is how the vendor can become an extension of your team — a true partner in your data journey,” Santos says.
But how do you find a vendor who can be a long-term partner?
Santos’ advice: “Seek partners who provide guidance, share industry best practices, and offer delivery consulting. These vendors invest in your success, bringing expertise that can help you navigate complex data challenges and uncover new opportunities.”
To determine a vendor’s partnership potential, evaluate:
- Expertise: Determine the expertise of the vendor’s team including domain knowledge, technical skills, and industry experience.
- Availability: Assess whether these experts are readily available for consultations, workshops, or ongoing support.
- Collaborative Approach: Look for vendors that can work collaboratively with your teams with an understanding of your unique challenges and objectives.
- Customization Capabilities: Gauge their ability to provide customized solutions and recommendations.
- Past Performance: Weigh their track record in co-creating data strategies and implementation plans with their clients.
- Ongoing Support: Consider the vendor’s approach to ongoing support, training, and knowledge transfer.
As you assess vendors, remember that their data is only a part of the equation. “Take a step back from the technical details and consider the bigger picture,” Santos says. “Which vendor is truly committed to being a partner in your success?”
Finding Your Best-Fit Data Vendor
Choosing the right data vendor is a major strategic decision for your business. By following this evaluation guide, you can ensure that the vendor you select meets your current needs and supports your future growth and data-driven initiatives. By focusing on fit for purpose, data methodology, and partnership potential, you ensure that you’re entering into a valuable partnership that enhances your data strategy and drives business success.
“The right data vendor provides a pathway to achieving your business goals. They become an integral part of your data strategy, offering insights, support, and expertise,” Santos says.
Here at ZoomInfo, we know that the more time wasted on bad data, the slower your revenue stream flows. That’s why we process more than 1.5 billion data points daily — collecting, verifying, publishing, and updating millions of contact and company profiles — and have a team of researchers to verify data in real time.
Experience the difference our commitment to data quality makes in driving your success. Request a demo today.