In this episode of the SpringDB Data Exchange, John Kosturos sits down with David Finkelstein, Chief Executive Officer of BDEX, to talk about what separates an identity graph you can trust from one you simply inherited.

BDEX started life as a big data exchange, built to help data companies monetize what they had. The team quickly ran into a problem that changed the company: every supplier anchored identity differently, and without a reliable way to connect those records, nothing downstream worked. Identity stopped being plumbing and became the product.

David explains how BDEX maps online signals, devices, and touch points back to offline consumer records, linking email addresses, mobile IDs, connected TV IDs, and household IP addresses at the person and household level. He also makes a case that should change how you evaluate any graph: the deterministic versus probabilistic question misses the point, because a bot using a stolen identifier can create a link that is perfectly deterministic and completely false. What matters is validation, disqualification, and knowing the origin of every record.

The conversation covers ad fraud and why advertisers stopped treating it as a cost of doing business, the volume of signals behind every identity decision, state privacy regulation, the BDEX hybrid pixel, keyword based intent and why it beats fixed category taxonomies, activation across channels, the weekly graph rebuild, and how the data is delivered.

BDEX is a SpringDB partner, and BDEX audiences are syndicated on the SpringDB Data Marketplace. If you want to evaluate BDEX identity, audience, or intent data, blend it with other consumer and business sources, or host it somewhere that does not punish you for running long lookbacks, SpringDB can help you scope it and put it to work.

WHO SHOULD LISTEN

Advertising and marketing platforms running cross device targeting, who need an identity graph that holds up at the point of match.

Agencies and brands buying audiences, who want to understand how much of a campaign is reaching real people rather than bots.

Data providers holding data tied to a single identifier type, who want to link it to other IDs so it can be activated more widely.

Data and platform engineers evaluating file formats, delivery paths, and refresh cadence before wiring a graph into production.

Go to market and revenue teams connecting consumer identity to business records, so buyers can be reached beyond a single work email address.

Data buyers and procurement teams who want to ask better questions about where a data set came from, how it is validated, and what happens to identifiers that fail those checks.

Guest: David Finkelstein, Chief Executive Officer, BDEX
 Host: John Kosturos, SpringDB

People Data Labs is one of the most widely used workforce data providers in the market, and most end users have never heard of it by name. That is by design.

In this episode of the SpringDB Data Exchange, John Kosturos sits down with Ben Eisenberg, CEO of People Data Labs, to explain how a company with no software product of its own became the data foundation behind platforms across recruiting, sales, marketing, and investment research.

Ben walks through the three joinable datasets that power People Data Labs, person, company, and job postings, all connected on a single persistent company identifier. He explains why not having a software product is a strategic advantage, how job postings quietly reveal hiring signals, budgets, and the technologies a company actually uses, and why every company is becoming a data company as AI lowers the barrier to building in house.

The conversation also covers the practical decisions every data buyer faces: API versus flat file delivery, consumption versus all you can eat pricing, delivery through Snowflake, Databricks, and S3, and how to judge data quality through golden records, record linkage, and benchmarking. Ben closes with an honest look at where global coverage is strongest, where it thins out, and why the use case decides everything.

In this episode:

• Why pure data beats owning a software product
• How person, company, and jobs data become joinable
• The hiring signals hidden in job postings
• API versus flat file delivery, and when to switch
• How data pricing actually works
• How to evaluate data quality and global coverage

About People Data Labs:
 People Data Labs is a workforce data provider that builds high quality person, company, and job posting datasets and delivers them to the platforms and teams that build on top of them. Its core resume dataset covers roughly 800 million profiles, supported by an identity graph of approximately 2.1 billion identities.

The SpringDB Data Exchange brings you conversations with the people building the data economy. Subscribe for a new episode, and learn more at springdb.io.

In this episode of the SpringDB Data Exchange Podcast, John Kosturos sits down with Peter Long, CEO of MCH Data, to break down how B2B marketers can effectively reach teachers, principals, school districts, and educational institutions with the right data strategy.

What you'll learn:
→ Why general B2B databases fail in the education market
→ How MCH Data monitors 80,000 schools every month and updates 4 million educator records
→ Why 1 in 7 public school teachers moves or leaves every year and what that means for your campaigns
→ The school buying calendar: when to market, when to pull back, and when budgets reset
→ How to segment 400+ educator job titles for precise targeting
→ Email deliverability rules specific to school district networks
→ Commercial models: list licensing, private cloud databases, APIs, and programmatic audiences

Whether you are new to education as a vertical or looking to sharpen your data strategy, this episode is the practical guide you need.

🔗 Learn more about MCH Data: mchdata.com
🔗 Explore education data on SpringDB: springdb.io/data-vendors/mch-data/

#EducationData #B2BMarketing #K12Marketing #EdTech #DataStrategy #SchoolDistrict #ProgrammaticAdvertising #DataExchangePodcast

In this episode of the Data Exchange Podcast, John Kosturos sits down with Peter Long, CEO of MCH Data, to break down how B2B marketers can effectively reach doctors, hospitals, nursing homes, and healthcare institutions using the right data and strategy.

What you'll learn:
→ How to segment 240+ medical specialties for targeted campaigns
→ Why healthcare buyers respond differently than standard B2B audiences
→ Email deliverability rules specific to healthcare institutions
→ How programmatic digital advertising works in the healthcare space
→ The person-plus-place linkage problem most databases miss
→ Commercial models: list licensing, APIs, private cloud databases

Whether you're new to healthcare as a vertical or looking to sharpen your data strategy, this episode gives you a practical roadmap.

🔗 Learn more about MCH Data: mchdata.com
🔗 Explore healthcare data on SpringDB: springdb.io/data-vendors/mch-data/

In this episode of SpringDB Data Exchange, we sit down with Nick Weldon, Co-Founder of 5x5, to break down how today’s leading platforms are powered by identity graphs, data enrichment, and intent data.

Nick shares how 5x5 built a unique data co-op model, where companies collaborate and exchange first-party and behavioral data to create a massive, unified data ecosystem—without relying on traditional data vendors or expensive data licensing.

We go deep into how data platforms are evolving, why enrichment is no longer optional, and how companies can unlock powerful insights by combining multiple data sources into a single identity graph.

🧠 What You’ll Learn
What an identity graph is and how it connects B2B, consumer, device & behavioral data
Why data enrichment is critical for CRM, MarTech & AdTech platforms
How intent data actually works (and why most solutions get it wrong)
The challenges of scaling large data pipelines (petabyte-level)
How modern platforms use data to build better products and experiences
🎯 Who Should Watch
Product leaders building data-driven platforms
Founders exploring data as a competitive advantage
Data engineers & architects working with large-scale datasets
Growth, RevOps, and marketing teams using enrichment & intent data
Anyone curious about how modern data ecosystems actually work
🔗 Full Podcast & More Episodes

👉 https://springdb.io/podcast

Rohini shares his background and what led him to join HG Insights after first experiencing the platform as a customer. Drawing on decades of experience in technology leadership, he explains why data, AI, and market intelligence are becoming essential tools for modern growth strategies.

The conversation covers how companies can move beyond surface-level market analysis, why understanding what technologies customers actually use matters, and how insights like spend data and buying signals help teams focus on the opportunities that are most likely to convert. Rohini also discusses how HG Insights supports both large enterprises and leaner teams, and how curated data, AI-powered tools, and partnerships are shaping the future of go-to-market execution.

Whether you’re a business leader, sales or marketing professional, or simply curious about how data is changing the way companies grow, this episode offers clear insights and practical takeaways.

Topics include:

Technographics and market intelligence explained simply

How companies identify the right accounts and buyers

Using data and AI to drive smarter growth decisions

The role of curated data and partnerships in scaling

👉 Subscribe for more conversations with leaders shaping the future of data and business growth.