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In their December 2025 report Martech for 2026, Scott Brinker and Frans Riemersma surveyed over 100 marketing technology leaders to map where the industry actually stands. The number that kept appearing: integration remained a Top 3 challenge for the majority of respondents, even after years of CDP adoption explicitly designed to solve it.
The reason, Brinker argues, is that the 2nd Age of Martech addressed integration the wrong way by adding another box to the stack rather than eliminating the spaces between boxes. The 3rd Age requires something different: a unified data foundation where capabilities plug in around the data, rather than requiring the data to travel to them. He calls this the composable canvas.
For Microsoft-centric organizations, this is not an abstract principle. It is a description of what Microsoft Fabric is trying to become: a single OneLake where all data lives in open Delta format, every Fabric workload reads from the same copy without replication, and the Microsoft ecosystem — Dynamics 365, Power BI, Azure ML, Microsoft 365 — operates as a coherent intelligence layer on top. The question this creates is direct: if your data already lives in OneLake, why would you extract it to a CDP vendor’s cloud to resolve identities, then import the results back?
“My view on build vs. buy has really changed. If you had asked me a year ago, I would have talked about a set of off-the-shelf SaaS tools. Now it’s all about what we can do with AI. We have 25 AI-native tools we’re immersed in and training our marketers on, plus 12 agentic AI use cases we’ve created that are unique to our business.”
— Meagen Eisenberg, CMO, Samsara
— from The New Martech “Stack” for the AI Age
Before explaining how to build identity resolution natively on Fabric, let’s be precise about where packaged CDPs, including Microsoft’s own Dynamics 365 Customer Insights, consistently break down.
The 60–90 day figure in CDP vendor marketing refers to getting a basic pipeline flowing not a reliable identity graph. Enterprise CDP implementations — Salesforce Data Cloud, Adobe Real-Time CDP, Twilio Segment, mParticle — consistently take 12 to 24 months from contract to production-quality identity resolution. This is also true of Microsoft’s own Dynamics 365 Customer Insights for complex matching scenarios. Data modeling, schema alignment, data quality remediation, connector configuration, and matching model tuning all add months on top of months.
Enterprise CDP contracts range from $300,000 to over $2 million annually before implementation costs. Systems integrators add $500,000–$1.5 million in year-one professional services. According to the Martech for 2026 survey, integration remained a Top 3 challenge despite CDP adoption. For Microsoft-centric organizations, third-party CDPs also sit outside the Microsoft security and governance perimeter, requiring custom integrations back to Dynamics, Teams, and Power BI on top of the base implementation cost.
Every packaged CDP requires your customer data to be replicated to the vendor’s cloud. That directly contradicts OneLake’s architecture, which is built on the premise that data lives in one place and every workload reads from that single copy. A CDP creates a perpetual derivative that’s always fighting to stay synchronized with OneLake, and adds a new data residency surface in every compliance conversation.
CDP vendors protect their matching algorithms as proprietary, even Microsoft’s Customer Insights. You accept their confidence thresholds and merging logic. When a match is wrong, you cannot audit the decision, retrain the model, or implement domain-specific rules. For KYC/AML, healthcare, and regulated B2B environments, this opacity is a compliance liability.
CDP pricing scales directly with profiles, events, and destinations. As your customer base grows, your bill grows proportionally, often non-linearly. Fabric compute costs do not scale the same way.
“That openness at the storage layer gets rid of the locked-in aspect that’s plagued this industry. We wanted something that was portable and open so that we weren’t locked into a particular cloud.”
— Chris Wissing, Chief Product Officer, Epsilon
— from The New Martech “Stack” for the AI Age
Your customer identity graph is your most strategic data asset. Once it lives in a CDP’s data model, switching means re-resolving all your identities, remapping all downstream systems, and losing historical linkages that often cannot be reconstructed.
Traditional CDPs were architected for resolving individual consumers. B2B identity resolution requires resolving accounts, contacts within accounts, and hierarchical relationships between subsidiaries, parent entities, and buying groups. Dynamics 365 Customer Insights is well-integrated with the Microsoft stack but consistently falls short for complex B2B matching at scale. A single enterprise account appears across Dynamics 365, SAP, ZoomInfo, Bombora, and partner data — under variant names and identifiers. Most B2B organizations maintain separate account matching outside their CDP because the CDP simply cannot handle it.
Zingg on Microsoft Fabric closes every one of these gaps. It runs natively on Fabric Spark notebooks, reads from OneLake, and writes a ZINGG_ID — a persistent, unified identity key — back to OneLake Delta tables. No extraction. No vendor cloud. No black box. The ZINGG_ID is instantly available to Power BI, Fabric notebooks, the Lakehouse, and downstream Dynamics 365 workflows.
“All CMOs are trying to solve the same three things: effectiveness, efficiency, and self-service.”
— Rick Schultz, CMO, Databricks
— from The New Martech “Stack” for the AI Age
A native ZINGG_ID in OneLake delivers exactly that: a self-service-ready unified customer identity that marketing, analytics, and CX teams can access from Power BI, Dynamics, and Azure ML without waiting for a CDP sync or a data engineering ticket.
Zingg on Fabric draws on your full enterprise data estate, Dynamics 365 CRM and ERP, Microsoft 365 engagement signals, Azure SQL operational data, partner data via Data Sharing, product telemetry, supply chain records, all in OneLake. The ZINGG_ID connects a richer picture than any CDP ingesting only marketing data.
Microsoft Purview provides data catalog, lineage tracking, and sensitivity labels. Your existing dbt transformations, Azure Data Quality checks, or Microsoft’s native Pipeline validations ensure records are clean before Zingg processes them. Zingg adds the ML-based matching layer on top.
“We want to own the core construct of the data and infrastructure. If we change agencies, all we need to do is flip the activation layer at the top. The foundation stays with us.”
— Kumar Ram, VP/Global Head of Marketing Data Sciences, HP
Zingg on Fabric resolves B2B accounts natively — hierarchies, subsidiaries, buying groups — powering ABM in Adobe Marketo Engage, Salesforce Account Engagement, and 6sense.
Customer records in OneLake (Delta format), cataloged by Purview. Zingg runs on Fabric Spark; ZINGG_ID written back to OneLake. Power BI dashboards on identity-resolved data; Dynamics 365 via Dataverse connectors. Activation to Braze, Iterable, Klaviyo, Salesforce Marketing Cloud, and Google Ads through Hightouch or Census.
OneLake’s architecture is built around the premise that data lives in one place and every capability reads from it. A CDP that requires data to leave OneLake to be resolved is architecturally backwards for Fabric based organizations. Zingg keeps your most strategic data asset in OneLake, governed by Purview, enriched by your entire enterprise data estate, and owned by your organization for B2C consumers and B2B accounts alike.
Build on Fabric. Don’t extract to a vendor’s cloud.
🔗 Zingg for Microsoft Fabric | Talk to the Team | Deployment Guides
📄 The New Martech “Stack” for the AI Age — Scott Brinker & Databricks (March 2026)
📄 Martech for 2026 — Scott Brinker & Frans Riemersma (December 2025)
