Micah Horner, Product Marketing Manager, TimeXtender
On June 1, 2026, insightsoftware expanded its partnership with Timextender to launch a new version of Jet Analytics, adding native support for Microsoft Fabric, Snowflake, Azure Data Lake, and Power BI.
The announcement is a Jet Analytics release. It's also a clear example of what an OEM partnership with Timextender actually delivers, both for the partner and for customers.
What "OEM" Means in This Context
insightsoftware doesn't resell Timextender. It embeds Timextender Data Integration (the module of the Timextender Data Platform that covers the Ingest, Prepare, and Deliver layers of the data lifecycle) inside Jet Analytics and sells the result as its own product, built for Microsoft Dynamics customers who need a data warehouse without hiring a team of ETL developers.
That's the OEM model: a software company builds automation, governance, and connectivity into its own product using Timextender's engine underneath, rather than building that layer from scratch or asking its customers to stitch together a separate tool.
The mechanism that makes this possible is the Unified Metadata Framework, Timextender's architecture for capturing every pipeline and transformation as portable metadata rather than hand-written code. Jet Analytics uses that framework to generate production-ready pipelines automatically for each customer's Dynamics environment. Because the logic lives in metadata rather than custom code, the same solution can redeploy to Microsoft Fabric, Snowflake, Azure Data Lake, or Azure SQL, without a rebuild.
For insightsoftware, the math is straightforward. Building a metadata-driven automation layer, with native connectors, incremental load logic, and a semantic layer that holds up across Fabric, Snowflake, and Power BI, is a multi-year engineering investment with ongoing maintenance costs.
Licensing that layer through an OEM agreement lets insightsoftware put its own engineering time into the parts of Jet Analytics that are specific to Dynamics and the Office of the CFO, while the underlying data infrastructure work is already built, tested, and maintained.
Why the Expanded Agreement Matters
The new Jet Analytics adds native connectors for Microsoft Fabric, Snowflake, Azure Data Lake, and Power BI, on top of the existing Dynamics support.
Jet Analytics Classic remains available for customers who need a SQL-centric, on-premises, or air-gapped deployment. That dual-platform structure means a mid-market Dynamics customer isn't forced into a rip-and-replace migration to get cloud capability. They can move to the new cloud platform on their own timeline, or stay on Classic if regulatory or infrastructure requirements call for it.
Jennifer Warawa, President at insightsoftware, put the underlying problem plainly in the announcement:
"AI isn't magic, it's only as reliable as the data behind it. Too many finance leaders are still flying blind, dealing with conflicting reports and systems that don't align. Jet Analytics provides the infrastructure layer that makes AI possible, delivering a trusted, governed data foundation that accelerates time to value up to 10x faster than building it manually."
That 10x acceleration comes from Timextender's metadata-driven automation engine, running underneath insightsoftware's brand.
Mosthagir Matin, Timextender's Chief Revenue Officer, framed the expansion from Timextender's side:
"We are excited to expand our partnership with insightsoftware, deepening a long-standing collaboration that brings even greater value to our customers. Together, we are delivering a unified, future-ready analytics platform that empowers organizations to move faster, gain clearer insights, and make more confident, data-driven decisions."
The Case for OEM Over Building In-House
Every software company with a data product eventually faces the same decision: build the data integration and governance layer internally, or license one.
Building it internally means owning connector maintenance across every source system a customer might have, keeping pace with changes to platforms like Fabric and Snowflake, and supporting incremental load, metadata documentation, and lineage as separate engineering workstreams.
That's a lot of surface area for a company whose core expertise is somewhere else, whether that's financial reporting, embedded analytics, or a vertical application.
An OEM relationship with Timextender changes that calculus. The partner gets a metadata-driven foundation that connects to any data source, automates the ETL and modeling work that would otherwise take a dedicated team, and keeps producing AI-ready data as the partner's own product evolves.
Timextender maintains the engine, ships updates continuously, and adds new native connectors like Fabric and Snowflake as customer demand shifts, so the partner's product improves without a proportional increase in its own engineering headcount.
On the Jet Analytics product page, insightsoftware backs this up with its own published numbers: a data foundation built up to 10x faster than a manual approach, with 70% lower data pipeline build costs and a 50% reduction in data maintenance costs.
The result for the partner's customers is a product that looks and feels entirely native to the partner's brand, with none of the friction of managing a second vendor relationship, while running on data infrastructure built and hardened by a company that does nothing else. Because the underlying platform is metadata-driven, processing happens inside the customer's own environment rather than routing data through a third party.
That's Timextender's Zero-Retention approach: for a deployment like this, where Jet Analytics targets the customer's own Fabric, Snowflake, or Azure Data Lake instance, the data itself stays inside the customer's tenant, and only the metadata describing pipelines and transformations is orchestrated by the platform.
Where This Fits for ISVs Considering the Same Move
Jet Analytics is one example of what an OEM partnership with Timextender produces, but the pattern applies to any ISV that needs a governed, automated data foundation inside its own product rather than as a bolt-on integration.
If your product needs to move data from any data source into a clean, documented, AI-ready structure, and you'd rather license that capability than build and maintain it, that's exactly what the OEM program is built for.