The dashboard that replaced a data warehouse. Foster City, CA.
When four Oracle veterans decided the entire premise of data warehousing was a workaround, they built a platform to prove it.
At any given moment, the analytics team at a Fortune 500 company is waiting. Waiting for the ETL job to finish. Waiting for the data warehouse to rebuild. Waiting for a ticket to get approved so someone can add a column to the model. The data is right there - sitting in SAP, in Oracle, in Workday - and somehow it is still unavailable. This is enterprise data in 2024, and it is, by design, bad.
Incorta's entire existence is a rebuttal to that design. The company - headquartered in Foster City, California, backed by Kleiner Perkins, GV, and Prysm Capital - has built what it calls an Operational Lakehouse: a platform that connects analytics directly to raw source-system data, bypassing the transformation layer entirely. No ETL pipeline. No star-schema development. No 18-month data warehouse project. Just questions, and answers.
It works for Starbucks, which uses it to analyze 20 billion-plus transactions across 28,000 locations in real time. It works for Broadcom, which cut its ETL expenditures by 70 percent. It works for Comcast, where a team of three engineers rewired the entire reporting infrastructure and stopped millions in losses from stale data.
"ETL is just a workaround."- Osama Elkady, CEO & Co-Founder, Incorta
The conventional data stack works like this: extract data from your operational systems, transform it into a shape the analysts can query, load it into a warehouse, and wait. Then, when the business changes - a new product line, a new ERP module, a new reporting requirement - start over. This process has a name: ETL. It also has a cost: typically 18 to 24 months of development before analysts see their first dashboard.
Somewhere in Silicon Valley in 2013, four engineers who had spent a combined four decades building these very systems at Oracle looked at that pipeline and asked the obvious question. Why is all this transformation necessary? The raw data in your ERP systems is already highly structured, highly relational, and almost always more detailed than what survives the ETL process. All that work - all those pipelines, all that modeling - is overhead introduced to compensate for the analytical limitations of a relational data warehouse.
The insight sounds simple. The implementation was not.
Traditional ETL vs. Incorta's Direct Data Mapping. "The pipeline was always the bottleneck. We removed it." - Incorta, probably.
In 2013, Osama Elkady was Vice President of Oracle Applications Development. He had spent 20 years inside one of the most dominant enterprise software companies in history. His colleagues - Klaus Fabian, Hichem Sellami, and Matthew Halliday - had between them architected BI Publisher, designed data warehouses across multiple industries, and shipped analytics products to thousands of enterprise customers.
They left to fix a problem they had spent careers creating. As Elkady put it: "I'm not able to do my next set of innovations being part of the company." They understood, from the inside, why enterprise analytics was slow - and they knew exactly where the slack was.
The company they built is not a pivot, not a rebrand, and not a feature-add to existing infrastructure. It is a structural argument that the modern enterprise data stack - decades of assumptions baked into warehouse architecture - is a solution to a problem that no longer needs to exist.
"The best developer is the one who writes no code."- Osama Elkady, CEO
Four Oracle veterans leave to build an analytics platform without ETL. The world is skeptical.
GV and Kleiner Perkins back the bet. Direct Data Mapping enters the enterprise market.
Telstra Ventures and M12 (Microsoft Ventures) join. International expansion begins.
Sorenson Capital leads. Starbucks and Broadcom deployments validate the approach at scale.
Prysm Capital leads the largest round yet. Total funding reaches ~$195M. National Grid Partners joins as strategic investor for energy sector expansion.
Named Niche Player every year. Operational GenAI and Nexus Suite launched. CEO recognized as top data voice on LinkedIn.
Cairo-based no-code AI platform acquired to accelerate agentic workflows. Static dashboards officially on notice.
Most analytics platforms require you to model your data before you can query it. Incorta does not. Its Direct Data Mapping technology ingests data from ERP systems in its native third-normal form - the same format it lives in inside SAP, Oracle, Workday, or Salesforce - and makes it immediately queryable at full resolution. The system pre-processes a data lake such that it does not need to be transformed, reshaped, or aggregated to achieve fast query performance. Every data point loads with awareness of how it relates to every other data point.
The result: enterprises get 100 percent of their data, not the summarized subset that survives a traditional transformation pipeline. Queries that used to require a star-schema model now run on raw, source-identical data. Projects that previously took 18 to 24 months deploy in weeks.
Proprietary engine that connects analytics to raw ERP data. No transformation. No aggregation. No approximation.
Creates a digital twin of your operational sources. Live, detailed data across all systems of record - in real time.
240+ pre-built connectors to SAP, Oracle, Workday, Salesforce, Snowflake, Databricks, and more - with automated schema detection.
Enterprise-grade generative AI with RAG and LLM integrations, running privately and securely on your live operational data.
AI-first platform layer: Nexus Connect, Nexus Workflows, and Nexus Analytics for agentic data pipelines and decision-making.
No-code AI application builder enabling composable intelligence - the shift from static dashboards to live, interactive workflows.
"We build what our customers don't even know is possible - until they see it."- Osama Elkady, CEO
When your product's core claim is that it reduces 24-month projects to weeks, you need proof that is hard to argue with. Incorta has it. From Fortune 500 retailers to global chipmakers to telecom providers, the results follow a pattern: dramatically less engineering overhead, more complete data, faster answers.
Real-time analysis of 20B+ transactions across 28,000+ locations. Reduced food production costs by 40%.
Direct Data Mapping cut ETL expenditures by up to 70 percent. Fewer engineers, less overhead, better data.
Three engineers rewired the entire reporting infrastructure. Stale data was costing millions. It stopped.
Unified 8 data sources and cut issue resolution times by 90 percent.
Thwarted 56% of reported fraudulent accounts using Incorta's Spark Graphframes and real-time dashboards.
7 fragmented ERP systems unified. Month-end close time cut by 50%. DSO reduced 20% across 8 sites.
Incorta's investor list reads like a who's who of enterprise software conviction. GV backed it in the Series A. Kleiner Perkins joined for Series B. Prysm Capital led the $120M Series D in June 2021 - one of the largest data analytics rounds of that year - with participation from Wipro Ventures and National Grid Partners. The strategic signal was clear: this isn't just another BI tool.
Key backers include GV (Google Ventures), Kleiner Perkins, M12 (Microsoft Ventures), Sorenson Capital, Telstra Ventures, Wipro Ventures, Prysm Capital, and National Grid Partners. The mix of pure-play venture capital and strategic corporate investors reflects Incorta's unusual position: relevant to cloud giants, telecom providers, and energy companies equally.
Incorta's operating thesis is not complicated. Every minute a business waits for its data pipeline to finish is a minute it makes decisions without full information. The traditional data warehouse was designed to make that wait acceptable. Incorta was designed to make the wait unnecessary.
The company's push into Operational GenAI, Nexus Workflows, and the acquisition of Layout.dev in early 2026 signals where this is heading: not just faster analytics, but analytics that act. AI agents that query live operational data, surface anomalies, and trigger workflows - without a human in the loop watching for the dashboard to refresh.
In a landscape where Tableau, Power BI, Looker, and Snowflake all compete for the same data analysis dollar, Incorta's differentiator has never been the visualization layer. It has always been the data access layer. The argument that all those other platforms are building beautiful charts on top of incomplete, stale, pre-aggregated data - and that there is a better way.
"When the founders left Oracle in 2013, they noticed businesses were drowning in data but unable to use it."- Incorta origin story, as told by its team
Go back to the analyst who opened this story - the one waiting for the ETL job to finish. They still exist in most large enterprises. But the window for that kind of patience is closing fast. As LLMs make it trivial to ask questions in natural language, the bottleneck is no longer the interface. It is the data infrastructure behind it. If your answer arrives on transformed, summarized, 24-hour-old data, your AI assistant is, at best, well-spoken and wrong.
Incorta's 2026 acquisition of Layout.dev is the most explicit signal yet of where they are headed: away from static dashboards entirely, toward composable, agentic workflows built on live operational data. The company that spent a decade arguing against ETL is now arguing against the dashboard as an endpoint. Query your data. Act on it. Automatically. In real time.
The founding bet - that raw data is more valuable than processed data, and that speed to insight is worth more than any transformation pipeline - has now been validated at Starbucks, Broadcom, Comcast, and dozens of Global 2000 companies. What Incorta is building next is a future where asking your business a question and getting an answer are separated by milliseconds, not months.
That analyst is still waiting. But probably not for much longer.