Data Mapping Approach Gains More Funding

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The need for speed in aggregating data from different sources is attracting the attention of technology investors who are looking for new approaches to mapping structured and unstructured data in real time and at scale while eliminating the need for data modeling.

One emerging approach that has so far reeled in about $25 million in venture funding is a “direct data mapping” engine developed by real-time analytics startup Incorta of San Mateo, Calif. The four-year-old company announced a $15 million funding round this week that includes new investor Kleiner Perkins Caulfield & Byers. Also participating were early funders GV (formerly Google Ventures) and Ron Wohl, a former Oracle executive vice president.

GV lead a $10 million Series A round in May 2016.

Incorta touts its data mapping software as making the traditional data warehouse obsolete. The approach is said to dispense with the traditional extract, transform, load (ETL) model. Hence, data from different sources can be merged in real time. The result is nothing less that “information freedom,” Ted Schlein, a Kleiner Perkins general partner asserted in a statement released on Tuesday (Sept. 19).

Incorta’s data engine maps data directly to its source regardless of format or structure, the startup claimed, with the goal of developing secure, real-time analytics applications in days. Moreover, the engine is said reduce query times from hours to seconds regardless of scale.

The data mapping technology was launched in March, and Incorta said Fortune 500 customers are expanding their implementations and re-upping with “seven-figure contracts.”

Osama Elkady, co-founder and CEO of Incorta, added that the latest funding round would be used to expand the startup’s product development and marketing operations.

The startup’s no-data-warehouse approach is designed to aggregate data across business operations, cloud applications and other enterprise platforms. Data warehouses have traditionally been used to store these different data sources, which were then crunched using a star-schema approach to spot relationships and perform analytics at scale.

Incorta claims its direct data-mapping framework identifies data sources and aggregates data on a single day. By contrast, a traditional data warehouse/business intelligence approach would take at least a week.

While the standard ETL approach to filtering and modeling data would require at least another week followed by a few more weeks to summarize data using visualization tools, Incorta claimed it can do all this in a couple of days.

The ability to map data directly is said to eliminate the traditional star-schema approach associated with data warehousing.

The startup also said its direct mapping framework leverages the Apache Parquet columnar storage format as a way to integrate with Spark cluster computing. That combination creates options for applying machine learning and predictive analytics technologies, Incorta said.

Along with Wohl, Incorta’s executive team includes several former executives from Oracle (NYSE: ORCL) and its Endeca analytics database unit. Ken Rudin, Google’s (NASDAQ: GOOGL) analytics chief, is also working with the startup.

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