Speed matters in all aspects of business — especially when it comes to analyzing data.
To that end, Incorta, a data analytics startup, said earlier this month that it raised $30 million in a Series C round led by Sorenson Capital, alongside existing investors, including GV (formerly known as Google Ventures), Kleiner Perkins, Telstra Ventures and others.
The company said it helps aggregate complex business data in real time, and it helps streamline the storing of that data — in turn helping businesses make decisions in real time, which can help with supply chain management and other functions.
In an interview with PYMNTS, Osama Elkady, co-founder and CEO of the company, said among the biggest misconceptions companies have about the information they have on hand is that current methods and legacy infrastructures are the best way to analyze their complex data.
“Companies assume that techniques of the past are still mandatory for modern business user needs,” Elkady told PYMNTS. “These techniques take months, which was perfectly acceptable to the traditional cadence of business. Today’s market conditions warrant much faster analysis to drive rapid response, and the old techniques just can’t keep up.”
By way of contrast, modern tools and techniques can now deliver time-sensitive insights to support decisions much more rapidly.
Speed is important, too, against the backdrop of a more stringent regulatory landscape, he said. Grappling with fraudsters is a time-sensitive situation.
“The longer you wait to detect an issue, the more severe the impact — and the regulations have kept pace,” he said. “Often, the penalties from a delayed response are significantly more costly than those made quickly.”
Yet, Elkady said even though businesses could benefit greatly from continuous data updates, refreshing data even every 12 hours — hardly real time — remains elusive.
“Between the new regulations and the lack of ability for most technology to keep up, we’ve got a problem,” he said.
He said a data warehouse is essentially a data repository, and warehouses have been ubiquitous for decades.
In terms of mechanics, Elkady said in order to analyze data within the warehouse in reasonable time frames, data engineers need to run data through a process called Extract, Transform and Load (also known as ETL). This process can take hours, depending on the size of the dataset, and is often “brittle, thereby dramatically impeding analysis.”
He said Incorta’s technology removes the ETL process from the equation, allowing data teams to access information instantly and in real time, saving hours and even weeks in the analytics process.
He said the company’s Direct Data Mapping technology runs queries with hundreds of joins across data and still delivers sub-second query response times.
“This ability completely eliminates the need to flatten (or ETL) the data,” Elkady said. “Business can feed data curiosity and stay ahead of the uncertainties by conducting real-time conversations with their data — and this enables them to make more accurate and timely business decisions.”