No-code AI analytics may soon automate data science jobs

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SparkBeyond, a company that helps analysts use AI to generate new responses to business problems without requiring any code, has today released its product SparkBeyond Discovery.

The company aims to automate the job as a data researcher. Typically, a computer scientist who wants to solve a problem may be able to generate and test 10 or more hypotheses a day. With SparkBeyond’s machine, millions of hypotheses can be generated per minute based on the data it uses from the open web and a client’s internal data, the company says. In addition, SparkBeyond explains its results in natural language so that a no-code analyst can easily understand it.

How companies can benefit from AI analytics data automation

The product is the culmination of the work that started in 2013, when the company had the idea of ​​building a machine to access the Internet and GitHub to find code and other building blocks to formulate new ideas to find solutions to problems. To use SparkBeyond Discovery, all a client company has to do is specify its domain and what exactly it wants to optimize.

SparkBeyond has offered a trial version of the product, which it began developing two years ago. The company says customers include McKinsey, Baker McKenzie, Hitachi, PepsiCo, Santander, Zabka, Swisscard, SEBx, Investa, Oxford and ABInBev.

One of SparkBeyond’s customer success stories involved a retailer who wanted to know where to open 5,000 new stores, with the goal of maximizing profits. As SparkBeyond CEO Sagie Davidovich explains, SparkBeyond took data from the point of sale from the retailer’s existing stores to find out which ones were most profitable. It correlated profitability with data from a number of external sources, including weather information, maps and geo coordinates. SparkBeyond then tested a number of hypotheses, including theories that three consecutive rainy days near competing stories correlated with profitability. In the end, the proximity to laundries correlated strongly with profitability, Davidovich explains. It turns out that people have time to shop while waiting for their laundry, something that may seem obvious in hindsight, but not at all obvious at first.

The company says its auto-generation of analyst prediction models puts it in a unique position in the AI ​​services market. Most AI tools aim to help the data researcher with the modeling and testing process when the data researcher has already come up with a hypothesis to be tested.

Competitors in the computer automation area

Several competitors, including Data Robot and H20, offer automated AI and ML modeling. But SparkBeyond’s VP and general manager, Ed Janvrin, says this area of ​​auto-ML feels increasingly commoditized. SparkBeyond also offers an auto-ML module, he says.

There are also several competitors including Dataiku and Alteryx that help prepare data without code. But these companies do not offer clean, automated feature discovery, Janvrin says. SparkBeyond works on its own data preparation features, which allow analysts to join most data types — such as time series, text analytics, or geospatial data — without having to write code.

Since 2013, SparkBeyond has quietly raised $ 60 million in total investor support, which it has not previously announced. Investors include the Israeli venture firm Aleph, Lord David Alliance and others.

“The demand for computer skills has reached virtually all industries,” Davidovich said in a statement. “What was once considered a domain for expert data researchers in large corporate organizations is now in urgent demand across companies of all sizes.”

“Our new release is powerful yet intuitive enough that data professionals — including analysts at medium and small organizations — can now leverage AI’s power to quickly join multiple datasets, generate millions of hypotheses, and create prediction models that can detect unexpected drivers for better decision making. ”


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