Machine learning is a powerful paradigm many organizations are utilizing to derive insights and add features to their applications, but using it requires skills, data, and effort. Explorium, a startup from Israel, has just announced $19 million of funding to lower the barrier on all of the above. The funding announced today comprises a seed round of $3.6 million led by Emerge with the participation of F2 Capital and a $15.5 million Series A led by Zeev Ventures with the involvement of the seed investors. Explorium was founded by Maor Shlomo, Or Tamir, and Omer Har, three Israeli tech entrepreneurs, who previously led large-scale data mining and optimization platforms for big data-based marketing leaders. "We are doing for machine learning data what search engines did for the web," said Explorium co-founder and CEO Maor Shlomo.
Data analytics company Explorium, cofounded by a trio of Israeli entrepreneurs, two of whom have experience in military intelligence, announced today that it has raised $31 million in a Series B round. Existing investor Zeev Ventures led the round joined by Adam Bain, Twitter's former COO and Partner at 01 Advisors, and Sir Ronald Cohen's Dynamic Loop, with the participation of seed investors Emerge and F2 Capital. "The biggest challenge that we see in the field in the next decade will not be how to build those sophisticated algorithms or how to use those beautiful dashboards, but rather what is the right data to feed into those analytical processes," Maor Shlomo, cofounder and CEO of Explorium, says. Explorium is connected to worldwide sources and it allows customers to ask predictive questions like trying to figure out sales for next month, better moderate risk or predict prices of real estate. According to Shlomo these are challenges because of humans' inherent limitations in regard to their ability to explore a variety of opportunities for data sources or pieces of information.
Explorium, a Tel Aviv-based startup developing an automated data and feature discovery platform, today closed a $31 million funding round. The capital infusion comes after several banner months for Explorium, which has tripled its customer base since last September and incorporated data relevant to more industries and verticals. Feature engineering -- the process of using domain knowledge to extract features from raw data via data-mining techniques -- is arduous. According to a Forbes survey, data scientists spend 80% of their time on data preparation, and 76% view it as the least enjoyable part of their work. It's also expensive -- Trifecta pegs the collective data prep cost for organizations at $450 billion.
Retailers the world over are grappling with how to attract today's digitally savvy consumers and turn them in to repeat customers. Yet, I would argue that this is nowhere more keenly felt than in China. Chinese consumers are setting shopping trends globally, especially with their avid use of digital devices and social media. They are much more likely than American or European consumers to interact with brands through social media, according to BCG. Chinese consumers are fast becoming the world's most discriminating and knowledgeable.
For impactful machine learning, data scientists first need clean, structured data. That's where feature engineering comes in -- to refine data structures that improve the efficiency and accuracy of machine learning models. Ryohei Fujimaki, Ph.D., CEO and founder of dotData, a data science platform, said, "Features are, without question, even more critical than the machine learning algorithm itself." Poor quality features will result in a failure of the machine learning algorithm, he said. On the other hand, high-quality features will allow even simple machine learning algorithms like linear regression to perform well.