A little less than 6 months after securing an impressive Series B round, Israeli data-science startup Explorium, which provides enterprises with an augmented data discovery platform, announced a partnership with Nova Consulting, a leading data strategy and consulting boutique.
The partnership is centered around Explorium's robust data enrichment and machine learning capabilities. These will empower Nova to build faster and more accurate predictive models to give its clients the most relevant decision-making recommendations and strategic advice for maximizing ROI. Nova will leverage the Israeli startup's data magic across diverse market sectors, including automotive, finance, energy, consumer goods, food, and entertainment.
"Explorium is the greatest startup that I've encountered over the last several years," said Nicolas Harlé, General Manager at Nova Consulting. "This partnership expedites our client's digital transformation, helping them to achieve their data science capabilities and meaningfully advancing their decision-making, sooner."
Using Explorium's unique data enrichment and modeling capabilities, Nova's data scientists get access to thousands of data sources and signals from Explorium's Enrichment Catalog, which are automatically combined with their internal datasets and used to generate impactful features.
According to Nova, the resulting models not only have higher predictive power but are also built in minutes rather than months, which allows Nova to give timely strategic advice to their global customer base.
"We're proud of our partnership with Nova. They help drive innovation for many reputable companies and truly demonstrate the value that the right data can contribute to understanding market conditions and informing critical business decisions," said Zach Booth, Director of Global Partnerships and Business Development. "Nova really gets data, and Explorium is a passport to the world of alternative data, ensuring that every variable is considered and tested for its impact on predictions and then rapidly integrated into predictive models."
