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Castor gets $23.5M boost; the startup helps tech companies make sense of their data stacks

Castor-Team-Series-A-Funding
Image credits: Castor

Castor, The Parisian startup assisting companies to find, understand and effectively utilise their data assets effectively has just announced bagging $23.5M in its Series A. The platform provides corporate teams with full visibility and trust of all their data assets across all teams, consequently making data discovery easier, regardless of data literacy levels.

This funding follows the rapid development of its core product, which has now grown to serve big data generating customers such as Deliveroo, Canva and Vestiaire Collective, among others. Castor’s customer base has grown rather quickly, particularly in the US where it boasts offices in San Francisco and New York, growing 40% month-on-month over the past 12 months.

This growth justifies the big jump in its received financing in this round since its previous round which was a $2.6M Seed round in late 2019. Led by Blossom, the Series A also featured previous investor Frst and a cohort of high-profile angel investors such as Florian Douetteau, founder of AI platform Dataiku. The funding will assist the data startup to develop its sales and marketing teams in the US as well as continue to develop the platform’s AI capabilities.

Empowering companies to better use their data

Founded by former data scientists and engineers Tristan Mayer, Xavier de Boisredon, Amaury Dumoulin and Arnaud de Turckheim, Castor exists to solve the common corporate frustration of struggling to identify the right data to work on. With a key focus on the user experience, Castor is the only data catalogue designed for mass adoption across the entire organisation.

Companies are constantly gathering a huge amount of data but keep struggling to efficiently scale its use. It is now a common occurrence to hear of large companies housing data in a ‘data warehouse’ that’s always growing as the company enters new markets. Without proper data discovery tools, data analysts are needlessly creating similar dashboards for each market they enter. Furthermore, operational scaling makes it increasingly difficult for companies to maintain a high standard of reliability in data analysis. This is made even worse by the fragmentation of data staff across different teams.

Castor solves this, firstly by empowering any employee to search through all available data assets in their company, thanks to its powerful search engine and dashboard which provides an overview of the entire data environment. Once the right data is found, the Castor web app assists team members in understanding the context around it and how to utilise it. Several use cases include expediting the onboarding process, streamlining data compliance projects, streamlining cloud migration, debugging and performing impact analyses.

The platform can provide its users with column-level, cross-system data lineage in minutes. Data personnel can have a bird’s eye view of the company’s data pipelines which enables teams to build trust in data. Performance can thus be better optimised alongside cost, with data security and compliance more manageable.

Castor has been distinguished owing to its viral adoption rates by its growing customer base. Data catalogue tools have always existed but are far too often clunky and difficult to maintain, resulting in very low uptake rates across companies. Castor has carefully designed every aspect of the platform with the user in mind to make working with data as easy, friendly and collaborative as possible. This is the incentive to use it.

“The next generation of successful scale-ups will all need to make better use of their data and Castor is the most effective tool for that. The efficacy of the product is borne out by the adoption rates company-wide among Castor’s customers and we can’t wait to introduce this product to other companies so that they can join the Castor revolution,” remarked Blossom partner Imran Ghory in a statement that highlighted the startup’s growth prospects.

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