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Market Milestone: Oracle Builds Data Science Gravity By Purchasing

Industry: Data Science Platforms

Key Stakeholders: IT managers, data scientists, data analysts, database administrators, application developers, enterprise statisticians, machine learning directors and managers, current customers, current Oracle customers

Why It Matters: Oracle released a number of AI tools in Q4 2017, but until now, it lacked a data science platform to support complete data science workflows. With this acquisition, Oracle now has an end-to-end platform to manage these workflows and support collaboration among teams of data scientists and business users, and it joins other major enterprise software companies in being able to operationalize data science.

Top Takeaways: Oracle acquired to retain customers with data science needs in-house rather than risk losing their data science-based business to competitors. However, Oracle has not yet not defined a timeline for rolling out the unified data science platform, or its future availability on the Oracle Cloud.

Oracle Acquires

On May 16, 2018, Oracle announced that it had agreed to acquire, an enterprise data science platform that Oracle expects to add to the Oracle Cloud environment. With Oracle’s debut of a number of AI tools last fall, this latest acquisition telegraphs Oracle’s intent to expedite its entrance into the data science platform market by buying its way in.

Oracle is reviewing’s existing product roadmap and will supply guidance in the future, but they mean to provide a single unified data science platform in concert with Oracle Cloud Infrastructure and its existing SaaS and PaaS offerings, empowering customers with a broader suite of machine learning tools and a complete workflow.

Who is was founded in 2014 by Ian Swanson, Jonathan Beckhardt, and Colin Schmidt. Crosscut Ventures led the initial seed round in 2014, followed by a Series A round in 2015 led by Greycroft, and an extension later that same year led by Whitehart Ventures. allows customers to explore data sources, build models and algorithms to analyze their data, and then deploy the results. Data scientists can work in familiar environments such as Jupyter and RStudio within the platform, analyze their data in standardized workflows to ensure validity across analyses, and then share the results with the rest of the company, either as a report, a dashboard, or an app.

Notable partnerships include, which allows customers to deploy Open Source models from within the platform, and MapR, which lets customers run experiments directly on MapR data stores without needing a separate compute cluster just to access their data. Since MapR is already an Oracle Cloud partner, this partnership in particular should continue seamlessly.

Why does this matter?

Oracle has long had a reputation for being the place where data lives for its customers. They want to provide the best environment to store enterprise data, and any apps and analytics built on top of enterprise data. The lack of an in-house data science platform was a hole in the analytics portfolio for Oracle which provided a notable risk for potentially exporting enterprise data to conduct data science; getting a data science platform in place to stop the potential leak was the next obvious step. Right now, the AI/ML market is worth about $12B, but this level of spend is expected to nearly quintuple by 2021, according to IDC. Acquiring and integrating into the Oracle Cloud will allow Oracle customers to more quickly deploy their machine learning and AI solutions in a single environment, which will both increase the value of working with Oracle Cloud and let Oracle participate more fully in the data science market.


For IT managers considering purchasing data science platform access in the near future from, Oracle has stated that products and services will remain available for purchase for now. Current purchases should go through for sales and support until Oracle has announced formal Oracle-based sales and product support..

If your purchasing timeline is beyond the next 6-12 months, expect that Oracle will integrate into Oracle Cloud Infrastructure, and strive to compete with other mega-vendors such as Microsoft, IBM, and SAP.  The battle for cloud-based data science supremacy will only intensify, both to collect machine-learning data sources and to acquire teams of highly skilled developers who will use these platforms to build next-generation applications.

1 thought on “Market Milestone: Oracle Builds Data Science Gravity By Purchasing

  1. […] the acquisition of earlier this year, Oracle now has a vested interest in ensuring that their customers have a […]

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