Databricks acquires AI-centric data governance platform Okera

Databricks acquires AI-centric data governance platform Okera

a year ago
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https://techcrunch.com/2023/05/03/databricks-acquires-ai-centric-data-governance-platform-okera/

Databricks today announced that it has acquired Okera, a data governance platform with a focus on AI. The two companies did not disclose the purchase price. According to Crunchbase, Okera previously raised just under $30 million. Investors include Felicis, Bessemer Venture Partners, Cyber Mentor Fund, ClearSky and Emergent Ventures.

Data governance was already a hot topic, but the recent focus on AI has highlighted some of the shortcomings of the previous approach to it, Databricks notes in today’s announcement. “Historically, data governance technologies, regardless of sophistication, rely on enforcing control at some narrow waist layer and require workloads to fit into the ‘walled garden” at this layer,’ the company explains in a blog post. That approach doesn’t work anymore in the age of large language models (LLMs) because the number of assets is growing too quickly (in part because so much of it is machine-generated) and because the overall AI landscape is changing so quickly, standard access controls aren’t able to capture these changes quickly enough.

Databricks acquires AI-centric data governance platform Okera

May 3, 2023, 5:26pm UTC
https://techcrunch.com/2023/05/03/databricks-acquires-ai-centric-data-governance-platform-okera/ > Databricks today announced that it has acquired Okera, a data governance platform with a focus on AI. The two companies did not disclose the purchase price. According to Crunchbase, Okera previously raised just under $30 million. Investors include Felicis, Bessemer Venture Partners, Cyber Mentor Fund, ClearSky and Emergent Ventures. > Data governance was already a hot topic, but the recent focus on AI has highlighted some of the shortcomings of the previous approach to it, Databricks notes in today’s announcement. “Historically, data governance technologies, regardless of sophistication, rely on enforcing control at some narrow waist layer and require workloads to fit into the ‘walled garden” at this layer,’ the company explains in a blog post. That approach doesn’t work anymore in the age of large language models (LLMs) because the number of assets is growing too quickly (in part because so much of it is machine-generated) and because the overall AI landscape is changing so quickly, standard access controls aren’t able to capture these changes quickly enough.