It’s no secret that deriving business value from data with analytic techniques like machine learning and artificial intelligence is a top CIO priority. Artificial intelligence, for example, was identified by CIOs as the top disruptive technology to existing business models in Gartner’s 2019 global CIO survey. But another top concern of CIOs, and rightly so, is data governance and security.
The reality is that enterprise IT and data teams face a challenging dual mandate to meet this lofty goal. These teams are under increasing pressure to make data widely available to the business users to support various digital transformation initiatives. Data scientists and business analysts need seamless access to data via the tools and applications of their choice. As enterprises embark on the journey to be data-driven, they require seamless and broad access to data using powerful analytics platforms such as Databricks. The IT infrastructure teams are forced to seek a balance between the mandate to make more data widely available with the competing directive to ensure that the enterprise’s use of data is in compliance with all applicable external privacy regulations and industry standards, as well as internal data usage best practices. Creating and enforcing data access control policies to ensure that only authorized users have access to the data, as well as implementing mechanisms to enable monitoring and auditing of access patterns is foundational to meeting this dual mandate.
These two mandates are in tension with one another and must be balanced. The companies that get this balance wrong by overly restricting access to sensitive data for data scientists and analysts risk missing out on valuable insights that could lead to competitive advantage. On the other end of the spectrum, enterprises that do not apply the proper data access governance controls risk unauthorized users accessing sensitive data, which can result in a lack of regulatory compliance and financial and/or reputational harm to the enterprise and in some extreme cases its ability to operate.
Privacera’s mission is to help organizations strike this delicate balance for their enterprise data and to that end provides a platform that strives to deliver this capability to enterprises. Privacera’s centralized data access governance platform is based on the open-source project Apache Ranger. Privacera has extended Ranger’s capabilities beyond traditional Big Data environments to cloud-native services and leading analytics platforms such as AWS, Azure, GCP, and Databricks. Together, Privacera and Databricks enable enterprises to safely and securely make data accessible for processing, advanced machine learning, and artificial intelligence by managing the complete data access governance lifecycle.
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Data Governance for Databricks with Privacera, Powered by Apache Ranger