Autonomous Data Cloud for AI-driven analysis.
Organizations today are moving from data chaos to AI and Data Ops is leading the charge. The goal of data ops is to unify data for analysis to drive business outcomes and doing so requires a heavy lift from Data Scientists, Engineers and IT.
DataOps can be the fuel that drives an organization forward, but complex obstacles to unifying data stand in the way. Teams see incredible value in utilizing AI, but aren’t able to implement efficiently due to the complexity of data infrastructures. Forcing teams to apply AI/ML algorithms to silos of data, but not providing a way of connecting siloed data creates the original challenge inherent with big data platforms - analysis paralysis.
Data Ops must understand platform specific query languages as well as complex ETL and data modeling techniques in order to effectively connect and analyze data. Hiring candidates with the right knowledge and certifications comes at a high price, and in-house training is expensive and time consuming. This shortage of expertise combined with inefficient data architectures can be costly by preventing teams to analyze data effectively.
Gemini Enterprise attacks the most pressing issues to Data Ops by unifying data across silos. Through it’s Autonomous Data Cloud, data across all enterprise platforms can be accessed and queried without data movement or ETL. This provides an optimal platform to instrument AI and ML techniques. By using the unified query engine, users can query everything with ANSI SQL - allowing teams to do more with less.
Also, teams can efficiently manage and deploy data platforms such as Splunk and Elastic without needing to know complex query languages. This frees teams up to focus on moving the business forward. Regardless of the platform, Gemini connects data to provide actionable insights with multi-dimensional context.
By Use Case