Enterprises rely on data scientists and analysts to provide new insights and valuable capabilities that can advance the business. However, numerous engineers and programmers are needed to build the data infrastructure required to support these individuals and their analytic initiatives. Everyone involved is spending up to 80% of their time just getting the data ready for analysis, and not directly on creating business value.
Automation is imperative to manage the growing complexity of data management—from ingestion to synchronization to preparation—and focus data scientists and analysts on building value for the business. Ramesh Menon shares best practices on how a large enterprise automated data ingestion, data synchronization, and the building of data models and cubes to create a big data warehouse for the rapid deployment of analytics. You’ll leave with a better understanding of how you can effectively and efficiently leverage big data technology to achieve business goals.
This session is sponsored by Infoworks.
Ramesh Menon is head of products at Infoworks. Ramesh has over 20 years of experience building enterprise analytics and data management products. Previously, he led the team at YarcData that built the world’s largest shared-memory appliance for real-time data discovery and one of the industry’s first Spark-optimized platforms and worked at Informatica, where he was responsible for the go-to-market strategy for Informatica’s MDM and Identity Resolution products.
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