There’s no scarcity of raw data today within enterprises. So why can’t analysts and data scientists generate insights fast enough to keep up with business needs?
The single metric Intuit uses is time to reliable insights: the total of time spent to ingest, transform, catalog, analyze, and publish. Based on the company’s experience, there are three elephants in the room when it comes to time to reliable insights—time to discover, time to catalog, and time to debug for data quality—and these aspects are unbounded. Sandeep Uttamchandani shares three design patterns/frameworks Intuit implemented to address these unbounded aspects, expediting time to reliable insights.
Sandeep Uttamchandani is the hands-on chief data architect and head of data platform engineering at Intuit, where he leads the big data analytics, ML, and transactional platform used by 3M+ small business users for financial accounting, payroll, and billions of dollars in daily payments. Previously, Sandeep held engineering roles at VMware and IBM and founded a startup focused on ML for managing enterprise systems. Sandeep’s experience uniquely combines building enterprise data products and operational expertise in managing petabyte-scale data and analytics platforms in production for IBM’s federal and Fortune 100 customers. Sandeep has received several excellence awards. He has over 40 issued patents and 25 publications in key systems conference such as VLDB, SIGMOD, CIDR, and USENIX. Sandeep is a regular speaker at academic institutions and conducts conference tutorials for data engineers and scientists. He advises PhD students and startups, serves as program committee member for systems and data conferences, and was an associate editor for ACM Transactions on Storage. Sandeep holds a PhD in computer science from the University of Illinois at Urbana-Champaign.
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