Data lakes as part of the logical data warehouse (LDW) have entered the trough of disillusionment. Some failures are due to lack of value from businesses focusing on the big data challenges and not the big analytics opportunity. After all, data is just data until you analyze it.
While the data management aspect has been fairly well understood over the years, the success of business intelligence (BI) and analytics on data lakes lags behind. In fact, data lakes often fail because they are only accessible by highly skilled data scientists and not by business users. But BI tools have been able to access data warehouses for years, so what gives?
Randy Lea explains why existing BI tools are architected well for data warehouses but not data lakes, the pros and cons of each architecture, and why every organization should have two BI standards: one for data warehouses and one for data lakes.
This session is sponsored by Arcadia Data.
Randy Lea is chief revenue officer at Arcadia Data, where he is charged with leading the company’s sales momentum. Randy is passionate about solving customer problems by leveraging analytics and data. An early participant in the data warehouse and BI analytics market, he has held leadership positions at companies including Aster Data, Think Big Analytics, and Teradata. Randy holds a bachelor’s degree in marketing from California State University, Fullerton.
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