The difference between a data warehouse and a data lake:
While data lakes and data warehouses are both widely used for storing big data, they are not the same thing, and should not be used interchangeably. A data lake is a vast pool of raw data, where the purpose is not yet defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose.
Nick Bonfligio on the perils of data warehouses in creating insight siloes
“Cloud data warehouses are here to stay. {...] I advise our customers to think about their warehouse as a part of an overall data solution and unified data model, not just something that’s off to the side for reporting Business Intelligence Insights. You need to ensure insights gleaned from your data warehouse get back into the operational systems where you can act on them, otherwise they rapidly decay in value. And while this is possible today, it often involves gobbling together four disparate solutions to get data and insights into and out of your warehouse. Syncari brings all the technology our customers need to create and distribute insights from customer data in one complete platform."
Eileen Treanor, CFO at Inkling on the role executives play in data strategy decisions-making:
“Sometimes we view data warehouses as panaceas. A big part of my role is to orient the business around the five or six pieces of data we need to consolidate that will give us more insights into how the business is doing. There has to be a strategic lens to any data initiative that can answer the C-level question of “what are we going to do with this data.” And let’s not try to do 500 things, let’s do five things. I always think it’s best to start small and build on that.”
To learn more, check out our full panel discussion, now available on-demand. 
