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not share data between applications. In order to analyze this data, each business unit in charge of a database would have to extract, transform, and load (ETL) the data into its own data mart, which is a smaller, stand-alone version of a data warehouse. Then, to develop business intelligence across an organization or to exe- cute analytics against company-wide data, data would have to be sent through the ETL process from the individual data marts into a central data warehouse. This data would then be prepared for analytics. The entire process was slow and cumbersome. Data formats varied across the applications, requiring further model- ing and transformation into a new data warehouse. But, at least you had access to the data because it was in your own data center.
The bottom line is that no company survives without some level of internal data sharing.
The Business Value of Data Sharing for Organizations
Data sharing across and beyond an organization consists of four basic work flows:
» Across lines of business (LOBs): Sharing data between organizations within the same enterprise
» Between enterprises: Outbound data sharing to another, separate enterprise to benefit your business
» Between enterprises: Receiving inbound data shared from another enterprise to benefit your business
» Monetizing data: Sharing live data as a service so data consumers can enrich their own, existing data
Across LOBs and groups within the
same enterprise
Within the same enterprise, organizations depend on email, spreadsheets, shared network drives, application programming interfaces (APIs), and other methods for communicating and for sharing data. Along with facilitating day-to-day business,
18 Data Sharing For Dummies, Snowflake Special Edition
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