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As part of its plan, the IT team conducted a wide-ranging data assessment to determine who has access to what data, and each data source’s encryption needs. There are a lot of variables that determine what should go into the data lake and what will probably stay on premise,” Pruitt says.
There are countless examples of big datatransforming many different industries. There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. Does Data Virtualization support web dataintegration?
These strategies minimize risks, streamline deployment processes, and future-proof datatransformations, allowing businesses to trust their data before it ever reaches production. Helps maintain business rule consistency and avoid regressions in data quality overtime. Summary: Why thisorder?
If after rigorous analysis you have determined that you have evolved to a stage that you need a data warehouse then you are out of luck with Yahoo! If you can show ROI on a DW it would be a good use of your money to go with Omniture Discover, WebTrends Data Mart, Coremetrics Explore. and Google, get a paid solution.
We are looking for good ROI use cases for AI, and we saw this as a worthy use case to chase,” he adds. There is no more waiting around for quality data. So, from a software development perspective, the ROI is huge. Developers get access to production quality data whenever they need it.
Return on Investment Now we bring it all together to calculate the ROI on embedded analytics. Costs: The investment in developing and maintaining the solution. “-1”: The formula assures that a positive ROI is achieved only when benefits exceed the costs. The formula looks like this: ($750k / $250k) = 3, so the ROI is 200 percent.
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