Beyond the hype, it’s now clear big data is an important route to growth and invention for large companies.
But big data success depends on your ability to get at the data, interrogate and productize data assets. That means enterprise architectures must serve two purposes.
The first is to make data available for analysis in a lab environment.
The second is to make data production ready for more specific, strategic projects and products.
Some early movers have already started building out these dual capabilities. They’ve been able to move their experiments from lab to factory environments in a repeatable, efficient and intelligent way.
This workbook aims to share their lessons and insights.
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