For external data to become embedded into decisions, products, and services, many managers will have to change their mind-sets and habits.

You may also want to involve some of your users and sponsors in debating the data management advantages and disadvantages of this kind of storage, but that’s about

a company or industry is still wrestling with data integration and quality for fundamental transaction data, it may take a while to get to big data.

Think of the computing horsepower needed by energy companies to process data streaming from smart meters, or by retailers tracking in-store smartphone navigation paths, or LinkedIn’s reconciliation of millions of colleague recommendations.

Storing large and diverse amounts of data on disk is becoming more cost-effective as the disk technologies become more commoditized and efficient. Storage in Hadoop environments is typically on multiple disks (solid state storage is still too expensive) attached to commodity servers. Companies like EMC sell storage solutions that allow disks to be

They’ll simply have to turn their gaze outward—to suppliers and their suppliers, to customers and their customers, and to business and political risks. And,

The idea is that as data begin to reveal a trend or finding—say, for example, weather data suggesting a drought that could lead to famine in a part of Africa—an analyst would post the hunch and the data on which it was based, and others could weigh in with new analyses and data. Such suggestive hypotheses have been described as “digital smoke signa

so they’ll be able to service it more effectively and know when it needs to be replaced.

The downside of machine learning is that it typically leads to results that are somewhat difficult to interpret and explain.