“Sentiment analysis” promises not just to identify workplace pain-points, but anticipate them. How employees use tools like Slack, Sharepoint, Yammer, email, blogs, or LinkedIn to share thoughts and coordinate actions invariably yields treasure troves of data worth mining. Algorithmically examining the language people use (or don’t use) when they communicate (or don’t communicate) about the work they are doing (or not doing) can be extraordinarily revealing. In some cases, sentiment analytics prove predictive: they point to people, programs, and projects requiring immediate intervention and oversight.
In the 1670s, in Delft, Netherlands, a scientist named Anton van Leeuwenhoek did something many scientists had done for 100 years before him. He built a microscope.
This microscope was different, but it was not extraordinary. Like so many inventions, he borrowed and tweaked his predecessors' ingenuity. But when he looked through this microscope, he found things that did seem extraordinary. He called them "animalcules," microbes in water droplets and human blood that ultimately provided the foundation for the germ theory of disease and eventually inspired a host of medicines and treatments.
The Leeuwenhoek discovery is crucial to our understanding of innovation, not only because it changed the face of biochemistry, but also because it represents a fundamental theme of discovery.
Breakthroughs in innovation often rely on breakthroughs in measurement.
THE DATA BOOM
Today businesses can measure their activities and customer relationships with unprecedented precision. As a result, they are awash with data. This is particularly evident in the digital economy, where clickstream data give precisely targeted and real-time insights into consumer behavior.
In turn, customers are acting as unwitting business consultants for these companies. Our purchases, searches, and online activities are being tracked to improve everything from websites to delivery routes and drug manufacturing.
Anyone with access to a Web browser can get summaries of billions of keyword searches, and this information is highly predictive of present and future economic activity, such as housing purchases and prices. Mobile phones, automobiles, factory automation systems and other devices are routinely instrumented to generate streams of data on their activities, making possible an emerging field of "reality mining" to analyze this information. Manufacturers and retailers use radio-frequency identification (RFID) tags to deliver terabits of data on inventories and supplier interactions and then feed this information into analytical models to optimize and reinvent their business processes.
U.S. News & World Report Five charts on the US economy's big data makeover Financial Times Revisions of this scale occur only once every five years when the BEA not only updates its data but revises its methods as well and applies the new...
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