This book is an important one for Chinese government and business organizations. Big data and analytics based on it promise to change virtually every industry and business function over the next decade. Any organization that gets started early with big data can gain a significant competitive edge. Just as early analytical competitors in the“small data”era (including Capital One bank, Progressive Insurance, and Marriott hotels) moved out ahead of their competitors and built a sizable competitive edge, the time is now for firms to seize the big data opportunity.
The pervasive future of big data is enabled by the pervasive nature of sensors and microprocessors today. We are entering into the ubiquitous computing age now. Virtually every mechanical or electronic device can leave a trail that describes its performance, location, or state. These devices, and the people who use them, communicate through the Internet—which leads to another vast data source. When all these bits are combined with those from other media—wireless and wired telephony, cable, satellite, and so forth—the future of data appears even bigger.
The availability of all this data means that virtually every business or organizational activity can be viewed as a big data problem or initiative. Manufacturing, in which most machines already have one or more microprocessors, is already a big data situation. Consumer marketing, with myriad customer touchpoints and clickstreams, is already a big data problem. Google has even described the self-driving car as a big data problem.
Governments have begun to recognize that they sit on enormous collections of data that wait to be analyzed. We can see big data and analytics initiatives among governments in Asia. Last year, Singapore helped to launch the Deloitte Analytics Institute (DAI). This new institute is sponsored in part by the Economic Development Board of the Singapore government. The DAI's goal is to do research and thought leadership on the application of analytics to government and business. Singapore has also sponsored several university-based research initiatives on analytics and big data.
Organizations that want to pursue big data opportunities need to begin working along several fronts. From a technology standpoint, they need to acquire and develop tools to manage both structured and unstructured data in massively parallel server environments, either on premise or in the cloud. They need to select analytical software to make sense of the data. Perhaps most importantly, they need to hire or develop the human talent to manage and analyze big data. These people are typically known as“data scientists”—hybrids of hacker and quantitative analyst—and they are in extremely short supply. The wise executive will develop approaches to securing the best people.
Some companies are beginning to realize the extent of the opportunity, and to act upon it now. GE, for example, has committed to spend more than $1.5 billion to develop its Global Software and Analytics Center in the San Francisco Bay Area as a part of its Global Research organization. The company plans to hire at least 400 computer and data scientists at this location, and has already hired 180. Globally GE has over 10,000 engineers engaged in developing software and analytics products and services, and their efforts will be coordinated through common analytics platforms, training and leadership education, and innovative offerings. A significant portion of big data activities at GE will be focused on industrial products, such as locomotives, turbines, jet engines, and large energy generation facilities.
The size and ambition of GE's commitment should set the tone for other organizations that want to succeed with big data. Chinese government agencies and firms are noted worldwide for their ambitious plans in other domains, and these should be extended to big data. Zipei Tu's book will help to guide government and business organization's efforts in this important area.
Thomas H. Davenport