Organizations that share data and innovate say governance helps. Percent of respon- dents who agree to a moderate or great extent that their organization’s data security practices lib- erate them to create value from analytics Lo w Ability to innovate High Lo w Level of sharing 19% 38% 14% 39%
ANALYTICS AS A SOURCE OF BUSINESS INNOVATION • MIT SLOAN MANAGEMENT REVIEW 13 and protocols,” says Peter Levin, a senior research scientist at Intel Corp. “Stewardship defines both data and algorithm access, limits, and exchange rules. Protocols describe the metadata needed to provide the context.” Good governance practices promote effective use of data. • Integrating data from multiple sources can slow down the data flow, as each step can add delay. At the Federal Bureau of Investigation, maintaining security — a form of preventive maintenance in the public sector — often depends on many dif- ferent groups sharing data with one another in a timely manner. “Security events may be con- nected even though initially they may appear isolated,” says Kevin Swindon, an FBI special agent and supervisor of the Boston Division CYBER Program. “Analytics now lets us uncover patterns, and these patterns may provide inves- tigative clues. However, speed is critical. As we have better defined our processes around data sharing, we’re able to focus on these types of inci- dents quickly, rather than spending time figuring out the mechanics around the data.” Good gov- ernance practices can also improve the speed of innovative use of data. Smart machines create more time for innovative thinking Smart machines that can take on tasks that tra- ditionally required a human have captured the popular imagination. But the immediate ben- efits from smarter machines are not in human replacement. As Tom Davenport, the President’s Distinguished Professor of Information Technology and Management at Babson College, has written, “Of course, automation technologies bring fears of job loss. I believe that when an organization adopts these tools, it’s a bad idea to put the primary focus on eliminating human jobs.” 8 Instead of elimination, liberation and augmentation more aptly describe the implications of automation for some segments of the labor market. For example, machine-learning techniques applied to dull, repetitive, data-cleaning work allow computers to learn from patterns they discern in large datasets, enabling companies to automate some analytical tasks and freeing up data experts to work on higher-value-added tasks. Data experts are just one of many pools of workers that au- tomated work flows may affect in ways that are not yet known. 9 For several years, the more advanced corporate users of analytics in our surveys have told us they are using analytics to automate processes in their companies.
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- Spring '17
- Hyejun lee
- Data Management, MIT Sloan School of Management, mit sloan management