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Employee odometers and technostress; big data trumps analytics; profiling the chief data officer.
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Organizations across an increasing number of sports and levels of competition are capitalizing on data to gain a competitive edge. Indeed, few industries have implemented data-driven decision making as successfully as sports. And learnings from the sports analytics revolution are applicable to a broad range of other industries.
Many major cities recognize the opportunity to improve urban life with data analytics, and are exploring how to use information technologies to develop smarter services and a more sustainable footprint. Amsterdam, which has been working toward becoming a “smart city” for almost 7 years, offers insights into the complexities facing city managers who see the opportunity with data, but must collaborate with a diverse group of stakeholders to achieve their goals. The city’s chief technology officer, Ger Baron, makes it clear that their efforts are still early days: “I can give you the nice stories that we’re doing great stuff with data and information, but we’re very much at a starting point,” he says.
The past several years have been period of exploration, experimentation, and trial and error in Big Data among Fortune 1,000 companies, and the result has been a different story. For these firms, it is not the ability to process and manage large data volumes that is driving successful Big Data outcomes. Rather, it is the ability to integrate more sources of data than ever before — new data, old data, big data, small data, structured data, unstructured data, social media data, behavioral data, and legacy data. Guest blogger Randy Bean, CEO of NewVantage Partners, explains why the “variety challenge” has emerged as the top data priority.
If you really want to create value, forget about burning platforms and start building them. A platform, explain professors Geoffrey Parker and Marshall Van Alstyne, and Sangeet Choudary, founder and CEO of Platform Thinking Labs, in Platform Revolution: How Networked Markets are Transforming the Economy and How to Make Them Work for You, is a “business model that uses technology to connect people, organizations, and resources in an interactive ecosystem.”
The 2016 Data & Analytics Report by MIT Sloan Management Review and SAS finds that analytics is now a mainstream idea, but not a mainstream practice. Few companies have a strategic plan for analytics or are executing a strategy for what they hope to achieve with analytics. Organizations achieving the greatest benefits from analytics ensure the right data is being captured, and blend information and experience in making decisions.
This week’s Tech Savvy looks at what’s happening in wearables at work, virtual reality in hiring, enhancing big data ROI, and digital transformation preparation. For instance, in a Netherlands warehouse, employees wearing smart glasses to pick orders show a 25% improvement in efficiency. And in big data, Wharton’s Eric Clemons notes that “Where big data analytics may create local fiefdoms, online social networks create distributed pockets of autonomous connection, affiliation, and even affection.”
Unlike agriculture, where cutting-edge technologies are being aggressively adopted, forestry and its related industries are something of a technology laggard. But the prospect of the industry using sensors in the field, both in sawmills and even embedded within trees themselves, is emerging. Eric Hansen and Scott Leavengood, both professors at Oregon State University’s Wood Science and Engineering department, discuss how the Internet of Things could help drive efficiency and improve quality in the forestry sector.
Analytics offers managers a great way to fine-tune processes, but too many executives focus on metrics at the expense of the bigger picture. The blinders and focus that work well to optimize the details of a problem may prevent managers from seeing other options, and intense focus on a narrow measure can address only the well-specified puzzle — resulting in a myopic view of the problem. Executives who desire bigger breakthroughs need to encourage exploration.
By studying data from email archives and other sources, managers can gain surprising insights about how groups should be organized and about which communications patterns are most successful. Anonymized analysis of internal information communication found that creative people, for instance, work more productively on projects with strong leaders than on collaborations without a clear leader. In addition, in many situations, groups of leaders taking turns worked better at sparking creativity.
Effectively managing and coordinating supply chains will increasingly require new approaches to data transparency and collaboration. Supply chains in coming years will become even more “networked” than they are today — with significant portions of strategic assets and core capabilities externally sourced and coordinated. Already, progressive companies are developing novel solutions to the dilemma of data transparency by using data “cleanrooms” and digital marketplaces.
General Mills brought a data scientist into its Consumer Insights group because it wanted to use its existing data more effectively. The company thought it was making decisions based too much on outside data at the expense of what it knew. But figuring out what the company actually knew about its consumers was the challenge facing Wayde Fleener as he came on board. In an interview with MIT SMR’s Michael Fitzgerald, Fleener talks about how he got started in building a Big Data practice within his division.
The Echo Nest, a self-described “music intelligence” company recently acquired by Spotify, uses machine-learning technology to connect people with music. “At our core,” says CEO Jim Lucchese, “what we’re trying to do is what a great deejay does, or the friend that you rely on musically: to better understand who you are as a fan.” In a Q&A, Lucchese describes how the company merges machine learning and cultural analytics to describe music in an analytics-friendly way and help users find new music they’ll enjoy.
Although workers and consumers generate two-thirds of all new data, that’s about to change. Sensors and smart devices — from traffic lights and grocery store scanners to hospital equipment and industrial sensors — are beginning to generate an enormous wave of data that will increase the digital universe ten-fold by 2020. Guest blogger Randy Bean, CEO of NewVantage Partners, explains what this means for executives trying to adapt to a rapidly changing, data-centered business environment.
When you’re dealing with data on the massive scale that a company like GE uses, a data warehouse just isn’t big enough to house it all. And organizing it ahead of analysis is more of a burden than a help. GE’s CIO Vince Campisi explains to MIT Sloan Management Review why his company is now storing data in a data lake — and how that approach changes the kind of human resources his company is looking for.
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