- Opinion & Analysis
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Companies have yet to apply analytics to human resources — but that’s about to change. And lessons learned in applying analytics to customer-focused areas can help avoid mistakes in strategic workforce decisions.
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In a video panel and Q&A, MIT SMR editors discuss key insights from a recently completed series of in-depth case studies on how prominent organizations are using data and analytics to transform their operations. They review Intermountain Healthcare, GE, Nedbank, and the City of Amsterdam’s efforts to become more data driven. This set of diverse organizations offers a unique perspective on the challenges and opportunities associated with becoming a data-driven organization.
The ability of artificial intelligence to sift through mountains of data and identify patterns — and problems — in real time is its key value for business. Using AI to predict failures and take action to prevent them will become commonplace in the very near future. But it can also offer insights into human behavior to help managers improve customer service and employee relations.
Digital transformation is happening all around us, but it’s the foundation for a much more profound transformation still to come. With huge challenges facing humanity on many fronts — climate, disease, population, food and water — we need cognitive technologies to augment human problem-solving capabilities. And those technologies are almost here.
In a conversation with MIT SMR’s David Kiron and Sam Ransbotham, associate professor of information systems at the Carroll School of Management at Boston College and guest editor for the Data and Analytics Big Idea Initiative for the MIT Sloan Management Review, Jeffrey Bohn, chief science officer at State Street Global Exchange discusses how he is developing better trading and risk strategies for clients using State Street’s proprietary data and analytics.
Analytics capabilities can greatly expand a company’s ability to innovate — but what do you do when the talent you need just isn’t available? Agribusiness giant Syngenta, faced with an insurmountable analytics talent bottleneck, turned to crowdsourcing. Using a series of contests, it outsourced the development of a set of award-winning analytics tools to improve its decision making — and learned, in the process, some key factors supporting successful crowdsourcing.
Although intuitively appealing, strategy maps and models such as the service profit chain have a common pitfall: They encourage managers to embrace general assumptions about the drivers of financial performance that may not stand up to close scrutiny in their own organizations. A more rigorous analytic approach called performance topology mapping may help managers avoid these assumptions, as well as the strategic mistakes they promote.
Nicholas Bloom, William Eberle Professor of Economics at Stanford University, conducted an extensive study of 30,000 US factories, and found that two practices, underpinned by innovative software and IT systems, stand out in highly effectively managed operations: monitoring and incentives.
There are many reasons to believe we are on the crest of substantial progress with even the most challenging of last mile deliveries. Innovative models such as smart locker systems, the use of electric vehicles, and on-demand fleet services such as UberRUSH are being explored. The MIT Megacity Lab is helping identify customer-specific insights about how supply chains deliver products to urban customers and finds that autonomous delivery vehicles, while still years from wide-scale implementation, hold game-changing promise.
Using data and analytics to understand the complexities of modern business has become not only common, but essential. Gahl Berkooz joined Ford Motor Co. in 2004, eventually becoming head of data and governance and a member of the company’s global data insights and analytics skill team. Berkooz became acutely aware of how important analytics is to the company’s ability to thrive in the global marketplace. “What it boils down to,” he told MIT SMR’s Michael Fitzgerald, “is that we know how to make decisions. It’s about finding the opportunities to bring data and analytics to make better decisions.”
It has become a truism that the pace of work is faster than ever, as digital technologies speed up communication and operational processes in a story of unending progress. But increased speed has not translated into increased rates of productivity growth. Since 2004, growth rates have slowed not just in the US but across the world. Chad Syverson, J. Baum Harris Professor of Economics at the University of Chicago’s Booth School of Business, explains what the implications are, and why the benefits of new technologies are not straightforward.
The 2016 MIT SMR/SAS Data and Analytics report, “Beyond the Hype: The Hard Work Behind Analytics Success,” finds that competitive advantage from analytics is declining — but that organizations achieving the greatest benefits have figured out how to ensure that the right data is being captured. In this webinar, the authors of the report explain how companies are making this transition and which are seeing the most success.
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.
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.
Most large corporations are saddled with fragmented analytical processes, limiting their ability to operate with agility, flexibility, and insight. As a result, larger firms are often challenged when it comes to innovation and responsiveness. But Big Data approaches that enabled the flexibility and rapid growth of newer, smaller firms are being adopted by mainstream corporations. The goal: overcome legacy challenges and introduce greater corporate speed.
At the alcohol beverage company Constellation Brands, graphic presentations of data are making it easier for sales people to see how they’re performing. In an interview with MIT Sloan Management Review, Joseph D. Bruhin, the company’s CIO, says that measuring marketing and sales efforts is a particular challenge in the alcohol industry — but one that his team has come up with a solution to. “Visibility of data is a critical piece,” he says. “We came up with a solution that’s really driven predominantly by information technology.”
The process of managing a data science research effort “can seem quite messy,” writes MIT Sloan’s Roger M. Stein. That can be “an unexpected contrast to a field that, from the outside, seems to epitomize the rule of reason and the preeminence of data.” While businesses are hiring more data scientists than ever, many struggle to realize the full organizational and financial benefits from investing in data analytics. This is forcing some managers to think carefully about how units with analytics talent are structured and managed.
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