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Philip Kim discusses how his division utilizes data to continuously improve performance — whether that’s to grow sales, decrease costs or improve performance — and in the process, democratize analytics.
Using geo-coding and analytics to reshape operations and care is taking hold in healthcare systems. A recent conference on healthcare and geographic information systems from GIS software company Esri highlighted how the Louisiana Department of Health and Hospitals and the Veterans Health Administration use technology to gain better understanding of patient and health trends. The related area of social work is also beginning to see some uptake of the technology.
More than half of managers surveyed strongly agree that their organizations need to step up analytics use, according to a 2013 global survey by MIT Sloan Management Review and SAS Institute. In addition, survey data suggests that in companies where analytics has improved the ability to innovate, managers are more likely to share data with partners and suppliers.
Words have become data; the physical states of our machinery have become data; our physical locations have become data; and even our interactions with each other have become data. Three recent books offer expert perspectives on the increasing power and importance of analytics.
“Big Data in Manufacturing” was the theme of a daylong conference held in Cambridge, Massachusetts, in November 2013 and sponsored by the MIT Forum for Supply Chain Innovation and the Accenture and MIT Alliance in Business Analytics. But the speakers’ insights weren’t restricted to manufacturing.
While some industries like health care and retail are starting to see the transformational potential of big data and predictive analytics, these strategies haven’t quite panned out for supply chain managers. Why? Two big barriers: The cost of hiring skilled employees and the complexity of connecting nodes across an extended supply chain network. New research suggests that the convergence of data science, predictive analytics and big data have the potential to transform the way in which supply chains leaders lead, and supply chains operate.
Researchers are proposing a new method to limit privacy harms from predictive analytics: Apply due process that would determine, legally, the fairness of an algorithm. While a new framework may be a step forward for individual privacy, what does it mean for organizations that collect and utilize big data through a predictive analytics lens? A couple of things — should data-oriented due process pass policy and legislative muster.
Current thinking has big data and analytics turning companies into data-driven powerhouses. But if companies don’t know how to use data well, it can actually prevent companies from making the transition to data-driven operations. Jeanne Ross, director of MIT’s Center for Information Systems Research, says that companies can build cultures that encourage people, even low-level employees, to work well with data by using evidence-based management.
A recent data and analytics survey sheds light on what large organizations are actually doing with big data initiatives. The survey of nearly 100 senior executives from Fortune 1000 companies found that executives are reporting anincreasing commitment to big data projects. As well, most executives, 70%, are looking to improve their “time to answer” with big data.
The 1997 sci-fi film Gattaca presents a society where DNA determines social class. A registry identifies and creates genetically superior individuals — termed “valids” — while winnowing out their naturally conceived “in-valid” counterparts. The “valids” have a predetermined career (and life!) path, unalterable by desire, capability, circumstance or happenstance.
But how close are fact and fiction? Can genetics, biometrics and, essentially, predictive analytics be utilized to determine an individual’s path?
The growing importance of algorithms to business and society is a little discussed feature of our increasingly digital world. These algorithms are the underpinnings of NSA surveillance, online search engines, corporate security, modern matchmaking and other activities in both the private and public sector. They can be a source of competitive advantage (think Google), play an operational role or drive marketing. Just what are algorithms, how are they used, and what happens when influential algorithms go wrong?
Smart cities are popping up around the globe, from China where 193 smart cities are being piloted, to Europe, the U.K. and the U.S. Their development involves a wide scope of technology, everything from renewable energy, green buildings and smart grids to traffic management, urban security and medical technology. The goal: urban sustainable development and economic growth. Opportunities abound to be part of this global urban revitalization effort. The question is, at what cost to participants?
On the website of energy analytics startup Pilio, CEO and cofounder Catherine Bottrill says that it is a combination of technology, economics and politics that are instrumental in environmental problem solving. However, there is another ingredient necessary, says Bottrill: People. Bottrill is combining behavioral science with energy analytics to help organizations better understand — and manage — their energy use.
Social networking and digital advertising are colliding at a dizzying rate. Facebook, which has over 1 billion users, is launching video ads. Twitter, with more than 200 million users, just bought MoPub, a digital advertising platform that essentially creates an ad space that is sold and delivered every time a user views a page. What does this all mean for the relationship between businesses and consumers? The short answer: Market manipulation.
Recommendation engine StyleSeek is relying on data and analytics to drive business and fashion decisions — every single one of them. With about 50,000 actively registered users — impressive considering the company recently came out of private beta — and close to 200 retailers on board including the likes of Nordstrom, Macy’s and Anthropologie, StyleSeek may have tapped a new approach to a longstanding industry.
It is an understatement to say LinkedIn is growing like a weed. With 238 million members in over 200 countries, 2.8 million active company profiles, and 1 million professionally oriented groups, LinkedIn has become the world’s largest professional networking site. Deepak Agarwal, LinkedIn’s director of relevance science, explains how his company uses data and analytics to sustain this growth.
Recent research out of the Department of Operations and Information Systems at the University of Utah, Salt Lake City, and the Department of Management Information Systems, Eller School of Management at the University of Arizona, Tucson, asks a seemingly simple question about organizations’ data collection and usage that could have some big implications on your own data techniques. The question: When is the right time to refresh data to support organizational decision-making?
We’re in a new world of omnichannel retailing that includes physical, online and mobile channels. And those channels are blurring. In a recent AllAnaltyics video and web chat, Analytics in the Age of Omnichannel Retailing, researchers Erik Brynjolfsson, Yu Jeffrey Hu and Mohammad Rahman discussed the challenges facing retailers.
Facts: 900 million. Active sources: more than 100,000. Data sets: 30,000, with 200 million time series and 1.5 billion fact values. Link all these data sources together and what do you get? Timely, if not crucial, contextual information about markets, trends, competitors, products and consumer opinions. This is the promise of DOPA, a project funded under the umbrella of the European Union.
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