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Companies handling large volumes of data face a greater chance of erroneous linkages creeping into their analytics. Three key habits of data managers can reduce the risk of missed data connections.
While Big Data allows personalized service, collecting detailed data about real people (rightly) often raises concerns. But can analytics be used to improve security?
Algorithms are affecting many aspects of daily life, but most people have no clarity as to how they work — even in the companies that create and use them. But individuals and organizations need to carefully consider what this lack of transparency means when it comes to fairness and honesty in commercial interactions and decide where to draw the line on data ethics.
Organizations have made rapid gains in their ability to generate big data sets, but the ability of managers and executives to develop insights from that data has lagged behind. Data processing by artificial intelligence offers the prospect of speeding things up — but it also risks expanding the gap, as managers lack understanding of how AI reaches its data-based conclusions.
Big Data is moving to a new stage of maturity — one that promises even greater business impact and industry disruption over the course of the coming decade. Organizations are now combining the agility of Big Data processes with the scale of AI capabilities to accelerate the delivery of business value.
On March 15, 2017, MIT SMR held a webinar to share insights from our report, “Analytics as a Source of Business Innovation.” Many participants asked questions during the webinar that we didn’t have time for, so we decided to answer them in blog format instead. This post is the second set of responses.
Sam Ransbotham and David Kiron, co-authors of the 2017 MIT SMR Data & Analytics Research Report, “Analytics as a Source of Business Innovation,” shared the findings and insights from their research into the changing landscape for companies looking to embed data and analytics into their strategies, processes, and operations.
On March 15, 2017, MIT SMR held a webinar to share insights from our report, “Analytics as a Source of Business Innovation,” which summarizes our findings about the increased ability to innovate with analytics and its benefits across industries. Many participants asked questions during the webinar that we didn’t have time for, so we’ll answer some of them in blog format instead.
The 2017 Data & Analytics Report by MIT Sloan Management Review finds that the percentage of companies deriving competitive advantage from analytics increased for the first time in four years. Incorporating survey results and interviews with practitioners and scholars, the report finds that companies’ increasing ability to innovate with analytics is driving a resurgence of strategic benefits from analytics across industries. The report is based, in part, on MIT SMR’s seventh annual data and analytics global survey, which includes responses from 2,602 business executives, managers, and analytics professionals from organizations located around the world.
IoT promised, and delivered, a data deluge. But is the data any good? Survey results from MIT SMR’s recent internet of things research suggest that it is — but the most value goes to those who got into IoT early and have years of experience under their belt. The message to those considering IoT projects: Don’t wait.
Coauthors Thomas H. Davenport and Stephan Kudyba discuss the many ways for organizations to monetize data, including selling “data products” directly to consumers. A seven-step model shows the way real-life companies are developing those products and services.
Analytics requires structured data, but we may be introducing errors in the processing we use to produce it. An alternative: Do more up-front structuring before we even collect the data so that less processing is needed.
On Dec. 1 at 11 a.m. EST, join MIT SMR coauthors Thomas H. Davenport and Stephan Kudyba in a free, live webinar, where they will discuss their recent article, “Designing and Developing Analytics-Based Data Products.” The authors will look at the ways in which the internet of things, market forces, and evolving technology are changing how companies plan the development of data products. This new product category requires a reworking of the traditional phases of product development.
Professor Marshall Van Alstyne, coauthor of Platform Revolution, provides insights on how platform strategy and IoT combine to produce value for all players in the ecosystem, using real-life examples from organizations such as Apple, Uber, Airbnb, and more.
In a panel discussion, three MIT SMR editors, joined by the chief analytics officer at EY, discussed key insights from a series of in-depth case studies on how prominent organizations are using data and analytics to transform their operations.
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.
South African finance leader Nedbank is using data and analytics as a way to help the bank’s clients better understand their business. And the more data-oriented the bank becomes, the better able it will be to turn its developing prowess on itself. As the bank dives deeper into analytics, the same data it’s using for clients can help Nedbank better understand its own organization, employees, suppliers, and more.
In this webinar, the authors of MIT SMR‘s research report on the Internet of Things and a representative of one of the report’s primary examples, John Buccola, CIO of WASH Multifamily Laundry Systems, share experiences, findings, and insights from their exploration into how companies are deriving value from the Internet of Things.
Organizations across the business spectrum are awakening to the transformative power of data and analytics. They are also coming to grips with the daunting difficulty of the task that lies before them. It’s tough enough for many organizations to catalog and categorize the data at their disposal and devise the rules and processes for using it. It’s even tougher to translate that data into tangible value. But it’s not impossible, and many organizations, in both the private and public sectors, are learning how.
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