- 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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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.
There is a growing belief that sophisticated algorithms paired with big data will find relationships independent of any preconceived hypotheses. But in businesses that involve scientific research and technological innovation, this approach is misguided and potentially risky, as spurious correlations and “noise” may lead analysts astray.
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.
Product monitoring enabled by the internet of things can unleash cost savings, service improvements, and better customer experiences. But before this revolution can move forward, both the quality and collection of performance data need to be greatly improved. A research project at the MIT Center for Transportation & Logistics carried out in collaboration with OnProcess Technology underlines the potential for fresh approaches.
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.
A new case study by MIT Sloan Management Review, “A Data-Driven Approach to Customer Relationships,” details how the South African bank Nedbank is using its rich access to a trove of transactional data from credit card use — from the time of transactions and size of purchases to retailer locations, and even specific details like the age, gender, race, marital status, and income bracket of some users — to help merchants make strategic decisions to better serve those customers.
South Africa’s Nedbank is a leader in its market — but to stay in that position, it needed to identify new ways to serve its existing business clientele as well as attract new customers. Its solution: Use the extensive transaction data the bank collects to help customers improve their service.
IoT, a “worldwide platform of platforms,” offers power for companies who can capture its value. Watch as Professor Marshall Van Alstyne, author of Platform Revolution, offers insights on how platform strategy and IoT combine to produce value for all players in the ecosystem, using his research and real-life examples. Learn what what platform strategy is and how companies are using IoT to advance their platform strategies
As demand for big data technologies grows, so does the problem of finding sufficient skills. Result: Talent shortages could limit the rate of productivity growth. Research shows that labor-market factors have shaped early returns on investment in big data technologies such as Hadoop, a framework for distributed processing of large data sets. It turns out that when know-how is scarce, organizations that invest in new IT or R&D derive significant benefits from the related investments of other organizations.
What’s happening this week at the intersection of management and technology: Smart earbuds at work; adding cybersecurity to the executive job description; diving into data lakes
Eight out of 10 executives surveyed say that as the business value of data grows, the risks their companies face from improper handling of data increase exponentially. While digital advancements enable new opportunities for businesses to compete and thrive, they also create increased exposure to systemic risks. Success in the digital age will require a new kind of ethical review around how companies gather and use data.
The combination of new analytical capabilities and burgeoning data assets are being used to form value-added “data products.” Such products have powered rapid growth in the value and success of online companies, but the expansion of analytics means the standard model for developing these products needs to evolve. An updated model needs to reflect new “time to market” expectations and input from a variety of stakeholders.
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