- Research Highlight
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What is the value of data? Many businesses don’t yet know the answer to that question. But going forward, companies will need to develop greater expertise at valuing their data assets.
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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.
An infographic provides highlights of MIT SMR‘s 2016 Internet of Things report and illustrates the three key areas business leaders need to address in order to realize the IoT value proposition.
The city of Amsterdam is becoming a model for “smart cities” through its innovation efforts to improve the lives of its employees and inhabitants. This case offers insights into what it takes to achieve these goals, including: taking the crucial step of doing an initial inventory of data available; using and integrating data from the private sector; and experimenting and learning from pilot projects.
Blockchain is a data storage technology with implications for business that extend well beyond its most popular application to date — the virtual currency, Bitcoin. Managers need to build their organization’s absorptive capacity around this topic for at least three reasons: (1) the potential effects on organizational value chains, (2) communication within and between organizations, and (3) benefits from cooperation.
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.
Thanks to social media and an increasing flood of data, the capacity to generate causes and controversies almost instantly has become the new norm in today’s “super-transparent society.” Individuals and organizations produce a voluminous, mostly involuntary, “digital exhaust,” which reveals much more about them than they think it does. Most business leaders have not yet come to grips with the new reality — and what it means for their organizations.
With analytics as a hammer, so many questions can start to look like nails. It is difficult for organizations to know what to do. But the “should” in “What should we do?” goes beyond just selecting what to hammer on for maximum insight. Companies need to pay attention to the ways in which the possibilities that analytical abilities create involve responsibilities as well.
Hugh Scandrett, VP of engineering for EnerNOC, is bringing transparency to an energy system that works against clarity. The goal: help companies realize more cost savings and cut back on energy usage. In a Q&A, Scandrett says that one big issue for companies is predicting future demand. “We predict a company’s usage based on analytics that look at weather, degree of sun azimuth, and a whole set of other parameters,” he says. “We then can provide techniques for minimizing peak usage, like pre-cooling a building.”
At The Coca-Cola Company, one of the big challenges is how to understand customers who are a long pipeline away in the inherently intermediated world of hundreds of Coke bottlers. That means moving toward newer technologies to do more forward-looking analytics versus backward-looking analytics, says the company’s Remco Brouwer and Mathew Chacko.
While analytical skills are improving among managers, the increasing sophistication of analyses is outpacing the development of those skills. The resulting gap creates a need for managers to become comfortable applying analytical results they do not fully understand. A 2014 survey by MIT Sloan Management Review, in partnership with SAS Institute Inc., highlights the ways that companies can address this problem by focusing on both the production and consumption sides of analytics.
Many companies have maintained a competitive advantage through analytics for many years — even decades. Those companies include Wal-Mart, ABB Electric, Procter & Gamble, American Airlines, and Amazon. Peter C. Bell (Ivey Business School) writes that “research over a 30-year period suggests that there have been five basic ways in which companies have sustained an advantage generated through analytics.” Tactics include keeping your company’s analytics secret and applying analytics to the right problems.
Analytics acts as an amplifier for business processes. In business, as in music, “louder” does not always mean “better,” so companies seeking to increase their analytics capacity should keep in mind four principles that underscore its limitations for business.
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.
At the Big Data Innovation Summit, Kaiser’s John Mattison detailed his expectations for the future of health care. He envisions a data-driven system that relies on genetic data in combination with personal data from the patient regarding exposures and lifestyle to help physicians predict health risks. But he also warned that companies have a great deal of work to do to meet the challenges of health care’s digital transformation.
Some people suggest that the concept of “big data” is nearing the end of its fifteen minutes of fame. They couldn’t be more wrong — because big data isn’t just about managing social media, unstructured data or massive data sets. It is an approach to data and analytics that finds new ways of looking at information — and it’s here to stay.
Wealth once was measured by the amount of land, employees or equipment you had. Today we are on the cusp of a period in which another factor is an indicator of potential wealth: how much information you have. Information has the potential to be a valuable asset, and a new framework, dubbed “the information footprint,” presents a way for companies to assess their information assets and the opportunities it gives them for new value creation.
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