- 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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Companies can monetize their data by improving internal business processes and decisions, wrapping information around core products and services, and selling information offerings to new and existing markets. Adopting any of these approaches, however, requires management commitment to specific organizational changes and targeted technology and data management upgrades.
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.
Effectively managing and coordinating supply chains will increasingly require new approaches to data transparency and collaboration. Supply chains in coming years will become even more “networked” than they are today — with significant portions of strategic assets and core capabilities externally sourced and coordinated. Already, progressive companies are developing novel solutions to the dilemma of data transparency by using data “cleanrooms” and digital marketplaces.
Problems with data quality come from a lot of sources — short-term solutions, mergers or acquisitions, or even the mundane complications of living in a complex society. The “stench” that develops when data quality declines can create serious issues for data-driven business. If a foul odor is emanating from your data, one solution might lie in refactoring analytics processes.
“Our world is awash in data, and data is not the same thing as facts,” writes Boston College’s Sam Ransbotham. “While data seems to promise objectivity, instead it requires analysis — which is replete with subjective interpretation.” Ransbotham argues that while having data is a necessary step towards making objective decisions, it’s a myth that data is objective. Moreover, findings that counter current thinking provide organizations with opportunity for distinction, differentiation and advantage.
Intermountain Healthcare is leading the way in data driven healthcare. In an example from Intermountain’s own operating rooms, the use of data to measure the impact of standardized surgeon attire on infection rates resulted in a significant drop in those rates. The infection control scenario is just one result from decades of work at Intermountain to build a data culture. Over the years, clinicians have learned to work together on a concerted effort to bring data based insights to clinicians and managers.
On May 7, 2015, we held a free, live webinar to share the findings and insights from the latest MIT Sloan Management Review Data and Analytics Big Idea Initiative research report, “The Talent Dividend.” The report presents our findings on the role of analytics talent in creating competitive advantage. At the end of the webinar, many participants asked questions. Unfortunately, we didn’t have time to answer them all during the webinar itself. So instead, we’ll answer some of the questions this month, and some next month.
What can companies do to help fill their data scientist gap? That was the topic at a conference hosted by the MIT Center for Digital Business.
“Creatives” such as marketers rely more on intuition than on data analysis or even consultation with colleagues when making decisions, says an Accenture Customer Analytics Survey. CIOs can combat that resistance by IDing metrics that matter and finding advocates open to new approaches.
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