The rise of data-driven decision making is real but uneven — Kristina McElheran and Erik Brynjolfsson

Kristina McElheran, MIT Initiative on the Digital Economy Visiting Scholar

Kristina McElheran, MIT Initiative on the Digital Economy Visiting Scholar


 Professor of Information Technology, Director, The MIT Initiative on the Digital Economy

Professor of Information Technology,
Director, The MIT Initiative on the Digital Economy

From Harvard Business Review

Growing opportunities to collect and leverage digital information have led many managers to change how they make decisions – relying less on intuition and more on data. As Jim Barksdale, the former CEO of Netscape quipped, “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” Following pathbreakers such as Caesar’s CEO Gary Loveman – who attributes his firm’s success to the use of databases and cutting-edge analytical tools – managers at many levels are now consuming data and analytical output in unprecedented ways.

This should come as no surprise. At their most fundamental level, all organizations can be thought of as “information processors” that rely on the technologies of hierarchy, specialization, and human perception to collect, disseminate, and act on insights. Therefore, it’s only natural that technologies delivering faster, cheaper, more accurate information create opportunities to re-invent the managerial machinery.

At the same time, large corporations are not always nimble creatures. How quickly are managers actually making the investments and process changes required to embrace decision-making practices rooted in objective data? And should all firms jump on this latest managerial bandwagon?

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You can prevent a ‘Panama Papers’ scandal at your law firm — Lou Shipley

MIT Sloan Lecturer Lou Shipley

MIT Sloan Lecturer Lou Shipley

From Huffington Post

The data breach at the law firm of Mossack Fonseca in Panama sent shock waves around the world recently with the prime minister of Iceland stepping aside, Swiss authorities raiding the headquarters of the Union of European Football Associations, and relatives of the president of China linked to offshore companies. The size of the breach was also shocking with 2.6 terabytes of data leaked. That’s 30 times bigger than the WikiLeaks release or the Edward Snowden materials. However, the most shocking part of the “Panama Papers” story is that the breach and exploit of the popular open source project Drupal was totally preventable.

Everyone knows that law firms manage large amounts of highly sensitive information. Whether the data involves an individual’s estate plan, a startup’s patent application, or a high-profile merger and acquisition, clients expect their information to be secure. Indeed, lawyers are required to keep this information both confidential and secure. Yet, despite the very high level of security owed this information, many firms lack an IT staff and outsource the creation and maintenance of their data management and security services. Once outsourced, there is an assumption that someone else will effectively manage the data and ensure its security.

This is many firms’ first mistake. Even if they aren’t managing their own IT, law firms still have an obligation to make sure that data is properly secured. This means asking frequent questions about security and ensuring that the vendor is implementing reasonable security measures.

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Why managing data scientists is different — Roger Stein

MIT Sloan Senior Lecturer Roger Stein

MIT Sloan Senior Lecturer Roger Stein

From the MIT Sloan Management Review

While businesses are hiring more data scientists than ever, many struggle to realize the full organizational and financial benefits from investing in data analytics. This is forcing some managers to think carefully about how units with analytics talents are structured and managed.

How can organizations realize the promise of the evolving disciplines that we broadly call analytics?

Although financial firms were among the first to recruit “quants” to use sophisticated mathematical models and high-powered computing hardware, analytics groups have now taken hold in areas ranging from health care to political campaigns to retailing to sports. Organizations like these can benefit from the insights gained by financial service firms on how best to manage teams doing advanced analytics. It requires skills and philosophies that are different from those that arise in managing other groups of smart professionals.

Rather than just involving oversight and planning, managing a data science research effort tends to be a dynamic and self-correcting process; it is difficult to plan precisely either a project’s timing or final outcomes. For those unused to this type of work, this process can seem quite messy — an unexpected contrast to a field that, from the outside, seems to epitomize the rule of reason and the preeminence of data.

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How bad data fed the Ebola epidemic — Rachel Glennerster, Herbert M’cleod and Tavneet Suri

MIT Sloan Associate Prof. Tavneet Suri

MIT Sloan Associate Prof. Tavneet Suri

From The New York Times

The West African Ebola outbreak first hit Sierra Leone in May 2014, followed by an explosion of cases in the capital Freetown in the autumn. The epidemic now counts more than 10,500 cases across Sierra Leone, with signs that the spread is slowing.

The early days of the crisis were characterized by a sense of immense fear, anxiety and alarm, regionally and globally. In Sierra Leone, a three-day, countrywide, military-led lockdown in September fed the fear in West Africa and beyond. Many flights originating in unaffected African countries were restricted. African students were prevented from attending some American schools, and there were countless reports of discrimination against Africans across the globe. Pictures of health workers in full protective suits became a ubiquitous symbol of the panic.

Misleading reports, speculation and poor projections from international agencies, government ministries and the media about the Ebola outbreak exacerbated the problem. The fear that was spread by the dramatic reports that accentuated the negative, undermined confidence, made it harder to encourage people to seek care, and misdirected attention away from Sierra Leone’s urban areas, where data suggest the economic effects of Ebola have been concentrated.

Valid, credible and timely data is essential during a global crisis. Without reliable data, efforts to assist affected people and to rebuild damaged communities can be misdirected and inefficient.

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New MIT Sloan Management Review study: An advanced analytics culture outweighs all other factors — David Kiron

The Need for Culture

The Need for Culture

What distinguishes the winners from the losers among companies converting data and analytics into a positive force in their strategies and operations? And what practices are keeping the winners ahead?

The Analytics Mandate, a new research report from MIT Sloan Management Review and SAS Institute, takes several steps toward answering these questions.

Our most significant finding? Our study shows that an advanced analytics culture outweighs other analytics-related factors -including data management technologies and skills-among companies that strongly agree they are gaining a competitive advantage from analytics. Essentially, a strong analytics culture is the lynchpin in moving from competitive parity to competitive advantage.

The need for change within a corporation’s culture, and the best way to achieve it, are both nicely illustrated in a case study included in our report.  WellPoint, the largest for-profit managed care organization within the Blue Cross Blue Shield umbrella, knew that sharing insurance data with physicians would provide doctors with a 360-degree medical view of every patient. This in turn, would better enable them to spot patients more likely to go to the emergency room or be readmitted to a hospital, contributing to expenses that drive up the high cost of health care delivery.

Within WellPoint, creating the data reports for physicians initially became a classic showdown between IT and interests from the business side.

The initial reports, prepared by the IT team, were late and lacked fundamental functionality.  For instance, different units within the company reported an emergency room visit in different ways.  The IT team’s explanation: no one told them the definitions had to be the same. This much was true — the business side didn’t think it needed to specify that emergency room visits be consistent across reports. They had assumed this was a given.

The high-profile project was subsequently placed in Red status. At this point, senior management got involved. Problems were brought to executives who, in turn, ensured resources were allocated. Outside consultants and experts were hired. More resources were diverted to the project.

Finally, after many challenging discussions, IT and the business side began working together using an iterative development approach called “Agile”, which focuses on “user stories.”. This meant understanding the perspective of the end user—the provider—and the context in which he or she would be using the data, as opposed to just developing according to a static set of  requirements.

Early reaction to the data system from doctors has been highly positive.  Over time, WellPoint believes that the proactive, coordinated-care model made possible when providers have actionable insights at their fingertips can cut health care costs by as much as 20%. That could work out to billions of dollars, given that WellPoint reimbursed more than $99 billion in health benefits for commercial and individual members in 2013.

In short, to create strategic benefits with analytics WellPoint had to change its organizational behavior. Without an effective collaboration between the business side and IT, the program would have remained in jeopardy. Without leadership’s involvement, the program would have remained in jeopardy. Preparing data for a strategic role often means changing business conduct and that, more often than not, requires a top down process to create the necessary alignment of incentives and goals.

To read the full report, please visit “The Analytics Mandate.”

David Kiron is Executive Editor, Big Ideas initiatives, for MIT Sloan Management Review.