Analytics & Organizational Culture

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Lessons from Becoming a Data-Driven Organization

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.


Predicting a Future Where the Future Is Routinely Predicted

The ability of artificial intelligence to sift through mountains of data and identify patterns — and problems — in real time is its key value for business. Using AI to predict failures and take action to prevent them will become commonplace in the very near future. But it can also offer insights into human behavior to help managers improve customer service and employee relations.


Achieving Meritocracy in the Workplace

Rewarding employees based on merit can be more difficult than it first appears. Even efforts to reduce bias can backfire; disparities in raises and bonuses by gender, racial, and other characteristics persist in today’s organizations not only despite management’s attempts to reduce them but also because of such efforts. The author describes how a simple analytics-based approach can address these concerns and produce a truly meritocratic workplace.



Building a Better Car Company With Analytics

Using data and analytics to understand the complexities of modern business has become not only common, but essential. Gahl Berkooz joined Ford Motor Co. in 2004, eventually becoming head of data and governance and a member of the company’s global data insights and analytics skill team. Berkooz became acutely aware of how important analytics is to the company’s ability to thrive in the global marketplace. “What it boils down to,” he told MIT SMR’s Michael Fitzgerald, “is that we know how to make decisions. It’s about finding the opportunities to bring data and analytics to make better decisions.”


Where Digitization Is Failing to Deliver

It has become a truism that the pace of work is faster than ever, as digital technologies speed up communication and operational processes in a story of unending progress. But increased speed has not translated into increased rates of productivity growth. Since 2004, growth rates have slowed not just in the US but across the world. Chad Syverson, J. Baum Harris Professor of Economics at the University of Chicago’s Booth School of Business, explains what the implications are, and why the benefits of new technologies are not straightforward.


Free Webinar: Foundations of Analytics Strategy

The 2016 MIT SMR/SAS Data and Analytics report, “Beyond the Hype: The Hard Work Behind Analytics Success,” finds that competitive advantage from analytics is declining — but that organizations achieving the greatest benefits have figured out how to ensure that the right data is being captured. In this webinar, the authors of the report explain how companies are making this transition and which are seeing the most success.


Just How Smart Are Smart Machines?

Managers don’t expect to see machines displacing knowledge workers anytime soon. Instead, they expect computing technology to augment rather than replace the work of humans. But in the face of a sprawling and fast-evolving set of opportunities, what forms should that augmentation take? Davenport and Kirby, authors of “Only Humans Need Apply: Winners and Losers in the Age of Smart Machines,” examine what cognitive technologies managers should be monitoring closely and what they should be applying now.


Beyond the Hype: The Hard Work Behind Analytics Success

The 2016 Data & Analytics Report by MIT Sloan Management Review and SAS finds that analytics is now a mainstream idea, but not a mainstream practice. Few companies have a strategic plan for analytics or are executing a strategy for what they hope to achieve with analytics. Organizations achieving the greatest benefits from analytics ensure the right data is being captured, and blend information and experience in making decisions.



Cognitive Technologies: The Next Step Up for Data and Analytics

This free on-demand webinar offers context for understanding cognitive technology offerings. It focuses on what technology capabilities will be available — and what tasks will still require human input. Topics include artificial intelligence, automation, and business rules for making cognitive technology functional. Presenters Thomas H. Davenport and Julia Kirby are co-authors of the forthcoming book Only Humans Need Apply: Winners and Losers in the Age of Smart Machines.


What the Internet of Things Could Mean to Consumers

From wearables to hotel desks that remind us to move around, connected objects are becoming a bigger part of consumers’ lives. For instance, famed design firm IDEO is using people-centered design to envision the Internet of Consumer Things, including helping to create a headband that lets people measure their brain activity and track their mental focus. In the hospitality industry, smart locks, smart lights, and even desks that suggest changing your posture could all become routine.


Pushing the Boundaries of Predictive Analytics and the IoT

From sensing issues with turbine engines to identifying non-standard washing machine loads, predictive analytics are a given in the Internet of Things (IoT). But what will happen to predictive analytics once everything is connected? This list of five links points to predictions and calculations that some people in the field are making.


Hype vs. Reality: A Reality Check on the Internet of Things

  • Blog
  • Read Time: 2 min 

The Internet of Things has plenty of hype — it’s going to be big, really big — but also plenty of detractors. The naysayers breathily predict everything from the surveillance state to a wrecked economy to people enslaved by machines. Here are nine bits of information to consider, from the way the Internet of Things will create (yet another) battle for control of the Internet, to the fact that the security of the Internet of Things is under fire before it even exists.


Sharing Supply Chain Data in the Digital Era

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.


Image courtesy of Flickr user enshahdi

How to Build (and Keep) a World-Class Data Science Team

To manage a first-rate data science or quant group, leaders need to build an engaging environment, get the team the resources it needs and balance being involved while also staying out of the way. In banking, for example, division managers generally don’t review loan applications. But in analytics, the most successful leaders engage regularly in hands-on research and continue to publish regularly even as they move up the executive ladder. By staying active in line research, analytics managers are able to hone their abilities to judge how difficult projects are and how long they will take.


Using Big Data for Better Health Outcomes

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.


The New Data Republic: Not Quite a Democracy

There are clear signs that the movement to democratize data is making real progress. Barriers such as infrastructure, culture, tools, and governance that once kept data access limited are quickly eroding. But access to data isn’t enough: Data democratization also requires knowing how to work with data and understand data analysis tools and techniques. Without these capabilities, the data democracy is only an illusion — and most people are still unable to participate fully.


At This Education Nonprofit, A Is for Analytics

In an interview with MIT Sloan Management Review, Christopher House CEO Lori Baas and director of quality assurance Traci Stanley explain how they’re using data throughout their educational organization to track student outcomes and look for improvements. “We now can show, based on the assessments, not only how our kids are improving in their cognitive development, or social-emotional development, but also how we compare to similar organizations,” says Bass.


When Health Care Gets a Healthy Dose of Data

American health care is undergoing a data-driven transformation — and Intermountain Healthcare is leading the way. This MIT Sloan Management Review case study examines the data and analytics culture at Intermountain, a Utah-based company that runs 22 hospitals and 185 clinics. Data-driven decision making has improved patient outcomes in Intermountain’s cardiovascular medicine, endocrinology, surgery, obstetrics and care processes — while saving millions of dollars in procurement and in its supply chain. The case study includes video clips of interviews and a downloadable PDF version.

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