Data & Analytics

Collaborate Smarter, Not Harder

Feeling pressure to become more agile and “networked,” organizations tend to overwhelm employees with collaboration demands, putting a drag on performance and engagement. But through analytics, they can scale collaboration more effectively, improve collaborative design and execution, drive planned and emergent innovations through networks, streamline work by diagnosing and reducing collaborative overload, and engage talent by identifying social capital enablers.

Three People-Centered Design Principles for Deep Learning

As organizations begin adopting deep learning, leadership must ensure that artificial neural networks are accurate and precise to avoid negative impacts on business decisions that hurt customers, products, and services. A designed-centered approach helps address both these short-term concerns as well as the long-term concerns that machines might displace humans when it comes to business decision-making.

You Can’t Afford to Please Everyone

While giving customers what they want — and as rapidly as possible — may be a worthy goal for service organizations, Amy R. Ward at the University of Chicago’s Booth School of Business notes that businesses can’t always afford to do this. Her research uses probability to understand how best to align resources with customer demand and improve operational efficiency on a day-to-day basis.

Self-Driving Companies Are Coming

  • Read Time: 9 min 

Automation can go far beyond cars. Self-driving company capabilities are closer than many leaders realize. And just as automobile manufacturers are rethinking the meaning of driving within the context of self-driving technology, business leaders are being forced to rethink an equivalent question: What does it mean to manage an enterprise once some of the work can be done autonomously?

How Algorithms Can Diversify the Startup Pool

Biases related to gender and other demographic factors creep into decisions about which projects to fund with venture capital. Data-driven approaches can help tease out those biases and limit their impact. Algorithmic methods identify potential instances of discrimination and increase transparency, making it easier to find and fix problems. Aversion to algorithms can be tempered by letting decision makers retain some subjective control over the data-driven process.

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Information Overload?

Counterpoints takes on two pressing questions in the sports analytics field: the issue of information overload and whether there is such a thing as too much data, and a very different — but related — issue: Biometrics. We’ll go to the mat over whether professional athletes will be willing to share their personal biometric data in real time.

The Regulation of AI — Should Organizations Be Worried?

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  • Read Time: 6 min 

As companies pour resources into designing the next generation of tools and products powered by AI, many are failing to simultaneously examine the question of who is ethically and legally responsible for the societal backlash if these systems go awry. Over 80% of Americans now believe that robots and/or AI should be carefully managed. Because there are no clear-cut answers or solutions, the talk of regulations — and, more lightly, standards — is getting louder.

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Why Sports Is a Great Proving Ground for Management Ideas

  • Read Time: 6 min 

Because of its sharp focus on measurable outcomes, the study of sports analytics brings many of the most critical issues in management into high relief. Through the lens of sports, there is a great deal to learn about leadership, performance management, decision-making, innovation, and, most of all, managing with data. MIT SMR’s sports analytics podcast, Counterpoints, is a great entry point to the playing field of data-driven management practice.

Measuring Culture in Leading Companies

To survive and thrive in today’s market, a healthy corporate culture is more important than ever. The MIT SMR/Glassdoor Culture 500 uses machine learning and human expertise to analyze culture using a data set of 1.2 million employee reviews on Glassdoor. This interactive tool offers previously untapped insights about the organizational culture of over 500 of the world’s leading companies and provides leaders with new tools for benchmarking culture in their own organizations.

What Does an AI Ethicist Do?

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  • Read Time: 7 min 

Microsoft has been active in advocating for an ethical perspective on artificial intelligence, and in 2018 it appointed its first general manager for AI policy and ethics. Tim O’Brien, who had been with the company for 15 years, says his activities as “AI ethics advocate” include extending the community of people who are focused on the ethics topic, meeting with Microsoft customers, and leading a research effort to develop a global perspective on tech ethics.

Predicting the College Football Playoff

Which teams make it to the college football playoffs isn’t as random as it sometimes seems, says University of Wisconsin-Madison professor Laura Albert. In this week’s Counterpoints podcast, we look at how Albert uses analytics to predict the brackets and how the football playoff selections compare to that other big college tournament, March Madness.

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AI Can Help Us Live More Deliberately

AI spares us from many mundane, time-consuming, nerve-wracking annoyances. The problem is, such annoyances play a key adaptive function by helping us learn to adjust our conduct in relation to the world around us. But AI designers can tackle that problem through system enhancements. By incorporating cognitive speed bumps, they can prompt users to engage in reflective thought rather than “outsourcing” cognitive, emotional, and ethical labor to software.

Using AI to Enhance Business Operations

Companies can turn AI hype into operational hay by developing their capacity for enterprise cognitive computing. This capacity entails five capabilities: data science competence, business domain proficiency, enterprise architecture expertise, an operational IT backbone, and digital inquisitiveness. The capabilities shape and are shaped by four practices: identifying use cases, managing application learning, cocreating applications, and thinking “cognitive.”

Why Personalization Matters for Consumer Privacy

Many different factors determine how consumers balance data privacy against a desire for personalized products and services — age, geography, and education among them. Companies can help their customers feel more comfortable with the data collection needed for personalized service by understanding customer values and maintaining transparency and good communication when it comes to data collection.

Sponsor's Content | How IT Taps Big Data to Optimize Digital Operations and Drive Business Advantage

  • MIT SMR Connections | Custom Research Report

New research by MIT SMR Connections and NETSCOUT shows that most IT leaders are well advanced along the analytics maturity curve when it comes to tapping data to manage and improve IT infrastructure. The practitioners deriving the most value from data are most likely to have the broadest view of where analytics can be implemented across both IT and business operations — and they are also most likely to view attention to data quality as the most important priority for action.

Customer Centricity in the Digital Age

  • Read Time: 4 min 

As AI moves from the hype stage to implementation within organizations, retailers and marketers have new competitive opportunities with customer centricity. AI enables companies to apply data about their customers’ wants, needs, and preferences to customize their offerings, create personalized shopping experiences, and make the purchase process simpler and more convenient.

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