Data & Analytics

Why Analytics Don’t Always Pay Off in the Playoffs

Every team has a data-based strategy for regular season play — but the playoffs are a completely different story. In this episode of Counterpoints, we talk with Mike Trudell, a reporter who covers the Los Angeles Lakers for ESPN and other sports news outlets, about the mysterious intangibles that come to the foreground during the playoffs.

The Best of This Week

This week’s must-reads for managing in a digital age: just because companies recognize re-skilling and upskilling is critical doesn’t mean they’re doing it right. Also, how to choose charts everyone understands, where blockchain is headed in the enterprise, and words of wisdom for managing your career in an age of uncertainty.

Choose Charts Everyone Understands

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Complex charts are good for aggregating data and then digging into it, especially if users can click on sections to find additional material or generate custom data sets. But interactive data visualizations aren’t always necessary — and sometimes, they’re just too complicated. While complex charts are good for exploring data, a classic bar chart, line chart, or pie chart is often best for communicating information.

Front Office, Disrupted

The explosion of streaming media offers fans unlimited access to sports and entertainment. So how can teams entice their audience to the events happening here and now? Sports Innovation cofounder and CEO Angela Ruggiero says success starts with understanding just how fans’ behavior has changed with the advent of digital technology — meaning, executives of sports companies and media outlets alike must be willing to completely rethink how they approach their marketing.

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An Executive Guide to the Fall 2019 Issue

This guide to the Fall 2019 issue of MIT Sloan Management Review summarize the issue’s key articles. The articles discuss finding better ways to collaborate; how to give customers what they’re looking for; the organized ecosystem of Dark Web cybercrime; and how algorithms can reduce bias.

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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

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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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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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