The battle over the driving experience is heating up and will be won in software – Lou Shipley

MIT Sloan Lecturer Lou Shipley

MIT Sloan Lecturer Lou Shipley

From TechCrunch 

Sirius XM’s recent all-stock $3.5-billion purchase of the music-streaming service Pandora  raised a lot of eyebrows. A big question was why Sirius paid so much. Is Pandora’s music library and customer base really worth that amount? The answer is that this was a strategic move by Sirius in a battle that is far bigger than radio. The real battle, which will become much more visible in the coming years, is over the driving experience.

People spend a lot of time commuting in their cars. That time is fixed and won’t likely change. However, what is changing is the way we drive. We’re already seeing many new cars with driver-assist features, and automakers (and tech companies) are working hard to bring fully autonomous cars to the market as quickly as possible. New cars today already contain an average of 100 million lines of code that can be updated to increase driver-assist options, and some automakers like Tesla already offer an “autonomous” mode on highways.

According to the Brookings Institute, one-quarter of all cars will be autonomous by 2040, and IHS predicts all cars will be autonomous after 2050. Those are conservative estimates, as we are likely to see major changes in the next 10 years.

These changes will impact the driving experience. As cars become more autonomous, we can do more than simply listen to music or podcasts. We may be able to watch videos, surf the web and more. The value of car real estate is already valuable, but it’s going to skyrocket as we change the way people consume media while driving.

The Pandora acquisition was a strategic move by Sirius to gain the necessary assets so that it won’t fall behind in this space — and to get into the fast-growing music-streaming business, where users consume music at home, work and play. While Pandora’s music library is arguably second-tier, it’s also good enough that it can provide pretty much every artist most people want. This is often how high-priced mergers happen — one party is concerned about falling behind and pays a premium to purchase the other company’s assets. It’s also a bet by Sirius about the driving experience of the future.

Read the full post at TechCrunch.

Lou Shipley is a Lecturer at the MIT Sloan School of Management. 

What the NBA gets wrong about lottery pick protections – Paul Michelman and Ben Shields

Paul Michelman, Editor-in-Chief, MIT Sloan Management Review

Ben Shields, Senior Lecturer, MIT Sloan School of Management

Excerpt from MIT Sloan Management Review

In this episode, we take a closer look at the value of pick protections in the NBA draft — and how your favorite NBA just might be doing it all wrong. The NBA draft is all about value: Just a couple of selections higher or lower could be the difference between a franchise-altering superstar or another half-dozen seasons selecting in the lottery. But when it comes time to move these assets around, value sometimes gets thrown out of the window, and teams make deals involving pick protections they later regret. To help us understand why — and to chart a better strategy for pick protections — we speak with Ben Foster who presented his and Michael Binns’s research on valuing protections of NBA draft picks at the 2019 MIT Sloan Sports Analytics Conference.

Listen to the full podcast here.

Ben Shields is a Senior Lecturer in Managerial Communication at the MIT Sloan School of Management.

Paul Michelman is the editor-in-chief of MIT Sloan Management Review.

 

Join the #DesignedforDigital Twitter Chat on October 8 to learn how to propel your business into the digital world

Many established companies have deployed such digital technologies as the cloud, mobile apps, the internet of things, and artificial intelligence. But few established companies are designed for digital. MIT Sloan Experts will join the authors of Designed for Digital: How to Architect Your Business for Sustained Success for a Twitter Chat on October 8 at 1 p.m. ET. During the chat, authors Jeanne RossCynthia Beath and Martin Mocker will discuss how organizations can retool their practices for digital success. 

Designed for Digital argues that business strategies must be fluid to adapt to the rapid pace of change in technology capabilities and customer desires. Business design has become a critical management responsibility. Effective business design enables a company to quickly pivot in response to new competitive threats and opportunities. Most leaders today, however, rely on organizational structure to implement strategy, unaware that structure inhibits, rather than enables, agility. In companies that are designed for digital, people, processes, data, and technology are synchronized to identify and deliver innovative customer solutions—and redefine strategy. Digital design, not strategy, is what separates winners from losers in the digital economy.

Designed for Digital offers practical advice on digital transformation, with examples that include Amazon, BNY Mellon, DBS Bank, LEGO, Royal Philips, Schneider Electric, USAA, and many other global organizations. Drawing on five years of research and in-depth case studies, the book is an essential guide for companies that want to disrupt rather than be disrupted in the new digital landscape.

Join us for the #DesignedforDigital Twitter chat on October 8 at 1 p.m. ET. to learn how companies can prepare for the digital economy. Make sure to use the hashtag #DesignedforDigital and follow @mitsloanexperts, @jrossCISR, @CynthiaBeath and @martinmocker to learn all about designing for digital. 

It pays to have a digitally savvy board – Peter Weill, Thomas Apel, Stephanie L. Woerner, Jennifer S. Banner

Peter Weill, Senior Research Scientist and Chair of the Center for Information Systems Research, MIT Sloan School of Management

Stephanie L. Woerner, Research Scientist at the MIT Sloan Center for Information Systems Research, MIT Sloan School of Management

From MIT Sloan Management Review

Boards of directors have many issues competing for their attention, but being digitally conversant in an era of digital transformation is quickly rising to the top of the list. Nearly all companies are looking for ways that technology can be used to improve their business models, customer experience, operational efficiency, and more — and boards must help them move forward at a sufficient pace, advocating for change by supporting and sometimes nudging their CEOs. Those that do are likely to see better financial results than those that don’t.

That’s what we discovered when we did a machine learning analysis of the digital know-how of all the boards of U.S.-listed businesses. (See “About the Research.”) Our research shows that companies whose boards of directors have digital savvy outperform companies whose boards lack it. We define digital savvy as an understanding, developed through experience and education, of the impact that emerging technologies will have on businesses’ success over the next decade. We measured it by analyzing data from surveys, interviews, company communications, and the bios of 40,000 directors, extracting key words that signal exposure to digital ways of thinking and working.

Our discoveries are striking: We found that among companies with over $1 billion in revenues, 24% had digitally savvy boards, and those businesses significantly outperformed others on key metrics — such as revenue growth, return on assets, and market cap growth.

Doing business in the digital era entails risks ranging from cybersecurity breaches and privacy issues to business model disruptions and missed competitive opportunities. When a board lacks digital savvy, it can’t get a handle on important elements of strategy and oversight and thus can’t play its critical role of helping guide the company to a successful future. But companies can fix that by understanding what characteristics to look for in existing and new board members, managing board agendas differently, and cultivating new learning opportunities.

Read the full post at MIT Sloan Management Review.

Peter Weill is a Senior Research Scientist and Chair of the Center for Information Systems Research (CISR) at the MIT Sloan School of Management.

Stephanie L. Woerner is a Research Scientist at the MIT Sloan Center for Information Systems Research.

Thomas Apel is chairman of the board at Stewart Information Services Corp.

Jennifer S. Banner is CEO at Schaad Cos. and lead director of BB&T Corp.

Improving strategic execution with machine learning – Michael Schrage, David Kiron

MIT Sloan Management Review Executive Editor David Kiron

David Kiron, Executive Editor, MIT Sloan Management Review

Michael Schrage, Research Fellow, MIT Center for Digital Business

From MIT Sloan Management Review

Machine learning (ML) is changing how leaders use metrics to drive business performance, customer experience, and growth. A small but growing group of companies is investing in ML to augment strategic decision-making with key performance indicators (KPIs). Our research,1 based on a global survey and more than a dozen interviews with executives and academics, suggests that ML is literally, and figuratively, redefining how businesses create and measure value.

KPIs traditionally have had a retrospective, reporting bias, but by surfacing hidden variables that anticipate “key performance,” machine learning is making KPIs more predictive and prescriptive. With more forward-looking KPIs, progressive leaders can treat strategic measures as high-octane data fuel for training machine-learning algorithms to optimize business processes. Our survey and interviews suggest that this flip ― transforming KPIs from analytic outputs to data inputs ― is at an early, albeit promising, stage.

Those companies that are already taking action on machine learning ― investing in ML and actively using it to engage customers ― differ radically from companies that are not yet investing in ML. They are far more likely to:

  • Develop a single, integrated view of their target customer.
  • Have the ability to drill down to see underlying KPI data.
  • Check their KPI reports frequently.

These differences all depend on treating data as a valuable corporate asset. We see a strong correlation between companies that embrace ML and data-driven decision-making.

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