Learning.africa

Developing tech-savvy leaders

by Sharon Shinn
Executives need “digital mindsets” that enable them to understand how to use the data and technologies that are driving today’s businesses.
  • To succeed in the digital age, executives should have about 30 percent fluency in key technologies and technical skills.
  • Leaders need to create cultures that embrace constant change and encourage all employees to develop digital mindsets.
  • Business schools must determine how much to focus on teaching technical skills and how much to focus on helping leaders use their skills to see, think, and manage differently.
As the world’s businesses become increasingly digital, business schools need to prepare students to manage data-driven organisations. But how much knowledge do today’s executives need about current technology? And how much can they rely on the data they acquire?

These are some of the questions Paul Leonardi and Tsedal Neeley explore in their new book, The Digital Mindset. Leonardi is the Duca Family Professor of Technology Management at the University of California Santa Barbara, and Neeley is the Naylor Fitzhugh Professor of Business Administration at Harvard Business School in Boston. In their book, they provide a framework that outlines the skills leaders must develop to manage “collaboration, computation, and change.”

AACSB Insights recently interviewed Leonardi to learn what a digital mindset is, how much managers need to understand about technology, and what role business schools play in preparing tomorrow’s tech-savvy leaders.

First, what is a digital mindset?


A digital mindset is a set of attitudes and behaviours that enable people and organisations to see new possibilities and chart a path for the future.

When we talk about the digital transformation of companies, most leaders think that either they or their employees need to learn how to code in Python, do multinomial regression, or command a robot. What we have found is that success in the digital age requires some new skills, sure, but more importantly, it requires new ways of thinking and acting. Acquiring skills gives you the vocabulary, knowledge, and intuition to see the bigger picture-to ask the important questions. Developing a new mindset means that you change your behaviour because you see the world in a new way.

In a typical organisation, who needs a digital mindset?

Everyone-from the CEO on down. People in the most senior ranks can’t build strategy for digital change if they don’t understand the possibilities and limitations that digital tools bring to the company. And people all the way down the hierarchy will be using those digital technologies. If they don’t understand how to use digital tools and the data they produce to improve work processes, interactions with customers, and so on, digital transformation will never occur.

What are some of the biggest types of technology people will need to understand?

The list is too long and the technologies are changing so rapidly that it would be difficult to provide a full account, but among them are algorithms, AI, robotic teammates, internal social media, blockchain, and the cloud.

To give just one example, we’re seeing a rise in robotic teammates, who might be bots, virtual assistants, or mechanical robots. To work effectively with a robotic teammate, you have to know when you can trust its recommendations and actions and when you can’t - just like you have to do with human teammates. But learning if you can trust a robot is very different than learning if you can trust a human.

You’ve said that top executives probably don’t need to know coding or become experts in technology, but they need to understand enough to meet the “30 percent rule.” Can you explain what that is?

We have found that you can be a competent citizen in the digital world if you have only about 30 percent fluency in certain areas of computation, collaboration, and change. I like the analogy of learning a foreign language. Research shows that you’ll need about 12,000 words to have mastery of a language and to communicate at a fluent level. But if you just want to be competent enough to work with people who speak a different language, you only need about 4,000 words - about 30 percent of the total.

Many companies today are collecting reams of data, but they don’t always know how to use that information effectively. How can managers take advantage of all the information they’ve acquired?

One of the consistent themes we’ve seen is that, because there is so much data available, leaders fall into the trap of thinking that all their answers lie somewhere in a database waiting to be uncovered. But what people forget is that companies don’t collect data about everything. The data they do collect come to be valued, but the data they don’t collect are often undervalued and often go unrecognised as data.

Take, for example, the National Basketball Association [NBA]. There is hardly an organisation on the planet today that collects more data about its employees. Just about every inch of a basketball court is covered in sensors that track who makes what shots from where, how many assists or rebounds a player makes, and even what foot a shooter leads with. Those sensors - and the data they produce - are connected to things we think we value, like shots attempted versus shots made.

But most NBA teams don’t collect data on whether a player makes it difficult for an opponent to make a rebound. That means that teams just don't have much data with which to evaluate, laud, and reward players who do that great - often hidden-defensive work. Consequently, those players don’t get the big contracts that go to the great shooter whose every action is dripping in data footprints.

You often hear the adage “measure what matters.” The reality is, what we measure comes to matter. What we don’t measure ends up not mattering, even though it might be really important.

People tend to think of data as neutral, but experts have warned that implicit bias can present skewed results. For instance, if most programmers are white men, they might not even be aware of writing code that discriminates against women or people of colour. What do managers need to understand about the limitations of data? How can they avoid the pitfalls?


Data aren’t collected, they are produced. All data are social constructs, they are not natural or neutral. This is important because we’re in a world where decisions are based increasingly on data.

Think about the ads you get served when you’re searching on Google. Fine-grained machine learning algorithms are constantly turning data into predictions about what you’re likely to buy at any particular moment. In order to understand whether a prediction is going to be useful, you need some rudimentary statistical knowledge. You have to be able to interpret statistics and ask the right questions.

It’s not as simple as saying that data or the algorithms that make sense of data are biased. That may be true in many situations. But it’s also true that there is bias in what data we choose to produce, as my NBA example suggests. There’s also bias in how we interpret the predictions derived from those data. If you know how your data are collected, categorised, and interpreted, you can begin to avoid the pitfalls of making biased decisions.

You note that digital transformation is always occurring, which means companies and employees need to be constantly learning. But most people resist change. How can managers create a company culture that embraces continuous digital transformation?

Most people think that change is episodic - that we go through long periods of stasis punctuated by short episodes of change. But in the digital world, we don’t have periods of stasis anymore. We’re always transitioning from one set of practices or business models to the next, in large part because of all the new data that are being collected, produced, and analysed through digital technologies.

Because we’re in this constant process of transitioning, it’s extremely important for companies to develop an ethos of experimentation and get feedback on what’s working and what’s not. Organisational leaders must help employees develop the skills they need to embrace change and create a culture that’s receptive to it. People should not be looking forward to a time when things calm down - because they won’t.

There are three really important things leaders can do to create the right cultures for digital change. First, they need to make sure employees understand how and why to use new digital technologies. Second, they need to bring skeptics along and get them comfortable with the idea that succeeding in the digital economy requires employees at all levels to have digital mindsets. Third, they need to provide opportunities for continuous learning-for employees to update their skills and be comfortable applying them in the changing context of work.

Where can people go to learn the digital skills they need?

Universities around the world offer a lot of great short educational programmes on topics like AI, machine learning, data science, and remote work. We’ve also found that many companies have had great success building their own in-house training programmes to help employees build digital skills. In our book, we extract some best practices from companies like Spotify, Atos, Philips, and Capital One who have shown how to build these programmes effectively.

Many business school administrators worry that executives will turn to alternate providers to acquire these skills. How can business schools make sure they’re competitive in this area? What kinds of degree programmes or certificate programmes should they be offering?

Business schools have to make some hard choices. Do they get into the business of teaching basic skills in computer programming and data analytics, or do they outsource that skill-based learning to other parts of the university or even to private programmes?

My view is that the job of the business school is to teach students how to put those skills to work to solve organisational problems and to build new business capabilities. Perhaps one approach is to make the acquisition of these skills prerequisites for admission or matriculation, just like we do with certain econ and stats skills.

What technical topics should MBA students have some familiarity with by the time they graduate?

They need to develop approaches to collaboration, computation, and change that build from knowledge about digitalisation. That includes understanding about remote work, working with machines, data curation and analytics, cybersecurity, experimentation, and change management. Those are the basic ingredients of a digital mindset.

If business schools don’t have faculty who have enough digital knowledge to teach the relevant courses, how can they fill that gap?

Just like companies, business schools need to continuously train their employees. Business school deans also can consider hiring graduates from technology management programmes, like the one I teach at UC Santa Barbara. Our students are trained in management and strategy in the context of an engineering school, so they comprehend the technologies at the heart of business transformation, and they understand the business problems and opportunities associated with those technologies. This background puts them in an ideal position to add digital knowledge to business school faculty.

If you could give one piece of advice to deans who want to make sure they’re prepared for digital transformation - both in the classes they offer and their own operations - what would it be?

Don’t delay getting to 30 percent fluency so you can develop your digital mindset. That will help you see how you need to revise your course offerings and co-curricular experiences. Once you’re there, create a culture in which you, your faculty, and your students feel compelled and empowered to continue learning as digital technologies continue to change.

Useful resources:
AACSB Insights
AACSB Insights publishes perspectives from leading voices in global business education, the latest business school data and insights, and views on the current and future state of business.
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