Leaders who lack digital literacy will be left behind
The exponential growth of the digital economy means that leaders who do not develop a digital mindset will soon no longer be able to lead their organizations effectively. Leaders and those who want to thrive in organizations need to improve their skills and gain digital literacy or they will be left behind.
That’s the view of Tsedal Neeley, professor of business administration at Harvard Business School, virtual work expert and co-author of The Digital Mindset, what it really takes to thrive in the age of data. , algorithms and AI.
Neeley says the concern about machines replacing humans misses the point. “Humans with digital literacy will replace humans without digital literacy,” she told The Irish Times.
“You can no longer survive with low levels of digital technology literacy. You need to understand the language of digital in terms of its impact on strategy, operating models, employees, retention and recruitment, stakeholders, products and services – and if you don’t understand it, you will not be able to participate in the digital transformation that is taking place.
The days of C-suite executives recruiting digital experts or recruiting high-level CIOs to their boards to compensate for their own lack of knowledge appear to be over in this analysis. The good news, however, according to Neeley, is that acquiring digital literacy isn’t as difficult as many imagine.
Drawing an analogy to learning a foreign language, she says you don’t have to be a master or an expert, but being literate enough to be a meaningful participant in conversations is essential.
A non-native English speaker, she notes, must acquire a vocabulary of around 12,000 words to be considered a master of the language. However, with as little as 3,500 words, people can usually understand and communicate effectively in a work environment.
Generally speaking, people should follow a “30% rule,” which is the minimum threshold you need to understand and take advantage of the digital threads woven into the fabric of our lives now.
Digital literacy doesn’t mean you have to master coding or become a data scientist, but you do need to understand what computer programmers and data scientists do, how to use A/B testing, how to interpret statistical models, and how get an AI-powered Chatbot to do what you need.
Neeley says this level of knowledge can be acquired without undertaking a conventional university-level program. The important point is to get a baseline on your existing knowledge and find short programs or modules to fill in the knowledge gaps.
Harvard Business School, for example, runs a nine-month course called Harvard Business Analytics. During this time, managers learn to code and gain basic knowledge of statistics and what AI and machine learning can do. This is just one of the ways managers can catch up quickly.
Learning to code isn’t essential, Neeley points out, but it’s not bad either. She cites the example of the Japanese company Rakuten, one of the world’s leading 5G operators. In 2019, he asked his entire workforce to learn to code and gave them six months to do so. Everyone in the organization should now understand the idea of data and the importance of algorithms and statistics, she observes.
This is not a one-time process, however, but one that involves a change of mindset.
“It’s about things like understanding how to do collaboration differently in a digital world, how to think about data and security and how to make decisions around data, and finally how to think about change in a time of rapid transformation that requires a continuous learning loop in order to keep innovating and making the right decisions.
One of the ways that a better understanding of digital informs better decision-making is that leaders gain a better appreciation of not only the many benefits that digital offers, but also its limitations and shortcomings and how best to which humans should interact with it. .
Artificial intelligence trumps humans in many areas and part of developing a digital mindset is recognizing that machines are better than humans at making certain predictions and performing specific tasks. Health care provides excellent examples.
Researchers at Seoul National University Hospital and the Medical School have developed an AI algorithm called deep learning-based automatic detection (DLAD) to analyze chest X-rays and detect abnormal cell growth such as cancers potentials. In a four-year study, the hospital found that AI was able to significantly reduce the number of overlooked lung cancers on chest X-rays, without a commensurate increase in the number of follow-up chest CT exams.
In another case, Google Health is creating a machine learning algorithm to identify metastatic breast cancer tumors from lymph node biopsies. Its unique advantage was its ability to identify suspicious regions indistinguishable to the human eye. LYRA was tested on two datasets and correctly distinguished between cancerous and non-cancer findings in 99% of cases.
The success of AI creates problems for doctors. As the authors observe in the book: “It can seem threatening that a machine contradicts your diagnoses. This is where the idea that machines are not humans is important. It looks threatening, but the code doesn’t make threats. It’s just a tool that we have to use.
Reinforce existing prejudices
The possibility of bias is another issue the authors caution against. A well-meaning model builder can reinforce existing biases if they don’t consider the bigger picture.
Take the example of the city of Boston, which used what appeared to be a well-designed app called StreetBump to solve the city’s persistent pothole problem. The app records accelerometer data from smartphones while a resident is driving, producing data indicating that a car has just hit a pothole. However, since residents with smartphones tended to have higher incomes, he generally identified potholes in wealthier areas.
When Boston’s Office of New Urban Mechanics discovered the data problem, the model was quickly adapted to represent the entire city, and the results changed significantly.
Several authoritative studies have also revealed inherent gender and racial biases in facial recognition software or exaggerated the level of crime in less affluent areas, based on human intervention on captured data.
“Police presence in low-income neighborhoods generates more data points, which then brings them into contact with more low-income people, which creates more police records, which then have a higher probability to accumulate in the score of chronic offenders.”
Having a digital mindset also recognizes that it’s vital to rework old technology investments, a process described as “investing in technical debt.”
“It’s like home renovations. We would much rather install new counters and appliances than spend money updating plumbing or electrical. But if we keep spending our money on fun things and don’t invest in maintenance, eventually the pipes will burst and the circuits will be shorted and we will have to go into debt to solve the infrastructure emergency.
Another very tangible benefit of acquiring digital literacy is how it facilitates experimentation, a key way to extract value from data and support continuous improvement and learning (see sidebar below).
A digital mindset also involves recognizing the issues around collaboration in the workplace. As someone whose work has involved many years of studying and writing about the interface between in-person and remote work, Neeley observes that Covid has broken down divisions between office workers and workers. remotely, especially given the much wider adoption of tools like Zoom, Teams, and Slack, among others.
“What gives me confidence today is that the level of empathy managers have with distant versus non-distant team members has never been higher than it is today. There is still a lot to discover, but the managers are better equipped and the communication and conversations take place on an equal footing”.
How experimentation helps foster a digital mindset
Know why: Start with a testable hypothesis and a clear rationale for how and why an experiment should be performed. Then create a learning agenda by outlining the key questions of an experiment, the steps, and how to evaluate its results.
Overcome barriers: Remove organizational barriers by making resources and data available to teams across all departments and rewarding teams that experiment. Digital experimentation can drive increased revenue, reduced costs, innovation, and employee satisfaction.
Value Failure: Establish psychological safety by defining experiences as learning opportunities. If an experiment fails, think about the lessons learned and how those lessons can be used to inform future experiments.
Recognize your data assets: The hundreds of millions of data points you have when employees and customers use their digital tools can be transformed into digital fingerprints that can form the basis of strong experiences.
The Digital Mindset, What It Really Takes to Thrive in the Age of Data, Algorithms, and AI, by Paul Leonardi and Tsedal Neely, is published by Harvard Business Review Press.