Quoting John Lennon, Bill Kanarick describes the tectonic industry shifts brought on by the pandemic: “There are decades where nothing happens, and there are weeks where decades happen.” After months of hunkering down at home, consumers got used to online shopping, telehealth doctor’s appointments and contactless and curbside pickup, effectively doubling e-commerce sales in the last 18 months.

“So just in a one-year period, what you saw is the intensification of commitment to an investment in digital transformation driven by the pandemic in part,” says Kanarick, EY’s global chief transformation architect for consulting. “Because you had to have a distributed workforce, you had to better meet the customer where the customer needed to be met.”

These new consumerist practices are here to stay, Kanarick predicts—and that means businesses have to reinvent themselves. He discusses how companies are rising to the challenge of new consumer needs and differentiates businesses that will thrive from those that will struggle to survive.

“You have to choose to commit to pursue a different future,” says Kanarick. “There’s no transformation effort on the planet that doesn’t itself come with significant risks. So, you’ve got to also understand how you’re going to mitigate and manage that downside risk.”

But Kanarick is ultimately optimistic about the future, arguing that many companies are steadfast in their commitment to adapting to the evolving digital landscape and keeping pace with customers’ digital habits. 

“If you just simply look at the past year and a half and the rate of change, and frankly in many cases, against seemingly insurmountable odds, the amount of prosperity and reinvention we were able to generate is staggering.”

Artificial intelligence holds an enormous promise, but to be effective, it must learn from massive sets of data—and the more diverse the better. By learning patterns, AI tools can uncover insights and help decision-making not just in technology, but also pharmaceuticals, medicine, manufacturing, and more. However, data can’t always be shared—whether it’s personally identifiable, holds proprietary information, or to do so would be a security concern—until now.

“It’s going to be a new age.” Says Dr. Eng Lim Goh, senior vice president and CTO of artificial intelligence at Hewlett Packard Enterprise. “The world will shift from one where you have centralized data, what we've been used to for decades, to one where you have to be comfortable with data being everywhere.”

Data everywhere means the edge, where each device, server, and cloud instance collect massive amounts of data. One estimate has the number of connected devices at the edge increasing to 50 billion by 2022. The conundrum: how to keep collected data secure but also be able to share learnings from the data, which, in turn, helps teach AI to be smarter. Enter swarm learning.

Swarm learning, or swarm intelligence, is how swarms of bees or birds move in response to their environment. When applied to data Goh explains, there is “more peer-to-peer communications, more peer-to-peer collaboration, more peer-to-peer learning.” And Goh continues, “That's the reason why swarm learning will become more and more important as …as the center of gravity shifts” from centralized to decentralized data.

Consider this example, says Goh. “A hospital trains their machine learning models on chest X-rays and sees a lot of tuberculosis cases, but very little of lung collapsed cases. So therefore, this neural network model, when trained, will be very sensitive to what's detecting tuberculosis and less sensitive towards detecting lung collapse.” Goh continues, “However, we get the converse of it in another hospital. So what you really want is to have these two hospitals combine their data so that the resulting neural network model can predict both situations better. But since you can't share that data, swarm learning comes in to help reduce that bias of both the hospitals.”

And this means, “each hospital is able to predict outcomes, with accuracy and with reduced bias, as though you have collected all the patient data globally in one place and learned from it,” says Goh.

And it’s not just hospital and patient data that must be kept secure. Goh emphasizes “What swarm learning does is to try to avoid that sharing of data, or totally prevent the sharing of data, to [a model] where you only share the insights, you share the learnings. And that's why it is fundamentally more secure.”

Advanced cybersecurity capabilities are essential to safeguard software, systems, and data in a new era of cloud, IoT, and other smart technologies. In the real estate industry, for example, companies are concerned about the potential for hijacked elevators, as well as compromised building management and HVAC systems.

According to Greg Belanger, vice president of security technologies at CBRE, the world’s largest commercial real estate services and investment firm, securing the enterprise has grown more complex—security teams must be familiar with controls and hardware on new devices, as well as what version of firmware is installed and what vulnerabilities are present. For example, if an HVAC system is connected to the internet, he questions, “Is the firmware that’s running the HVAC system vulnerable to attack? Could you find a way to traverse that network and come in and attack employees of that company?” 

Understanding enterprise vulnerabilities are crucial to safeguard physical assets but investing in the right tools can also be a challenge, says Belanger. “Artificial intelligence and machine learning need large sets of data to be effective in delivering the insights,” he explains. In the era of cloud-first and industrial internet of things (IIoT), the perimeter is becoming far more fluid. By applying AI and machine learning to datasets, he says, “You start to see patterns of risk and risky behavior start to emerge.”

Another priority when securing physical assets is to translate insights into metrics that C-suite leaders can understand to help boost decision-making. CEOs and boards of directors, who are becoming more security savvy, can benefit from aggregated scores for attack surface management. “Everybody wants to know, especially after an attack like Colonial Pipeline, could that happen to us? How secure are we?” says Belanger. But if your enterprise is able to assign merit to various features, or score them, then it’s possible to measure improvement. Belanger continues, “Our ability to see the score, react to the threats, and then keep that score improving is a key metric.”

And that’s why attack surface management is critical Belanger continues, because “we're actually getting visibility to CBRE as an attacker would, and oftentimes these tools are automated. So we're seeing far more than any one hacker would see individually. We're seeing the whole of our environment.”

This episode of Business Lab is produced in association with Palo Alto Networks.

If you drive in the United States, chances are you can’t remember the last time you bought a paper map, printed out a digital map, or even stopped to ask for directions. Thanks to GPS and the mobile mapping apps on our smartphones and their real-time routing advice, navigation is a solved problem.

But in developing or fast-growing parts of the world: not so much. If you live in a place like Doha, Qatar, where the length of the road network has tripled over the last five years, commercial mapping services from Google, Apple, Bing, or other providers simply can’t keep up with the pace of infrastructure change. 

“Each one of us who grew up in Europe or the US probably cannot understand the scale at which these cities grow,” says Rade Stanojevic, a senior scientist at the Qatar Computing Research Institute (QCRI), part of Hamad Bin Khalifa University, a Qatar Foundation university, in Doha. “Pretty much every neighborhood sees a new underpass, new overpass, new large highway being added every couple of months.” 

As Qatar copes with this rapid growth—and especially as it prepares to host the FIFA World Cup in 2022—the bad routing advice and accumulating travel delays from outdated digital maps is increasingly costly. That’s why Stanojevic and colleagues at QCRI decided to try applying machine learning to the problem.

A road network can be interpreted as a giant graph with where every intersection is a node and every road is an edge, says Stanojevic, whose specialty is network economics. Road segments can have both static characteristics, such as the designated speed limit, and dynamic characteristics, such as rush-hour congestion. To see where traffic really is going—rather than where an old map says it should go—and then predict the best routes through an ever-changing maze, all a machine-learning model would need is lots of up-to-data data on both the static and dynamic factors. “Fortunately enough, modern vehicle fleets have these monitoring systems that produce quite a lot of data,” says Stanojevic.

Stanojevic is talking about taxis. His team at QCRI partnered with a Doha-based taxi company called Karwa to collect full GPS data on their vehicles’ comings and goings. They used that data to build a new mapping service called QARTA that offers routing advice to drivers at Karwa and other operators such as delivery fleets.

Stanojevic says QARTA’s deeper understanding of the actual road and traffic situation in Doha helps drivers shave tens of seconds off every trip, which translates into a fleet-wide efficiency gain of 5 to 10 percent. “If you’re running a fleet of 3,000 cars, five percent of that is 150 cars,” Stanojevic says. “You can basically remove 150 cars from the road and not lose any business.”

Although QCRI’s system probably can’t compete with the big map-services providers in the developed world, it could help cities in the Middle East and other developing regions manage growth more wisely, Stanojevic says. And a few years from now, as more autonomous vehicles take to the streets, machine-learning-based routing advice could look at the big picture in a busy city and help fleets cut carbon emissions by keeping drivers out of traffic jams. “By having some sort of a global view of what’s going on in the whole city, autonomous vehicles can actually reroute us to have some sort of global load balancing, to help everyone be better off.”

This podcast was produced in partnership with the Qatar Foundation.

Show notes and links

Qatar Computing Research Institute

Sofiane Abbar, Rade Stanojevic, Shadab Mustafa, and Mohamed Mokbel, Traffic Routing in the Ever-Changing City of Doha, Communications of the ACM, April 2021

For the past several years, economists and government leaders have regularly sounded alarms about the dangers of big tech monopolies. On her 2020 campaign website, for example, Senator Elizabeth Warren said “big tech companies have too much power, too much power over our economy, our society, our democracy." In the months since the election, politicians on both the left and right have expressed concerns over how to encourage competition and innovation among the big tech leaders, and even how to hold onto democratic ideals in the face of digital misinformation and conspiracy theories. 

The challenge with a company like Facebook is that its business model actively encourages tribalism and anger, which is not the way markets usually work, says Paul Romer, an economics professor at New York University who previously served as the chief economist of The World Bank and was the co-recipient of the 2018 Nobel Prize in Economics Sciences. “When economists defend the market, we have this very simple idea in mind, where I as a buyer give something and get some good back,” he says. “None of those features are characteristic of this new market for digital services, where advertising is like the hidden method of capturing compensation for these firms.”

 Users, he says, “are being manipulated in ways that they don't fully understand.”

Regulators won’t work because big tech firms are too powerful, Romer maintains, while traditional antitrust laws are not well-suited to deal with this problem. However, he says that a progressive tax on digital advertising revenue, passed by state legislatures, could create a unique incentive for companies such as Google and Facebook to split up their businesses and discourage growth by acquisition.

Such a progressive tax model, however, needs to be aggressive: “The kind of tax that I think would create a big incentive to change, at say Google and Facebook, the two biggest firms in this market, has to be a tax where the average tax rate they pay right now, given their size, is 35% of their revenue.”

Show notes and links:

·     Paul Romer, Taxing Digital Advertising, May 1, 2021

·     Maryland Breaks Ground with Digital Advertising Tax, National Law Review, March 17, 2021

·     Once Tech’s Favorite Economist, Now a Thorn in Its Side, Steve Lohr, New York Times, May 20, 2021

Executives need to clearly communicate risks but also bring context to data. Tech talk is out: speaking the same language will win the day.

It’s drilled into the heads of board directors and the C-suite by scary data-breach headlines, lawyers, lawsuits, and risk managers: cybersecurity is high-risk. It’s got to be on the list of a company’s top priorities.

But how many directors get lost in the technicalities of technology? The challenge for a chief information security officer (CISO) is talking to the board of directors in a way they can understand and support the company.

Niall Browne, senior vice president and chief information security officer at Palo Alto Networks, says that you can look at the CISO-board discussion as being a classic sales pitch: successful CISOs will know how to close the deal just like the best salespeople do. “That's what makes a really good salesperson: the person that has the pitch to close” he says. “They have the ability to close the deal. So they ask for something.” 

“For ages,” Browne says, CISOs have had two big problems with boards. First, they haven’t been able speak the same language so that the board could understand what the issues were. The second problem: “There was no ask.” You can go in front of a board and give your presentation, and the directors can look like they’re in agreement, nodding or shaking their heads, and you can think to yourself, “Job done. They’re updated.” But that doesn’t necessarily mean that the business’s security posture is any better.

That’s why it’s important for CISOs to raise the board’s understanding to the level where they know what’s needed and why. Especially when it comes to new advances in cybersecurity, like attack surface management, which is “probably one of the areas that CISOs focus least on and yet is the most important,” Browne says. For example, “many times the CISO and the security team may not be able to see the wood from the trees because they're so involved in it.” And to do that, CISOs need a set of metrics so that anybody can read a board deck and within minutes understand what the CISO is trying to get across, Browne says. “Because for the most part, the data is there, but there's no context behind it.” 

This episode of Business Lab is produced in association with Palo Alto Networks.

During the past few months, Microsoft Exchange servers have been like chum in a shark-feeding frenzy. Threat actors have attacked critical zero-day flaws in the email software: an unrelenting cyber campaign that the US government has described as “widespread domestic and international exploitation” that could affect hundreds of thousands of people worldwide. Gaining visibility into an issue like this requires a full understanding of all assets connected to a company’s network. This type of continuous tracking of inventory doesn’t scale with how humans work, but machines can handle it easily.

For business executives with multiple, post-pandemic priorities, the time is now to start prioritizing security. “It’s pretty much impossible these days to run almost any size company where if your IT goes down, your company is still able to run,” observes Matt Kraning, chief technology officer and co-founder of Cortex Xpanse, an attack surface management software vendor recently acquired by Palo Alto Networks.

You might ask why companies don’t simply patch their systems and make these problems disappear. If only it were that simple. Unless businesses have implemented a way to find and keep track of their assets, that supposedly simple question is a head-scratcher.

But businesses have a tough time answering what seems like a straightforward question: namely, how many routers, servers, or assets do they have? If cybersecurity executives don’t know the answer, it’s impossible to then convey an accurate level of vulnerability to the board of directors. And if the board doesn’t understand the risk—and is blindsided by something even worse than the Exchange Server and 2020 SolarWinds attacks—well, the story almost writes itself.

That’s why Kraning thinks it’s so important to create a minimum set of standards. And, he says, “Boards and senior executives need to be minimally conversant in some ways about cybersecurity risk and analysis of those metrics.” Because without that level of understanding, boards aren’t asking the right questions—and cybersecurity executives aren’t having the right conversations.

Kraning believes attack service management is a better way to secure companies with a continuous process of asset discovery, including the discovery of all assets exposed to the public internet—what he calls “unknown unknowns.” New assets can appear from anywhere at any time. “This is actually a solvable problem largely with a lot of technology that's being developed,” Kraning says. “Once you know a problem exists, actually fixing it is actually rather straightforward.” And that’s better for not just companies, but for the entire corporate ecosystem.

Show notes and links:

“A leadership agenda to take on tomorrow,” Global CEO Survey survey, PwC

In a recent survey, “2021 Thriving in an AI World,” KPMG found that across every industry—manufacturing to technology to retail—the adoption of artificial intelligence (AI) is increasing year over year. Part of the reason is digital transformation is moving faster, which helps companies start to move exponentially faster. But, as Cliff Justice, US leader for enterprise innovation at KPMG posits, “Covid-19 has accelerated the pace of digital in many ways, across many types of technologies.” Justice continues, “This is where we are starting to experience such a rapid pace of exponential change that it’s very difficult for most people to understand the progress.” But understand it they must because “artificial intelligence is evolving at a very rapid pace.”

Justice challenges us to think about AI in a different way, “more like a relationship with technology, as opposed to a tool that we program,” because he says, “AI is something that evolves and learns and develops the more it gets exposed to humans.” If your business is a laggard in AI adoption, Justice has some cautious encouragement, “[the] AI-centric world is going to accelerate everything digital has to offer.”

Business Lab is hosted by Laurel Ruma, editorial director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next.

This podcast episode was produced in association with KPMG.

Show notes and links

“2021 Thriving in an AI World,” KPMG

There’s nothing new about conspiracy theories, disinformation, and untruths in politics. What is new is how quickly malicious actors can spread disinformation when the world is tightly connected across social networks and internet news sites. We can give up on the problem and rely on the platforms themselves to fact-check stories or posts and screen out disinformation—or we can build new tools to help people identify disinformation as soon as it crosses their screens.

Preslav Nakov is a computer scientist at the Qatar Computing Research Institute in Doha specializing in speech and language processing. He leads a project using machine learning to assess the reliability of media sources. That allows his team to gather news articles alongside signals about their trustworthiness and political biases, all in a Google News-like format.

“You cannot possibly fact-check every single claim in the world,” Nakov explains. Instead, focus on the source. “I like to say that you can fact-check the fake news before it was even written.” His team’s tool, called the Tanbih News Aggregator, is available in Arabic and English and gathers articles in areas such as business, politics, sports, science and technology, and covid-19.

Business Lab is hosted by Laurel Ruma, editorial director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next.

This podcast was produced in partnership with the Qatar Foundation.

Show notes and links

Tanbih News Aggregator

Qatar Computing Research Institute

“Even the best AI for spotting fake news is still terrible,” MIT Technology Review, October 3, 2018

The digital revolution is here, but not everyone is benefiting equitably from it. And as Silicon Valley’s ethos of “move fast and break things” spreads around the world, now is the time to pause and consider who is being left out and how we can better distribute the benefits of our new data economy. “Data is the main resource of a new digital economy,” says IT for Change director Parminder Singh. Global society will benefit because the economy will benefit, argues Singh, on decentralization of data and distributed digital models. Data commons—or open data sources—are vital to help build an equitable digital economy, but with that comes the challenge of data governance.

This podcast episode was produced by Insights, the custom content arm of MIT Technology Review. It was not produced by MIT Technology Review’s editorial staff.

“Not everybody is sharing data,” says Singh. Big tech companies are holding onto the data, which stymies the growth of an open data economy, but also the growth of society, education, science, in other words, everything. According to Singh, “Data is a non-rival resource. It's not a material resource that if one uses it, other can't use it.” Singh continues, “If all people can use the resource of data, obviously people can build value over it and the overall value available to the world, to a country, increases manifold because the same asset is available to everyone.”

One doesn’t have to look very far to understand the value of non-personal data collected to help the public, consider GIS data from government satellites. Innovation plus the open access to geographic data helped not only create the Internet we know today, but those same tech companies. And this is why Singh argues, “These powerful forces should be in the hands of people, in the hands of communities, they should be able to be influenced by regulators for public interest.” Especially now that most of the data is now collected by private companies.

IT for Change is tackling this with a research project called “Unskewing the Data Value Chain,” which is supported by Omidyar Network. The project aims to assess the current policy gaps and new policy directions on data value chains that can promote equitable and inclusive economic development. Singh explains, “Our goal is to ensure the value chains are organized in a manner where the distribution of value is fairer. All countries can digitally industrialize at if not an equal piece, but an equitable pace, and there is a better distribution of benefits from digitalization.”

Business Lab is hosted by Laurel Ruma, editorial director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next. 

This podcast was produced in partnership with Omidyar Network.

Show notes and links

“Unskewing the Data Value Chain: A Policy Research Project for Equitable Platform Economies,” IT for Change, September 2020

“Treating data as commons”, The Hindu, Parminder Singh, September 2, 2020

“Report by the Committee of Experts on Non-Personal Data Governance Framework,” Ministry of Electronics and Information Technology, Government of India

“A plan for Indian self-sufficiancy in an AI-driven world,” Mint, Parminder Singh, July 29, 2020