While making digital the main channel of customer engagement, banks are also looking to move beyond business as usual, says Amit Anand, a Vice President in Cognizant Consulting’s Banking and Financial Services.
COVID-19 made online channels indispensable for bank customers, including those who preferred in-person banking. This accelerated their digital strategies and created an opportunity to go beyond the basics and become partners in their customers’ pursuit of financial wellness.
As banks bet big on digital, they are looking at technologies such as AI, advanced analytics, and automation to provide personalization, prediction and speed in creating powerful customer experiences. Banks are also increasingly relying on machines to automate repetitive tasks and make complex decisions, creating demand for human skillsets that complement intelligent machines.
Cognizant’s Center for the Future of Work (CFoW), working with Oxford Economics, recently surveyed 4,000 C-level executives globally, including 287 senior banking and financial services executives to understand how banks are adapting to fast and dramatic changes.
The earliest forms of digital banking trace back to the advent of ATMs and cards launched in the 1960s. As the internet emerged in the 1980s with early broadband, digital networks began to connect retailers with suppliers and consumers to develop needs for early online catalogues and inventory software systems.
By the 1990s the Internet became widely available and online banking started becoming the norm. The improvement of broadband and ecommerce systems in the early 2000s led to what resembled the modern digital banking world today. The proliferation of smartphones through the next decade opened the door for transactions on the go beyond ATM machines. Over 60% of consumers now use their smartphones as the preferred method for digital banking.
The challenge for banks is now to facilitate demands that connect vendors with money through channels determined by the consumer. This dynamic shapes the basis of customer satisfaction, which can be nurtured with Customer Relationship Management (CRM) software. Therefore, CRM must be integrated into a digital banking system, since it provides means for banks to directly communicate with their customers.
There is a demand for end-to-end consistency and for services, optimized on convenience and user experience. The market provides cross platform front ends, enabling purchase decisions based on available technology such as mobile devices, with a desktop or Smart TV at home. In order for banks to meet consumer demands, they need to keep focusing on improving digital technology that provides agility, scalability and efficiency.
Seven Ways to Capitalize on Digital
Institute front-to-back digitization. Banks can effectively compete with fintech competitors by becoming digital institutions.
Explore new customer segments and business paradigms. Digital makes it easier than ever for banks to explore small business segments, even as they pursue existing markets.
Emphasize platform centricity and smart aggregation. Open banking standards can help banks to provide personalized products to customers in collaboration with third-party providers and fintechs.
Invest in personalizing the customer relationship. Banks should use personalized experiences to make customers’ lives as frictionless as possible.
Focus on re-building trust and resiliency. Banks need to eliminate any biases in decisions made by machines.
Enshrine inclusivity into your digital strategy. Banks should use digital to reach customers who are left out by being physically and cognitively challenged.
Balance machine-driven and human-centric work. Create sturdy human-machine collaboration by reevaluating jobs for a shared environment.
Amit Anand is Vice President and North American Practice Leader for Cognizant Consulting’s Banking and Financial Services. Amit has 20 years of experience with firms such as Accenture, Infosys and Cognizant. He has successfully led and managed large business transformation, digital and IT transformation, and associated organizational change management for several financial services clients. Amit is a recognized thought leader with more than 15 publications on topics such as Open Banking, Digital 2.0 and new-age operating models. He can be reached at Amit.Anand@cognizant.com
Manish Bahl leads the Cognizant Center for the Future of Work in Asia-Pacific and the Middle East. A respected speaker and thinker, Manish has guided many Fortune 500 companies into the future of their business with his thought-provoking research and advisory skills. Within Cognizant’s Center for the Future of Work, he helps ensure that the unit’s original research and analysis jibes with emerging business-technology trends and dynamics in APAC, and collaborates with a wide range of leading thinkers to understand and predict how the future of work will take shape. He most recently served as Vice President, Country Manager with Forrester Research in India. He can be reached at Manish.Bahl@cognizant.com
Blockchain analytics firm AnChain.AI has signed a deal with the U.S. Securities and Exchange Commission (SEC) to help monitor and regulate the turbulent decentralized finance (DeFi) industry, according to a company spokesperson. The initial value of the contract is $125,000, with five separate one-year $125,000 option years for a total of $625,000.
According to CEO and co-founder Victor Fang, “The SEC is very keen on understanding what is happening in the world of smart contract-based digital assets…so we are providing them with technology to analyze and trace smart contracts.”
AnChain.AI is a San Jose-based artificial intelligence and machine learning blockchain startup that focuses on tracking illicit activity across crypto exchanges, DeFi protocols, and traditional financial institutions. In revealing the SEC contract, which started in May 2021, the company also announced today a $10 million Series A round of funding led by an affiliate of Susquehanna Group, SIG Asia Investments LLP, at an undisclosed valuation.
The deal comes on the heels of the SEC taking further interest in DeFi as it rapidly matures and grows in size. The industry currently manages more than $82 billion, and the largest decentralized exchange, Uniswap, processed over $1.8 billion worth of transactions in the last 24 hours, many of which included tokens that could be determined to be securities by the SEC.
Additionally, these platforms are becoming increasingly complex. Fang noted that the Uniswap platform is actually an amalgam of 30,000 separate smart contracts that execute the actual exchange of tokens.
The SEC’s first major action against the DeFi space came in 2018, when it shut down EtherDelta, a ‘DeFi’ exchange that it deemed to be operating illegally.
In an August interview with The Wall Street Journal, SEC Chairman Gary Gensler warned that DeFi operations are not immune from oversight because they use the word decentralized, and that “There’s still a core group of folks that are not only writing the software, like the open source software, but they often have governance and fees…There’s some incentive structure for those promoters and sponsors in the middle of this.”
SEC Commissioner Hester Peirce echoed this sentiment in a March interview with Forbes, but perhaps in an acknowledgement of the potential in DeFi asked these projects to come forward and be pro-active with the regulator, “When you start to look at the tokens themselves and try to figure out whether they’re securities, it does get kind of confusing.
In particular, it’s so hard in the DeFi landscape because there’s such variety. This is why I encourage individual projects to come in and talk to the SEC because it really does require a look at the very particular facts and circumstances.”
In addition to cataloguing and monitoring known wallets tied to illicit actors, AnChain.AI has built a predictive engine that can be used to identify unknown addresses and transactions that could be suspicious. This is all part of Fang’s goal to move beyond doing “post-incident investigations” to move the “defense all the way up to the upstream” and make it “preventive”.
Aside from government clients, AnChain.AI’s technology is also being used by centralized cryptocurrency exchanges and traditional financial institutions. In a press release, Ye Li, Investment Manager at SIG said of the investment, “AnChain.AI has made great progress in developing its market-leading crypto security technology to meet its customers’ broad demand in regulatory compliance and transaction intelligence.”
For decades, anthropologists have been telling us that it’s often the informal, unplanned interactions and rituals that matter most in any work environment. So how much are we missing by giving them up?
n the summer of 2020, Daniel Beunza, a voluble Spanish social scientist who taught at Cass business school in London, organized a stream of video calls with a dozen senior bankers in the US and Europe. Beunza wanted to know how they had run a trading desk while working from home. Did finance require flesh-and-blood humans?
Beunza had studied bank trading floors for two decades, and had noticed a paradox. Digital technologies had entered finance in the late 20th century, pushing markets into cyberspace and enabling most financial work to be done outside the office – in theory. “For $1,400 a month you can have the [Bloomberg] machine at home.
You can have the best information, all the data at your disposal,” Beunza was told in 2000 by the head of one Wall Street trading desk, whom he called “Bob”. But the digital revolution had not caused banks’ offices and trading rooms to disappear. “The tendency is the reverse,” Bob said. “Banks are building bigger and bigger trading rooms.”
Why? Beunza had spent years watching financiers like Bob to find the answer. Now, during lockdown, many executives and HR departments found themselves dealing with the same issue: what is gained and what is lost when everyone is working from home? But while most finance companies focused on immediate questions such as whether employees working remotely would have still access to information, feel part of a team and be able to communicate with colleagues, Beunza thought more attention should be paid to different kinds of questions:
How do people act as groups? How do they use rituals and symbols to forge a common worldview? To address practical concerns about the costs and benefits of remote working, we first need to understand these deeper issues. Office workers make decisions not just by using models and manuals or rational, sequential logic – but by pulling in information, as groups, from multiple sources. That is why the rituals, symbols and space matter.
“What we do in offices is not usually what people think we do,” Beunza told me. “It is about how we navigate the world.” And these navigation practices are poorly understood by participants like financiers – especially in a digital age.The engineers who created the internet have always recognised that people and their rituals matter. Since it was founded in 1986, the Internet Engineering Task Force (IETF) has provided a place for people to meet and collectively design the architecture of the web.
Its members wanted to make design decisions using “rough consensus”, since they believed the internet should be an egalitarian community where anybody could participate, without hierarchies or coercion. “We reject: kings, presidents and voting. We believe in: rough consensus and running code” was, and still is, one of its key mantras.
To cultivate “rough consensus”, IETF members devised a distinctive ritual: humming. When they needed to make a crucial decision, the group asked everyone to hum to indicate “yay” or “nay” – and proceeded on the basis of which was loudest. The engineers considered this less divisive than voting.
Some of the biggest decisions about how the internet works have been made using this ritual. In March 2018, in a bland room of the Hilton Metropole on London’s Edgware Road, representatives from Google, Intel, Amazon, Qualcomm and others were gathered for an IETF meeting. They were debating a controversial issue: whether or not to adopt the “draft-rhrd-tls-tls13-visibility-01” protocol. To anybody outside the room, it might sound like gobbledegook, but this protocol was important.
Measures were being introduced to make it harder for hackers to attack crucial infrastructure such as utility networks, healthcare systems and retail groups. This was a mounting concern at the time – a year or so earlier, hackers seemingly from Russia had shut down the Ukrainian power system. The proposed “visibility” protocol would signal to internet users whether or not anti-hacking tools had been installed.
For an hour the engineers debated the protocol. Some opposed telling users the tools had been installed; others insisted on it. “There are privacy issues,” one said. “It’s about nation states,” another argued. “We cannot do this without consensus.” So a man named Sean Turner – who looked like a garden gnome, with a long, snowy-white beard, bald head, glasses and checked lumberjack shirt – invoked the IETF ritual.
“We are going to hum,” he said. “Please hum now if you support adoption.” A moan rose up, akin to a Tibetan chant, bouncing off the walls of the Metropole. “Thanks. Please hum now if you oppose.” There was a much louder collective hum. “So at this point there is no consensus to adopt this,” Turner declared. The protocol was put on ice.
Most people do not even know that the IETF exists, much less that computer engineers design the web by humming. That is not because the IETF hides its work. On the contrary, its meetings are open to anyone and posted online. But phrases like “draft-rhrd-tls-tls1.3” mean most people instinctively look away, just as they did with derivatives before the 2008 financial crisis. And, as with finance, this lack of external scrutiny – and understanding – is alarming, particularly given the accelerating effects of innovations such as AI.
Many of the engineers who build the technologies on which we rely are well-meaning. But they – like financiers – are prone to tunnel vision, and often fail to see that others may not share their mentality. “In a community of technological producers, the very process of designing, crafting, manufacturing and maintaining technology acts as a template and makes technology itself the lens through which the world is seen and defined,” observes Jan English-Lueck, an anthropologist who has studied Silicon Valley.
When the IETF members use humming, they are reflecting and reinforcing a distinctive worldview – their desperate hope that the internet should remain egalitarian and inclusive. That is their creation myth. But they are also signalling that human contact and context matter deeply, even in a world of computing. Humming enables them to collectively demonstrate the power of that idea. It also helps them navigate the currents of shifting opinion in their tribe and make decisions by reading a range of signals.
Humming does not sit easily with the way we imagine technology, but it highlights a crucial truth about how humans navigate the world of work, in offices, online or anywhere else: even if we think we are rational, logical creatures, we make decisions in social groups by absorbing a wide range of signals. And perhaps the best way to understand this is to employ an idea popularised by anthropologists working at companies such as Xerox during the late 20th century, and since used by Beunza and others on Wall Street: “Sense-making”.
One of the first thinkers to develop the concept of sense-making was a man named John Seely Brown. JSB, as he was usually known, was not trained as an anthropologist. He studied maths and physics in the early 60s, and finished a PhD in computer science in 1970, just as the idea of the internet was emerging, and then taught advanced computing science at the University of California, with a particular interest in AI. Around this time, after meeting some sociologists and anthropologists, he became fascinated by the question of how social patterns influence the development of digital tools, too.
He applied for a research post at Xerox’s Palo Alto Research Center (Parc), a research arm that the Connecticut-based company set up in Silicon Valley in 1969. Xerox was famous for developing the photocopier, but it also produced many other digital innovations. The authors of Fumbling the Future, a book about the history of the company, credits it with inventing “the first computer ever designed and built for the dedicated use of a single person … the first graphics-oriented monitor, the first handheld ‘mouse’ simple enough for a child, the first word-processing programme for non-expert users, the first local area communications network … and the first laser printer.”
During his application process to Parc, JSB met Jack Goldman, its chief scientist. The two men discussed Xerox’s research and development work, and its pioneering experiments with AI. Then JSB pointed to Goldman’s desk. “Jack, why two phones?” he asked. The desk contained both a “simple” phone and a newer, more sophisticated model.
“Oh my God, who the hell can use this phone?” Goldman said, referring to the new phone. “I have it on my desk because everyone has to have one, but when real work gets done I’ve got to use a regular one.”
That was exactly the kind of thing, Seely Brown said, that scientists at Xerox should also be researching: how humans were (or were not) using the dazzling innovations that Silicon Valley companies kept creating. Having started steeped in “hard” computing science, JSB realised that it paid to be a “softie” when looking at social science, or – to employ the buzzwords that were later popularised in Silicon Valley by the writer Scott Hartley – to be a techie and a “fuzzy”.
JSB joined Parc and put his new theories to work. Although the research centre had initially been dominated by scientists, by the time JSB arrived, a collection of anthropologists, psychologists and sociologists were also there. One of these anthropologists was a man named Julian Orr, who was studying the “tribe” of technical repair teams at Xerox.
By the late 20th century, copy machines were ubiquitous in offices. Work could collapse if one of these machines broke down. Xerox employed numerous people whose only job was to travel between offices, servicing and fixing machines. These technicians were routinely ignored, partly because the managers assumed that they knew what they did. But Orr and JSB suspected this was a big mistake, and that the technicians did not always think or behave as their bosses thought they should.
JSB first noticed it early in his time at Xerox, when he met a repairman known as “Mr Troubleshooter”, who said to him: “Well, Mr PhD, suppose this photocopier sitting here had an intermittent image quality fault, how would you go about troubleshooting it?”
JSB knew there was an “official” answer in the office handbook: technicians were supposed to “print out 1,000 copies, sort through the output, find a few bad ones, and compare them to the diagnostic”. It sounded logical – to an engineer.
“Here is what I do,” Mr Troubleshooter told JSB, with a “disgusted” look on his face. “I walk to the trash can, tip it upside down, and look at all the copies that have been thrown away. The trash can is a filter – people keep the good copies and throw the bad ones away. So just go to the trash can … and from scanning all the bad ones, interpret what connects them all.” In short, the engineers were ignoring protocols and using a solution that worked – but one that was “invisible … and outside [the] cognitive modelling lens” of the people running the company, JSB ruefully concluded.
How common was this kind of subversive approach? Orr set off to find out. He first enrolled in technical training school. Then he shadowed the repair teams out on service calls, at the parts depot, eating lunch and just hanging out when there was not much work to do. The fact that Orr had once worked as a technician himself helped in some respects: the repair crews welcomed him in. But it also created a trap: he sometimes had the same blind spots as the people he was studying. “I had a tendency to regard certain phenomena as unremarkable which are not really so to outsiders,” he later wrote in a report. He had to perform mental gymnastics to make “familiar” seem “strange”.
So, like many other anthropologists before him, he tried to get that sense of distance by looking at the group rituals, symbols and spatial patterns that the technicians used in their everyday life. Or quickly realized that many of the most important interactions took place in diners. “I drive to meet the members of the customer support team for breakfast at a chain restaurant in a small city on the east side,” Orr observed in one of his field notes. “Alice has a problem: her machine reports a self-test error, but she suspects there is some other problem … [so] we are going to lunch at a restaurant where many of [Alice’s] colleagues eat, to try to persuade Fred, the most experienced [technician], to go to look at the machine with her …
Fred tells her there is another component that she needs to change, according to his interpretation of the logs.” The repair teams were doing collective problem solving over coffee in those diners, using a rich body of shared narrative about the Xerox machines, and almost every other part of their lives. Their “gossip” was weaving a wide tapestry of group knowledge, and tapping into the collective views of the group – like the IETF humming.
This knowledge mattered. The company protocols assumed that “the work of technicians was the rote repair of identical broken machines,” as Lucy Suchman, another anthropologist at Parc, noted. But that was a fallacy: even if the machines seemed identical when they emerged from the Xerox factory, by the time repairmen encountered the machines they had histories shaped by humans. What engineers shared at the diner was this history and context. “Diagnosis is a narrative process,” Orr said.
The Xerox scientists eventually listened to the anthropologists – to some degree. After Orr issued his report on the technicians, the company introduced systems to make it easier for repair people to talk to one another in the field and share knowledge – even outside diners. A two-way radio system allowed tech reps in different regions to call on each other’s expertise. Xerox later supplemented these radios with a rudimentary messaging platform on the internet known as Eureka, where technicians could share tips. JSB viewed this as “an early model for social media platforms”.
Other Silicon Valley entrepreneurs became increasingly fascinated by what Parc was doing, and tried to emulate its ideas. Steve Jobs, a co-founder of Apple, toured Parc in 1979, saw the group’s efforts to build a personal computer, and then developed something similar at Apple, hiring away a key Parc researcher. Other Parc ideas were echoed at Apple and other Silicon Valley companies. But Xerox’s managers were not nearly as adept as Jobs in terms of turning brilliant ideas into lucrative gadgets, and in subsequent decades Xerox’s fortunes ailed.
That was partly because the company culture was conservative and slow-moving, but also because Parc was based on the west coast, while the main headquarters and manufacturing centres were on the other side of the country. Good ideas often fell between the cracks, to the frustration of Parc staff.
Still, as the years passed, Parc’s ideas had a big impact on social science and Silicon Valley. Their work helped to spawn the development of the “user experience” (UX) movement, prodding companies such as Microsoft and Intel to create similar teams. Their ideas about “sense-making” spread into the consumer goods world, and from there to an unlikely sphere: Wall Street.
A social scientist named Patricia Ensworth was one of the first to use sense-making in finance. Starting in the 80s, she decided to use social science to help explain why IT issues tended to generate such angst in finance. Her research quickly showed that the issues were social and cultural as much as technical. In one early project she found that American software coders were completely baffled as to why their internally developed software programmes kept malfunctioning – until she explained that office customs in other locations were different.
In the early 90s, Ensworth joined Moody’s Investors Service, and eventually became director of quality assurance for its IT systems. It sounded like a technical job. However, her key role was pulling together different tribes – software coders, IT infrastructure technicians, analysts, salespeople and external customers. Then she formed a consultancy to advise on “project management, risk analysis, quality assurance and other business issues”, combining cultural awareness with engineering.
In 2005, Ensworth received an urgent message from a managing director at a major investment bank. “We need a consultant to help us get some projects back on track!” the manager said. Ensworth was used to such appeals: she had spent more than a decade using techniques pioneered by the likes of Orr and Seely Brown in order to study how finance and tech intersected with humans.
The investment bank project was typical. Like many of its rivals, this bank had been racing to move its operations online. But by 2005 it was facing a crisis. Before 2000 it had outsourced much of its trading IT platform to India, since it was cheaper than hiring IT experts in the US. But while the Indian coders and testers were skilled at handling traditional investment products, they struggled to cope with a new derivatives business that the bank was building, since the Indian coders had formal, bureaucratic engineering methods. So the bank started to use other suppliers in Ukraine and Canada who had a more flexible style and were used to collaborating with creative mathematicians. But this made the problems even worse: deadlines were missed, defects emerged and expensive disputes erupted.
“In the New York office, tensions were running high between the onsite employees of rival outsourcing vendors,” Ensworth later wrote. “The pivot point occurred when a fight broke out: a male Canadian tester insulted a female Indian tester with X-rated profanity and she threw hot coffee in his face. Since this legally constituted a workplace assault, the female tester was immediately fired and deported. Debates about the fairness of the punishment divided the office … [and] at the same time auditors uncovered some serious operational and security violations in the outsourced IT infrastructures and processes.”
Many employees blamed the issues on inter-ethnic clashes. But Ensworth suspected another, more subtle problem. Almost all the coders at the bank, whether they were in India, Manhattan, Kyiv or Toronto, had been trained to think in one-directional sequences, driven by sequential logic, without much lateral vision. The binary nature of the software they developed also meant that they tended to have an “I’m-right-you’re-wrong” mentality. Although the coders could produce algorithms to solve specific problems, they struggled to see the whole picture or collaborate to adapt as conditions changed. “The [coders] document their research in the form of use cases, flowcharts and system architecture designs,” Ensworth observed. “These documents work well enough for version 1.0, because the cyberspace model matches the user community’s lived experience. But over time, the model and the reality increasingly diverge.”
The coders often seemed unaware of the gap between their initial plan and subsequent reality. Ensworth persuaded the suppliers in India to provide training about American office rules and customs, and tried to teach the suppliers in Ukraine and Canada about the dangers of taking an excessively freewheeling approach to IT. She showed coders videos of the noisy and chaotic conditions on bank trading floors; that was a shock, since coders typically toiled in library-like silence and calm. She explained to managers at the bank that coders felt angry that they could not access important proprietary databases and tools. The goal was to teach all “sides” to copy the most basic precept of anthropology: seeing the world from another point of view.
Ensworth did not harbour any illusions about changing the bank’s overall culture. When the financial crisis erupted in 2008, the project was wound down and she moved on. However, she was thrilled to see that during the 18 months that she worked at the bank, some of the anthropology lessons stuck. “Delivery schedules and error rates were occasionally troublesome, but no longer a constant, pervasive worry,” she later wrote. Better still, the workers stopped throwing coffee around the office.
But what would happen to the business of sense-making at work if humans were suddenly prevented from working face to face? As he hovered like a fly on the wall of trading rooms on Wall Street and in the City of London in the early 2000s, Beunza often asked himself that question. Then, in the spring of 2020, he was unexpectedly presented with a natural experiment. As Covid-19 spread, financial institutions suddenly did what Bob had said they never would – they sent traders home with their Bloomberg terminals. So, over the course of the summer, Beunza contacted his old Wall Street contacts to ask a key question: what happened?
It was not easy to do the research. Anthropology is a discipline that prizes first-hand observations. Conducting research via video calls seemed to fly in the face of that. “A lot of my work depends on speaking to people face to face, understanding how they live their lives on their own terms and in their own spaces,” said Chloe Evans, an anthropologist at Spotify, to a conference convened in 2020 to discuss the challenge. “Being in the same space is vital for us to understand how people use products and services for the companies we work for.”
However, ethnographers realized there were benefits to the new world, too: they could reach people around the world on a more equal footing, and sometimes with more intimacy. “We see people in contexts not available to us in lab situations,” observed an ethnographer named Stuart Henshall, who was doing research among poor communities in India. Before the pandemic, most of the Indian people he interviewed were so ashamed of their domestic spaces that they preferred to meet in a research office, he explained. But after lockdown, his interviewees started talking to him via video calls from their homes and rickshaws, which enabled him to gain insight into a whole new aspect of their lives. “Participants are simply more comfortable at home in their environment. They feel more in control,” he observed. It was a new of type of ethnography.
When Beunza interviewed bankers remotely, he found echoes of this pattern: respondents were more eager to engage with him from home than in the office, and it felt more intimate. The financiers told him that they had found it relatively simple to do some parts of their job remotely, at least in the short term: working from home was easy if you were writing computer code or scanning legal documents. Teams that had already been working together for a long time also could interact well through video links.
The really big problem was incidental information exchange. “The bit that’s very hard to replicate is the information you didn’t know you needed,” observed Charles Bristow, a senior trader at JP Morgan. “[It’s] where you hear some noise from a desk a corridor away, or you hear a word that triggers a thought. If you’re working from home, you don’t know that you need that information.” Working from home also made it hard to teach younger bankers how to think and behave; physical experiences were crucial for conveying the habits of finance or being an apprentice.
Beunza was not surprised to hear that the financiers were eager to get traders back to the office as soon as they could; nor that most had quietly kept some teams working in the office throughout the crisis. Nor was he surprised that when banks such as JPMorgan started to bring some people back in – initially at 50% capacity – they spent a huge amount of time devising systems to “rotate” people; the trick seemed not to be bringing in entire teams, but people from different groups. This was the best way to get that all-important incidental information exchange when the office was half-full.
But one of the most revealing details from Beunza’s interviews concerned performance. When he asked the financiers at the biggest American and European banks how they had fared during the wild market turmoil of spring 2020, “the bankers said that their trading teams in the office did much, much better than those at home,” Beunza told me in the autumn of 2020. “The Wall Street banks kept more teams in the office, so they seem to have done a lot better than Europeans.” That may have been due to malfunctions on home-based tech platforms. But Beunza attributed it to something else: in-person teams had more incidental information exchange and sense-making, and at times of stress this seemed doubly important.
The bankers that Beunza observed were not the only ones to realize the value of being together in the same physical space. The same pattern was playing out at the IETF. When the pandemic hit, the IETF organizers decided to replace in-person conventions with virtual summits. A few months later they polled about 600 members to see how they felt about this switch. More than half said they considered online meetings less productive than in-person, and only 7% preferred meeting online. Again, they missed the peripheral vision and incidental information exchange that happened with in-person meetings. “[Online] doesn’t work. In person is NOT just about the meeting sessions – it is about meeting people outside the meetings, at social events,” complained one member. “The lack of serendipitous meetings and chats is a significant difference,” said another. Or as one of them put it: “We need to meet in person to get meaningful work done.”
They also missed their humming rituals. As the meetings moved online, two-thirds of the respondents said they wanted to explore new ways to create rough consensus. “We need to figure out how to ‘hum’ online,” said one member. So the IETF organizers experimented with holding online polls. But members complained that virtual polls were too crude and one-dimensional; they crave a more nuanced, three-dimensional way to judge the mood of their tribe. “The most important thing to me about a hum is some idea of how many people present hummed at all, or how loudly. Exact numbers don’t matter, proportionality does,” said one.
JUST over five years ago, Anglo American was in deep trouble. The natural resources giant, beset by a collapse in commodity prices, scrapped its dividend and announced plans to close mines and cut thousands of workers. Amid talk of an emergency capital raise, its market value fell to less than US$3 billion.
Last week, the trials of 2016 probably seemed like a parallel universe to its chief executive officer Mark Cutifani.
Fuelled by a rally in iron ore and other commodity prices, he announced record first-half earnings and billions in dividends. Anyone who took a punt on Anglo’s shares when they reached their nadir, would have seen a 14-fold increase as the market capitalization soared to US$55 billion.
“High commodity prices have been very important to us,” Mr Cutifani told investors last week. “We don’t think this is as good as it gets.”
Anglo American is one of many. With raw materials prices surging, the whole natural resources sector is showering shareholders with special dividends and buybacks as miners, oil drillers, trading houses, steelmakers and farmers reap billions in windfall profits.
The sector, marked down by investors because of its contribution to climate change and a reputation of squandering money on mega projects, is again a great cash machine.
The economic rebound from last year’s Covid slump has powered an explosive rally in commodity prices as consumers forgo vacations and dining out and spend their money loading up on physical goods instead: everything from patio heaters to start-of-the-art TVs. Politicians are helping, too, lavishing hundreds of billions on resource-heavy infrastructure projects.
The Bloomberg Commodity Spot Index, a basket of nearly two dozen raw materials, surged to a 10-year high last week and is rapidly closing in on the record set in 2011.
Brent crude, the global oil benchmark, has again surged above US$75 a barrel, copper is headed back towards US$10,000 a tonne, European natural gas is at its highest ever for the summer season, and steel is changing hands at unprecedented levels. Agricultural commodities such as corn, soya beans and wheat are also expensive.
“Demand continues to improve with increasing global vaccinations,” Joe Gorder, the chief executive of Valero Energy, one of the world’s largest oil refiners, said last week.
Even commodities long left for dead, like thermal coal, are enjoying a new life in 2021. Coal, burned in power stations to produce electricity, together with huge volumes of carbon emissions, is trading at a 10-year high.
While commodities prices are the main reason behind the turnaround, there are structural factors at play as well.
Miners and oil companies have cut spending in new projects savagely, creating a supply shortfall. The miners were first, as they curbed investment from 2015 to 2016 as investors demanded more discipline; oil companies followed up last year and some major energy companies last week announced further cuts in spending for 2021.
The result is that while demand is surging, supply is not – at least for now. The oil majors are benefiting too from the work of the Organization of the Petroleum Exporting Countries alliance of oil producers, which is still holding back a large share of output.
Anglo American, which announced US$4 billion in dividends, is probably the most remarkable turnaround story in the natural resources sector, but its profits were still dwarfed by its bigger rivals. Rio Tinto and Vale, the world’s two leading iron ore miners, together vowed to hand back more than US$17 billion in dividends recently. There is still more to come for investors, with both BHP, the world’s biggest miner, and Glencore, another big miner and commodity trader, yet to report.
And for once, the world’s biggest steelmakers were not only able to absorb the costs, but pass them on. An industry that has spent much of the last decade in crisis is now also able to reward long-suffering shareholders.
The world’s largest steelmaker outside China, ArcelorMittal, that was forced to sell shares and scrap its dividend just five years ago, posted its best results since 2008 last week and announced a US$2.2 billion share buyback programme.
The miners have stolen the spotlight from the energy industry, traditionally the biggest dividend payer in the natural resources industry.
Still, Big Oil recovered from the historic price collapse of 2020, when a vicious Saudi-Russian price war and the Covid-19 pandemic briefly sent the value of West Texas Intermediate, the US oil benchmark, below zero. Supported by rising oil, natural gas, and, above all, the chemicals that go into plastics, Exxon Mobil, Chevron, Royal Dutch Shell, and TotalEnergies delivered profits that went to pre-covid levels.
With cash flow surging, Shell, which last year cut its dividend for the first time since World War II, was able to hike it nearly 40 per cent, and announced an additional US$2 billion in buybacks. “We wanted to signal to the market the confidence that we have in cash flows,” Shell CEO Ben van Beurden said.
Chevron and Total also announced they will buy shares. Exxon, though, is still licking its wounds and focused on paying down debt.
The more opaque world of commodity trading has also cashed in. Glencore said last week that it was expecting bigger trading profits than forecast, with rivals Vitol and Trafigura, two of the world’s largest oil traders, also benefiting from lucrative opportunities created by rocketing prices.
The agricultural traders have cashed on higher prices and unusually strong demand from China.
Bunge, a trader that is the world’s largest crusher of soya beans, told investors it expected to deliver its best earnings-per-share since its initial public offer two decades ago. Archer-Daniels-Midland Co, another big American grain trader and processor, also flagged strong earnings. And Cargill, the world’s largest agricultural trader, is heading towards record earnings in its 2021 fiscal year.
Whether the natural resources boom can last is hotly contested. Many investors worry climate change makes the long-term future of the industry hard to read and they also fret about the tendency of executives to approve expensive projects at the peak of the cycle.
Mining executives fear Chinese demand will slow down at some point, hitting iron ore in particular. But the current lack of investments may support other commodities, like copper and oil.
But Shell’s Mr van Beurden summed up the bullish case last week: “Supply is going to be constrained, and demand is actually quite strong”. BLOOMBERG
Buying an iced coffee can be hit and miss. Although it is absurdly simple on paper – coffee served over ice and stirred with milk – there are all manner of variables that can affect quality. So, perhaps it’s best you just make your own. I spoke to Stuart Wilson, the founder of Whitstable-based speciality roastery Lost Sheep Coffee and Lynsey Harley of the Fife roastery and Edinburgh cafe Modern Standard, on how to make the perfect version at home.
Step one: coffee
You have three choices here, but the foundation of each is the right sort of bean. “I would recommend a Brazilian or a Colombian,” says Wilson. “You want a nice, sweet, full-bodied coffee … preferably single origin.” This is important. Coffee blends – which is to say a mixture of coffee from different countries – are typically designed to be brewed hot. When combined with heated milk, their natural sugars are brought to the fore.
If you use a blend to make iced coffee, this process doesn’t occur and the finished product might be more acidic than you expect. Wilson’s advice is to “get the purest, most fulfilling flavour in its simplest form, because then there’s less to go wrong”. Also important: don’t buy pre-ground coffee from supermarkets. The quality is poor, the grind isn’t fine enough for espresso, and it will be staler than if you buy direct from a roaster.
The easiest option: capsules If you are in a hurry, the quickest way to make an iced coffee is to use a Nespresso (or equivalent) machine. Plenty of specialty roasters make Nespresso-compatible pods, many of which are now, thankfully, compostable.
The expensive option: espresso machine If you are lucky enough to have an espresso machine at home, you can also get your shot of hot coffee this way. The key here is consistency. When Wilson makes an espresso, the process is exact: “We weigh out exactly 18.5 grams of freshly ground coffee, and then tamp it softly and evenly. We would then time the espresso extraction, a good starting point is 30 seconds, and weigh the espresso extraction at the end, to ensure that we have 30 grams of espresso”.
The longest option: cold brew If neither of these are available, you can try making your own cold brew. This requires a lot more time, because cold water won’t dissolve the coffee’s solubles as much or as quickly as hot water. You may also want to choose a different type of bean for this method since, as Harley points out: “You’ll probably want light to medium roasts, as anything darker might be too bitter”.
The recipe is simple, though: take your cafetière, add 120g of coarsely ground coffee per litre of cold water, stir (but definitely do not plunge), and leave in the fridge for 24 hours. After that, you can plunge it, or feed it through a filter, then decant it into a jug or pot. This is your cold brew coffee. But remember, as Wilson says, this will be weaker than an espresso extraction, so use a double shot for the iced coffee.
Step two: sweeten
Now, theoretically, you have chosen and prepared the perfect type of coffee in the perfect way, which means it should be sweet enough to drink as is. But if you do need to add sugar, do so when you are pouring the hot coffee. If you’re using a cold brew, put sugar in the glass before the coffee. If you’re using a capsule or an espresso machine, sweeten the drink before you tip it into the glass.
Step three: serve over ice
Good news, the hardest part is over. Wilson describes the rest of the process as simply: “Coffee in, ice over the top, milk, stir,” but let’s break that down a bit. Your shot of (hot, unless you’ve cold brewed) coffee goes in the bottom of a glass. Now it’s time to add the ice. You want to fill the glass between a third and a half with ice, to cool the coffee down. Your first few cubes will melt quickly, because of the heat of the coffee. The alternative method is to blend the ice, but this has its own pitfalls.
As Harley says: “Blended drinks just change the dilution of the coffee, so blending the ice with the milk and espresso turns it more into a slushie. If people want to blend the ice, they might want to consider increasing their espresso, maybe adding another double, so that once blended it tastes more balanced. But I’m more of a cube purist. I like just to have a chilled drink, and not a blended one.”
Step four: add milk
Now you top up the glass with milk. Again, personal preference comes before anything else here, so feel free to experiment with what works for you, but Wilson thinks that “full-fat milk” gives a more full-bodied flavour. But milk alternatives will work just as well. If you’re non-dairy, Harley suggests avoiding soy milk, since “the acidity in the coffee can often split soy and that’s not ideal”. Instead, she says, “Oat milk works well cold, and it adds a really lovely biscuity flavour to the iced coffees. Coconut milk is another good substitute, and it’s a good summer flavour, too.”
Step five: stir vigorously
Your coffee is almost ready. The final step, according to Wilson, is to stir. “I’m not talking electric whisks,” he says. “I’m talking a good four or five full stirs around the cup. What you want to do is get the coffee, the milk and the ice to combine and chill down hard, and then you get that really nice, ice coldness all the way through.”
Iceland has achieved the holy grail for working stiffs: same pay for shorter hours.Results from two trials of reduced hours showed no productivity loss or decline in service levels, while employees reported less stress and an improved work-life balance, researchers at U.K.-based think tank Autonomy and Iceland’s Association for Sustainable Democracy said in a report.
Achieving shorter hours with sustained productivity and service levels involved rethinking how tasks were completed, according to the report. That included shortening meetings or replacing them with emails, cutting out unnecessary tasks, and rearranging shifts.
The trials, conducted from 2015 to 2019, cut hours to about 35 a week from 40 with no reduction in pay. Involving about 2,500 workers, equivalent to more than 1% of the Nordic country’s working population, results showed their “wellbeing dramatically increased,” the researchers said. Since then, 86% of Iceland’s entire working population have either moved to shorter hours or can negotiate to do so.
In Nordic peer Finland, Prime Minister Sanna Marin, 35, has suggested a four-day work week is worth looking into, saying employees deserve some of the trickle-down benefits of improved productivity. Even so, her government is currently not working on such policy.
Workers went from a 40-hour weekly schedule to 35- or 36-hour weekly schedules without a reduction in pay. The trials were launched after agitation from labor unions and grassroots organizations that pointed to Iceland’s low rankings among its Nordic neighbors when it comes to work-life balance.
Workers across a variety of public- and private-sector jobs participated in the trials. They included people working in day cares, assisted living facilities, hospitals, museums, police stations and Reykjavik government offices.
Participants reported back on how they reduced their hours. A common approach was to make meetings shorter and more focused. One workplace decided that meetings could be scheduled only before 3 p.m. Others replaced them altogether with email or other electronic correspondence.
Some workers started their shifts earlier or later, depending on demand. For example, at a day care, staff took turns leaving early as children went home. Offices with regular business hours shortened those hours, while some services were moved online.
Some coffee breaks were shortened or eliminated. The promise of a shorter workweek led people to organize their time and delegate tasks more efficiently, the study found.
Working fewer hours resulted in people feeling more energized and less stressed. They spent more time exercising and seeing friends, which then had a positive effect on their work, they said.
Many countries regulate the work week by law, such as stipulating minimum daily rest periods, annual holidays, and a maximum number of working hours per week. Working time may vary from person to person, often depending on economic conditions, location, culture, lifestyle choice, and the profitability of the individual’s livelihood.
For example, someone who is supporting children and paying a large mortgage might need to work more hours to meet basic costs of living than someone of the same earning power with lower housing costs. In developed countries like the United Kingdom, some workers are part-time because they are unable to find full-time work, but many choose reduced work hours to care for children or other family; some choose it simply to increase leisure time.
Standard working hours (or normal working hours) refers to the legislation to limit the working hours per day, per week, per month or per year. The employer pays higher rates for overtime hours as required in the law. Standard working hours of countries worldwide are around 40 to 44 hours per week (but not everywhere: from 35 hours per week in France to up to 112 hours per week in North Korean labor camps) and the additional overtime payments are around 25% to 50% above the normal hourly payments. Maximum working hours refers to the maximum working hours of an employee. The employee cannot work more than the level specified in the maximum working hours law.
Cohen, Yehudi (1974). Man in Adaptation: the cultural present. Aldine Transaction. pp. 94–95. ISBN0-202-01109-7. In all, the adults of the Dobe camp worked about two and a half days a week. Because the average working day was about six hours long, the fact emerges that !Kung Bushmen of Dobe, despite their harsh environment, devote from twelve to nineteen hours a week to getting food. Even the hardest working individual in the camp, a man named =oma who went out hunting on sixteen of the 28 days, spent a maximum of 32 hours a week in the food quest.
Ireland-based cryptocurrency exchange Bitsane disappeared without a trace last week, likely taking hundreds of thousands of users’ assets with it.
Account holders told Forbes that attempts to withdraw bitcoin, XRP and other cryptocurrencies began failing in May, with Bitsane’s support team writing in emails that withdrawals were “temporarily disabled due to technical reasons.” By June 17, Bitsane’s website was offline and its Twitter and Facebook accounts were deleted. Emails to multiple Bitsane accounts are now returned as undeliverable.
Victims of the scam are comparing notes in a group chat with more than 100 members on the messaging app Telegram and in a similar Facebook group. Most users in the groups claim to have lost up to $5,000, but Forbes spoke with one person in the U.S. who says he had $150,000 worth of XRP and bitcoin stored in Bitsane.
Bitsane’s disappearance is the latest cautionary tale for a cryptocurrency industry trying to shed its reputation as an unsafe asset class. Several exchanges like GateHub and Binance have been breached by hackers this year, but an exchange completely ceasing to exist with no notice or explanation is far more unusual.
Bitsane had 246,000 registered users according to its website as of May 30, the last time its homepage was saved on the Internet Archive’s Wayback Machine. Its daily trading volume was $7 million on March 31, according to CoinMarketCap.
“I was trying to transfer XRP out to bitcoin or cash or anything, and it kept saying ‘temporarily disabled.’ I knew right away there was some kind of problem,” says the user who claims to have lost $150,000 and asked to remain anonymous. “I went back in to try to look at those tickets to see if they were still pending, and you could no longer access Bitsane.”
At the height of the cryptocurrency craze in late 2017 and early 2018, Bitsane attracted casual investors because it allowed them to buy and sell Ripple’s XRP, which at the time was not listed on Coinbase, the most popular U.S. cryptocurrency exchange. CNBC published a story on January 2, 2018 with the headline “How to buy XRP, one of the hottest bitcoin competitors.” It explained how to buy bitcoin or ethereum on Coinbase, transfer it to Bitsane and then exchange it for XRP.
Three of the five Bitsane users Forbes spoke to found out about the exchange through the CNBC article. Ripple also listed Bitsane as an available exchange for XRP on its website until recently. A Ripple spokesperson did not respond to a request for comment.
Bitsane went live in November 2016 according to a press release, registering in Dublin as Bitsane LP under CEO Aidas Rupsys, and its chief technology officer was Dmitry Prudnikov. Prudnikov’s LinkedIn account has been deleted, and neither he nor Rupsys could be reached for comment.
A separate company, Bitsane Limited, was incorporated in England in August 2017 by Maksim Zmitrovich. He wanted to own the intellectual property rights to part of Bitsane’s code and use it for a trading platform his company, Azbit, was building. Zmitrovich says Bitsane’s developers insisted that their exchange’s name be on the new legal entity he was forming. But Azbit never ended up using any of the code since the partnership did not materialize, and Bitsane Limited did not provide any services to Bitsane LP.
On May 16, Bitsane Limited filed for dissolution because Zmitrovich wasn’t doing anything with it and the company’s registration was up for renewal. Some of the Bitsane exchange’s victims have found the public filing and suspected Zmitrovich as part of the scam, but he insists accusations against him are unfounded.
He says he hasn’t spoken to Prudnikov—who was in charge of negotiations with Azbit—in at least five months, and Prudnikov has not returned his calls since account holders searching for answers began contacting him. Azbit wrote a blog post about the Bitsane scam on June 13, explaining Bitsane Limited’s lack of involvement.
“I’m sick and tired of these accusations,” Zmitrovich says. “This company didn’t even have a bank account.”
The location of the money and whereabouts of any of Bitsane LP’s employees remain a mystery to the scam victims, who are unsure about what action to take next. Multiple account holders in the U.S. say they have filed complaints with the FBI, but all of them are concerned that their cash is gone for good.
I’m a reporter on Forbes’ wealth team covering billionaires and their fortunes. I was previously an assistant editor reporting on money and markets for Forbes, and I covered stocks as an intern at Bloomberg. I graduated from Duke University in 2019, where I majored in math and was the sports editor for our student newspaper, The Chronicle. Send news tips to email@example.com.
Binance In 2019 cryptocurrencies worth $40 million were stolen.
Josh Garza, who founded the cryptocurrency startups GAW Miners and ZenMiner in 2014, acknowledged in a plea agreement that the companies were part of a pyramid scheme, and pleaded guilty to wire fraud in 2015. The U.S. Securities and Exchange Commission separately brought a civil enforcement action against Garza, who was eventually ordered to pay a judgment of $9.1 million plus $700,000 in interest. The SEC’s complaint stated that Garza, through his companies, had fraudulently sold “investment contracts representing shares in the profits they claimed would be generated” from mining.
Following its shut-down, in 2018 a class action lawsuit for $771,000 was filed against the cryptocurrency platform known as BitConnect, including the platform promoting YouTube channels. Prior fraud warnings in regards to BitConnect, and cease-and-desist orders by the Texas State Securities Board cited the promise of massive monthly returns.
OneCoin was a massive world-wide multi-level marketingPonzi scheme promoted as (but not involving) a cryptocurrency, causing losses of $4 billion worldwide. Several people behind the scheme were arrested in 2018 and 2019.
Digital will separate the winners from the laggards in the hypercompetitive, post-pandemic business landscape, says Ben Pring, Managing Director of Cognizant’s Center for the Future of Work. We undertook a global, multi-industry study to understand how businesses are preparing for this future and here’s what we found.
COVID-19 changed digital from a nice-to-have adjunct to a must-have tool at the core of the enterprise. The pandemic forced businesses to reassess how they strategize and execute their digital ambitions in a world that has migrated online, possibly for good in many areas. Those that did not prioritize digital prior to the pandemic found that procrastination was no longer an option — the digital landscape is hypercompetitive.
The CFoW found that digital technologies are key to success in the coming years and uncovered six key steps that all organizations can take to more fruitfully apply to gear-up for the fast unfolding digital future:
Scrutinize everything because it’s going to change. From how and where employees work, to how customers are engaged, and which products and services are now viable as customer needs and behaviors evolve rapidly.
Make technology a partner in work. Innovations in AI, blockchain, natural language processing, IoT and 5G communications are ushering in decades of change ahead and will drive new levels of functionality and performance.
Build new workflows to reach new performance thresholds. The most predictable, rote and repetitive activities need to be handed off to software, while humans specialize in using judgment, creativity and language.
Make digital competency the prime competency for everyone. No matter what type of work needs to be done, it must have a digital component. Levels of digital literacy need to be built out even among non-technologists, including specialized skills.
Begin a skills renaissance. Digital skills such as big data specialists, process automation experts, security analysts, etc. aren’t easy to acquire. To overcome skills shortages, organizations will need to work harder to retain and engage workers.
Employees want jobs, but they also want meaning from jobs. How can businesses use intelligent algorithms to take increasing proportions of tasks off workers’ plates, allowing them to spend their time creating value? This search for meaning stretches beyond the individual tasks of the job to what the organization itself stands for.
Here are a few key findings from our research:
Redesigning the workplace is just the beginning: The virus will force enterprises to ask more strategic questions.
A mesh of machine emerges: While IoT is beginning to take hold, few respondents have piloted 5G projects. But over time , the mesh of machines created by IoT and 5G will serve as the foundation for news levels of functionality and possibility.
The 3As-AI , automation and analytics are the engines of digitization: To make the future of work happen, the 3As are emerging as a sophisticated and complex set of tools more deeply embedded in processes.
Ben Pring leads Cognizant’s Center for the Future of Work and is a coauthor of the books Monster: A Tough Love Letter On Taming The Machines That Rule Our Jobs, Lives, and Future, What To Do When Machines Do Everything and Code Halos: How the Digital Lives of People, Things, and Organizations Are Changing the Rules of Business. In 2018, he was a Bilderberg Meeting participant. He previously spent 15 years with Gartner as a senior industry analyst, researching and advising on areas such as cloud computing and global sourcing. He can be reached at Benjamin.Pring@cognizant.com
Digitalization is the adoption of digital technology to transform services or businesses, through replacing non-digital or manual processes with digital processes or replacing older digital technology with newer digital technology. Digital solutions may enable – in addition to efficiency via automation – new types of innovation and creativity, rather than simply enhancing and supporting traditional methods.
One aspect of digital transformation is the concept of ‘going paperless‘ or reaching a ‘digital business maturity’ affecting both individual businesses and whole segments of society, such as government,mass communications,art, health care, and science.
Digital transformation is not proceeding at the same pace everywhere. According to the McKinsey Global Institute‘s 2016 Industry Digitization Index, Europe is currently operating at 12% of its digital potential, while the United States is operating at 18%. Within Europe, Germany operates at 10% of its digital potential, while the United Kingdom is almost on par with the United States at 17%.
One example of digital transformation is the use of cloud computing. This reduces reliance on user-owned hardware and increases reliance on subscription-based cloud services. Some of these digital solutions enhance capabilities of traditional software products (e.g. Microsoft Office compared to Office 365) while others are entirely cloud based (e.g. Google Docs).
As the companies providing the services are guaranteed of regular (usually monthly) recurring revenue from subscriptions, they are able to finance ongoing development with reduced risk (historically most software companies derived the majority of their revenue from users upgrading, and had to invest upfront in developing sufficient new features and benefits to encourage users to upgrade), and delivering more frequent updates often using forms of agile software development internally. This subscription model also reduces software piracy, which is a major benefit to the vendor.
With the acceleration of digital transformation in business, most CTOs, CIOs, and even middle management or analysts are now asking, “What’s next with data?” and what ongoing role will technology play in both digital and data transformations. Other questions that keep these individuals up at night include:
How can people throughout all organizational levels be more empowered to use data and help others make better decisions?
What prevents people from more deeply exploring and using data?
In what ways can analytics tools and methods help more people use data in the daily routine of business—asking questions, exploring hypotheses, and testing ideas?
With this in mind, plus observations and discussions with many Tableau customers and partners, it seems that today’s circumstances, behaviors, and needs make it the right time for predictive data analytics to help businesses and their people solve problems effectively.
Current realities and barriers to scale smarter decision-making with AI
With growing, diverse data sets being collected, the analytics use cases to transform data into valuable insights are growing just as fast. Today, a wide range of tools and focused teams specialize in uncovering data insights to inform decision-making, but where organizations struggle is striking the right balance between activating highly technical data experts and business teams with deep domain experience.
Until now, using artificial intelligence (AI), machine learning (ML), and other statistical methods to solve business problems was mostly the domain of data scientists. Many organizations have small data science teams focused on specific, mission-critical, and highly scalable problems, but those teams usually have a long project list to handle.
At the same time though, there are a large number of business decisions that rely on experience, knowledge, and data—and that would greatly benefit from applying more advanced analysis techniques. People with domain knowledge and proximity to the business data could benefit greatly, if they had access to these techniques.
Instead, there’s currently a back-and-forth process of relying on data scientists and ML practitioners to build and deploy custom models—a cycle that lacks agility and the ability to iterate quickly. By the end, the data that the model was trained on could be stale and the process starts again. But organizations depend on business users to make key decisions daily that don’t rise to the priority level of their central data science team.
The opportunity to solve data science challenges
This is where there’s an opportunity to democratize data science capabilities, minimizing the trade-offs between extreme precision and control versus the time to insight—and the ability to take action on these insights. If we can give people tools or enhanced features to better apply predictive analytics techniques to business problems, data scientists can gain time back to focus on more complex problems. With this approach, business leaders can enable more teams to make data-driven decisions while continuing to keep up with the pace of business. Additional benefits gained from democratizing data science in this way include:
Reducing data exploration and prep work
Empowering analyst experts to deliver data science outputs at lower costs
Increasing the likelihood of producing successful models with more exploration of use cases by domain experts
Extending, automating, and accelerating analysis for business groups and domain experts
Reducing time and costs spent on deploying and integrating models
Promoting responsible use of data and AI with improved transparency and receiving guidance on how to minimize or address bias
Business scenarios that benefit from predictive analytics
There are several business scenarios where predictive capabilities can be immensely useful.
Sales and marketing departments can apply it to lead scoring, opportunity scoring, predicting time to close, and many other CRM-related cases. Manufacturers and retailers can use it to help with supply chain distribution and optimization, forecasting consumer demand, and exploring adding new products to their mix. Human resources can use it to assess the likelihood of candidates accepting an offer, and how they can adjust salary and benefits to meet a candidate’s values. And companies can use it to explore office space options and costs. These are just a few of the potential scenarios.
A solution to consider: Tableau Business Science
We are only at the beginning of exploring what predictive capabilities in the hands of people closely aligned with the business will unlock. AI and ML will continue to advance. More organizations, in a similar focus as Tableau, will also keep looking for techniques that can help people closest to the business see, understand, and use data in new ways to ask and answer questions, uncover insights, solve problems, and take action.
This spring Tableau introduced a new class of AI-powered analytics that gives predictive capabilities to people who are close to the business. In this next stage of expanded data exploration and use, we hope business leaders embrace data to help others make better decisions, and to provide transparent insight into the factors influencing those decisions.
When people can think with their data—when analysis is more about asking and answering questions than learning complex software or skills—that’s when human potential will be unleashed, leading to amazing outcomes. Learn more about Tableau Business Science, what this technology gives business teams, and the value it delivers to existing workflows.
Olivia Nix is a Senior Manager of Product Marketing at Tableau. She leads a team focused on the use of AI and ML in analytics and engagement, including how to use technology to enable more people in organizations to make data-driven decisions. Olivia has been at Tableau for four years where she has worked closely with development teams on new product launches. Prior to Tableau, Olivia worked as an analyst at the Pew Center on Global Climate Change (now C2ES) and Johnson Controls. She has her MBA from the UCLA Anderson School of Management.
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.
The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.
Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.
For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.
Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, “Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.