Analytics Innovation, Disruption, And Transformation

Timo Elliott

I’ll be presenting a session on Analytics Innovation, Disruption, and Transformation at the BI2017 conference in Orlando next week. This post is an introduction to some of the themes I’ll be covering.

Analytics has been around for a long time, and it’s undergone some major changes. But one thing never seems to change: It’s still the hottest technology space in the industry.

For example, this is Gartner’s list of the top strategic trends for 2017 – and you can see that “intelligence” is at the top of the list, including machine learning, intelligent apps, and intelligent things.

 

BI & Analytics is yet again the top priority on Gartner’s long-running annual CIO survey, as it has been for ten out of the last twelve years.

 

Note that this is actually a very unusual situation. Normally, a new technology becomes a priority, companies invest money in it, and then it fades back down the list now that the new “problem” has been solved.

Consider mobile, for example: When smartphones and tablets came out, it suddenly became a high priority to support them, but now that the technology is in place, it has faded back down the list.

So why has analytics stayed so high for so long? Why haven’t we “fixed it” yet?

One reason analytics remains a hot topic is that the amount and variety of data available has skyrocketed, constantly creating new analytic challenges. But even more importantly, analytics has become an essential part of digital transformation.

For the last few decades, we’ve typically thought of business intelligence as a byproduct of our operational processes. In other words, we manufacture products, ship them around the world, and sell them to customers. Each of these processes generates a lot of data, and we use that data to keep track of operations and create more optimized processes in the future.

 

This remains as true and important today as it’s ever been in the past. But now there’s another dimension coming into play. Organizations are increasingly realizing that digital transformation doesn’t just require new processes – it requires a new approach to implementing processes. They have to be more agile, more intelligent, and more responsive to change.

 

These new processes flip the traditional equation on its head. New digital processes are created on the fly by analytics.

The typical customer journey is a great example. In the old days, purchasing a product was a fairly linear process, and companies characterized as a “sales funnel.” But now it’s more like a “write your own adventure” book – where there are many different possible interaction paths. At each point in the process, the customer gets to choose the next chapter.

Analytics is used to help guide the customer towards the “right” choice at each point, indicating what other products they may be interested in, or offering discounts to encourage immediate purchase.

In other words, every “customer process” is unique, with analytics doing all the work, creating thousands or millions of personalized “processes” based on the needs of each individual.

Because these new processes are analytics-powered rather than hard-coded, they can be much more agile and responsive to change – indeed, new machine learning approaches mean that they can even update and optimize themselves.

Effectively creating and managing these kinds of flexible, on-the-fly processes is THE big new opportunity in digital business. But it also means that analytics has to have a more process-oriented approach, not just treated as a series of one-off decisions — and this is an area where traditional BI leaders have an advantage over the “islands of innovation” approach of tools that focus only on data discovery and visualization.

Gartner believes that information and analytics will be used to reinvent, digitalize, or eliminate 80% of today’s business processes.

Analytics is no longer just an afterthought to the “real business” – it’s the heart of the new business models of the future.

Analytics also enables “live businesses.” A live business is one that anticipates, simulates, and innovates new business opportunities, and that looks to create the future rather than just reporting on the past.

Here’s the analogy I like to use: Imagine this expensive crystal vase has been knocked over and is plummeting to the floor. That’s the equivalent of something going wrong in your business – an overdue production, a late delivery, or an unhappy customer.

Traditionally, businesses would have been stuck with analyzing the puddle and shards of glass on the floor after the vase had broken, in order to figure out what went wrong, who to blame, and how to avoid it next time.

But what if you could actually catch the vase before it hit the ground—in other words, if you knew about the business problem before you lost money or ruined customer satisfaction? That’s live business. To achieve this, you need a seamless, real-time link between operations and analytics and this plays to the strengths of new in-memory operational+analytics solutions such as the SAP HANA platform.

But while analytics is a hot topic, it doesn’t mean that it’s without problems. Various reports indicate that the reported success rate of BI deployments has stalled. For example, Howard Dresner found that BI initiatives described as successful dropped from 41% to 35% in 2015.

It’s worth noting that it’s not completely clear what BI “success” really means — it’s largely a subjective measure because few organizations actually define what success would look like before they start on a projects. But I believe user satisfaction is falling because business expectations are rising even faster than BI technology improvements.

This has real consequences. In particular, some organizations continue to implement only “old-style,” centralized business intelligence. This is increasingly is out of phase with the needs of today’s more analytics-savvy business users. Gartner calls these people “BI-nosaurs” and warns that the comet that might wipe them out is coming .

Here are some of the typical complaints of today’s analytics users.

First, users find analytics too slow, with almost a third having to wait days or weeks for a BI request. People would like to access information themselves without needing IT, but a third said that they find their enterprise BI too complex, too complicated, and too cumbersome to use. Finally, almost half the data that business people want to access is now from outside the organization, and is therefore unlikely to be in the corporate system in the first place.

For all these reasons, the penetration rate of analytics remains low in organizations, with many reporting that fewer than 10% of employees using BI – although again, it’s not always clear what “using” means – often the data is used indirectly; for example, cut-and-pasted into a spreadsheet or presentation. But there’s obviously a huge opportunity to get more data to more people, both inside and outside the organization.

From the point of view of business users, traditional analytics organizations look like the taxi companies that have been displaced by more flexible car-sharing applications like Uber and Lyft. Many people found taxis too expensive and annoyingly hard to find when they wanted one – but there was no alternative, so they put up with it. Now there are lots of lightweight analytics products available, and business departments increasingly have their own IT budget to spend.

The result it that the older ways of doing things are being disrupted, and just like the taxi companies, traditional analytics organizations have to adapt to the new tools and new ways of working in order to compete effectively.

So how should analytics organizations react to these trends? I’ll be following up with future posts on areas such as supporting “modern BI,” the new Big Data architectures, the adoption of predictive and machine learning, and changes to how companies are organizing for BI.

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Timo Elliott

About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in publications such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics. 

Three Reasons Discrete Manufacturers Must Integrate Digital And Physical Products

David Parrish

Discrete manufacturers in automotive, aerospace and defense, high tech, and industrial machinery and components are facing unprecedented pressures on their ability to innovate, engage with customers and consumers, and maximize return on their assets. By 2018, nearly one-third of discrete manufacturing leaders will be disrupted by competitors that are digitally enabled, reports IDC. In the age of digital disruption and transformation, discrete manufacturers must rethink traditional business models to capitalize on new, digital opportunities. One such opportunity is the sale of digital products.

Digital products offer many benefits over physical products, including frictionless buying, immediate delivery, and no shipping or supply chain management costs. But digital products can be difficult to sell on their own. To address this challenge, companies are pairing digital products with physical ones. For discrete manufacturers, this pairing offers new business models and revenue-stream opportunities.

Valuing digital products: Using physical products to drive digital sales

What is the value of a digital product? Consumers in the B2C world have historically been slow to jump at the purchase of digital products. As Fast Company reports, it takes a companion physical product to give the digital product value. For example, consider the case of Apple’s iPod and digital music downloads. In the age of Napster and free MP3s, digital music downloads were a slow seller. This changed after Apple introduced its iPod in 2001, creating a new physical product to house these digital downloads. More than 5 billion songs were sold through Apple’s iTunes store by 2008.

Learning from Apple, discrete manufacturers can adopt a similar approach by integrating their physical and digital offerings. Digital offerings, such as remote upgrade service and preventive maintenance contracts, are a natural add-on to physical products. IDC estimates that by 2018, 60% of large manufacturers will bring in new revenue from information-based products and services with embedded intelligence driving the highest profitability levels.

Three applications for digital-physical product integration

For discrete manufacturers, integrating digital and physical products offer three key benefits:

  1. Increased aftermarket value. Selling remote monitoring and digital services is perhaps the most obvious application for digital and physical product integration. Offering upgrades, continuous service, and preventive maintenance via remote monitoring is an important new revenue stream for discrete manufacturers. For example, remote monitoring can dramatically extend the shelf life of industrial machinery used in the food and beverage industries, high-tech manufacturing and automotive manufacturing. Typically, an industrial machine has a shelf life of 20+ years. But the rapid pace of technological change means machines constantly need to be retrofitted. Conditioning-monitoring sensors combined with the Internet of Things (IoT), cloud technology, and analytics would enable discrete manufacturers to offer ongoing digital service plans.
  1. Data monetization. IDC estimates that less than 10% of data is effectively used. Discrete manufacturers must treat data as a digital asset and use this data to improve user experiences, provide insight, influence decisions, and set directions. In the automotive space, discrete manufacturers can leverage usage and engagement information to effectively send content, such as software upgrades and infotainment. Like the Apple iPod/digital download model, auto manufacturers could use the physical product (the car entertainment system) to sell the digital product (the infotainment) to drivers. Automobile manufacturers can use analytic data to better understand driving patterns and preferences, location usage, and demographics. Analyzing this data will allow manufacturers to better target their digital infotainment offerings.
  1. Faster design-to-market cycles. Embedding sensors in industrial machines will generate a wealth of digital performance data that is useful not only for predictive maintenance but also for streamlining future production. Industrial machines are incredibly complex. Ideally, these machines are built following a model-based systems engineering approach that allows designs to be reused for a variety of customers. Integrating sensors into these machines will produce a stream of data that discrete manufacturers can use for future production guidelines. This includes using the data to configure new customer orders. This approach accelerates design-to-market cycles and increases customer satisfaction.

For discrete manufacturers to capitalize on new business opportunities, they need a strategic partner to support digital and physical product integration. Manufacturers need a platform that enables the seamless integration of industrial IoT with advanced analytics process to support product development.

Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value by reading Accelerating Digital Transformation in Industrial Machinery and Components. Explore how to bring Industry 4.0 insights into your business today by reading Industry 4.0: What’s Next?

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David Parrish

About David Parrish

David Parrish is the senior global director of Industrial Machinery & Components Solutions Marketing for SAP. Before joining SAP, he held various product and industry marketing positions with J.D. Edwards, PeopleSoft, and QAD going back to 1999.

CEOs Say They’re In Charge Of Analytics (Others Aren’t So Sure!)

Timo Elliott

Thanks to a tweet from Carsten Linz, the head of the SAP Center for Digital Leadership, I stumbled across some fascinating research from McKinsey on leadership in analytics.

What really caught my eye is that CEOs believe they are in charge of the analytics agenda in their organization:

But other executives aren’t so sure:

“Thirty-eight percent of CEOs say they lead their companies’ analytics agendas, but only 9 percent of all other C-suite executives agree. These respondents are much more likely to cite chief information officers or business-unit heads as leaders of the analytics agenda.”

What’s also fascinating about this is the reasons that CEOs give for not investing in as much analytics as their competitors:

Given they think they’re in charge of the initiatives, CEOs naturally don’t think senior leadership is a problem. But it’s by far the highest reason given by other senior leaders. What’s going on here?

CEOs say that the biggest problems are “uncertainty over which actions should be taken” and “lack of financial resources.” The latter is a fancy way of saying that CEOs don’t believe that analytics has a high enough ROI.

Creating formal ROI cases for analytics has always been a problem; it’s hard to do a traditional financial analysis of the benefits when you “don’t know what you don’t know.” But many different studies over the years have consistently shown that once the business has new insights, they optimize the organization new ways. Forrester, for example, has shown that analyzing real-life cases before and after implementation produces very high ROI for analytics projects.

And without a clear way to determine the relative benefits of different analytics projects, it’s easy to see why CEOs think that the second biggest problem is uncertainty about how to proceed.

So what’s lacking, and what should organizations do about this?

I think the other senior executives have it right: The problem is indeed senior leadership. It takes a leap of faith to believe that better analytics will indeed to better business outcomes, as it always has done in the past (counter-examples, anyone?)—and that’s a job that only the CEO is fully qualified to do.

The bottom line is that formal ROI cases work well for many types of IT projects—but not for analytics. In an era where business is moving faster than ever, the benefits of investing in better visibility are clear. Today’s CEOs need to lead the rest of the organization away from the ROI of ignorance.

For more great McKinsey research, check out their latest roundup of analytics-related articles: Analytics Comes of Age.

This article originally appeared on Digital Business & Business Analytics.

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Timo Elliott

About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in publications such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics. 

More Than Noise: Digital Trends That Are Bigger Than You Think

By Maurizio Cattaneo, David Delaney, Volker Hildebrand, and Neal Ungerleider

In the tech world in 2017, several trends emerged as signals amid the noise, signifying much larger changes to come.

As we noted in last year’s More Than Noise list, things are changing—and the changes are occurring in ways that don’t necessarily fit into the prevailing narrative.

While many of 2017’s signals have a dark tint to them, perhaps reflecting the times we live in, we have sought out some rays of light to illuminate the way forward. The following signals differ considerably, but understanding them can help guide businesses in the right direction for 2018 and beyond.

When a team of psychologists, linguists, and software engineers created Woebot, an AI chatbot that helps people learn cognitive behavioral therapy techniques for managing mental health issues like anxiety and depression, they did something unusual, at least when it comes to chatbots: they submitted it for peer review.

Stanford University researchers recruited a sample group of 70 college-age participants on social media to take part in a randomized control study of Woebot. The researchers found that their creation was useful for improving anxiety and depression symptoms. A study of the user interaction with the bot was submitted for peer review and published in the Journal of Medical Internet Research Mental Health in June 2017.

While Woebot may not revolutionize the field of psychology, it could change the way we view AI development. Well-known figures such as Elon Musk and Bill Gates have expressed concerns that artificial intelligence is essentially ungovernable. Peer review, such as with the Stanford study, is one way to approach this challenge and figure out how to properly evaluate and find a place for these software programs.

The healthcare community could be onto something. We’ve already seen instances where AI chatbots have spun out of control, such as when internet trolls trained Microsoft’s Tay to become a hate-spewing misanthrope. Bots are only as good as their design; making sure they stay on message and don’t act in unexpected ways is crucial.

This is especially true in healthcare. When chatbots are offering therapeutic services, they must be properly designed, vetted, and tested to maintain patient safety.

It may be prudent to apply the same level of caution to a business setting. By treating chatbots as if they’re akin to medicine or drugs, we have a model for thorough vetting that, while not perfect, is generally effective and time tested.

It may seem like overkill to think of chatbots that manage pizza orders or help resolve parking tickets as potential health threats. But it’s already clear that AI can have unintended side effects that could extend far beyond Tay’s loathsome behavior.

For example, in July, Facebook shut down an experiment where it challenged two AIs to negotiate with each other over a trade. When the experiment began, the two chatbots quickly went rogue, developing linguistic shortcuts to reduce negotiating time and leaving their creators unable to understand what they were saying.

Do we want AIs interacting in a secret language because designers didn’t fully understand what they were designing?

The implications are chilling. Do we want AIs interacting in a secret language because designers didn’t fully understand what they were designing?

In this context, the healthcare community’s conservative approach doesn’t seem so farfetched. Woebot could ultimately become an example of the kind of oversight that’s needed for all AIs.

Meanwhile, it’s clear that chatbots have great potential in healthcare—not just for treating mental health issues but for helping patients understand symptoms, build treatment regimens, and more. They could also help unclog barriers to healthcare, which is plagued worldwide by high prices, long wait times, and other challenges. While they are not a substitute for actual humans, chatbots can be used by anyone with a computer or smartphone, 24 hours a day, seven days a week, regardless of financial status.

Finding the right governance for AI development won’t happen overnight. But peer review, extensive internal quality analysis, and other processes will go a long way to ensuring bots function as expected. Otherwise, companies and their customers could pay a big price.

Elon Musk is an expert at dominating the news cycle with his sci-fi premonitions about space travel and high-speed hyperloops. However, he captured media attention in Australia in April 2017 for something much more down to earth: how to deal with blackouts and power outages.

In 2016, a massive blackout hit the state of South Australia following a storm. Although power was restored quickly in Adelaide, the capital, people in the wide stretches of arid desert that surround it spent days waiting for the power to return. That hit South Australia’s wine and livestock industries especially hard.

South Australia’s electrical grid currently gets more than half of its energy from wind and solar, with coal and gas plants acting as backups for when the sun hides or the wind doesn’t blow, according to ABC News Australia. But this network is vulnerable to sudden loss of generation—which is exactly what happened in the storm that caused the 2016 blackout, when tornadoes ripped through some key transmission lines. Getting the system back on stable footing has been an issue ever since.

Displaying his usual talent for showmanship, Musk stepped in and promised to build the world’s largest battery to store backup energy for the network—and he pledged to complete it within 100 days of signing the contract or the battery would be free. Pen met paper with South Australia and French utility Neoen in September. As of press time in November, construction was underway.

For South Australia, the Tesla deal offers an easy and secure way to store renewable energy. Tesla’s 129 MWh battery will be the most powerful battery system in the world by 60% once completed, according to Gizmodo. The battery, which is stationed at a wind farm, will cover temporary drops in wind power and kick in to help conventional gas and coal plants balance generation with demand across the network. South Australian citizens and politicians largely support the project, which Tesla claims will be able to power 30,000 homes.

Until Musk made his bold promise, batteries did not figure much in renewable energy networks, mostly because they just aren’t that good. They have limited charges, are difficult to build, and are difficult to manage. Utilities also worry about relying on the same lithium-ion battery technology as cellphone makers like Samsung, whose Galaxy Note 7 had to be recalled in 2016 after some defective batteries burst into flames, according to CNET.

However, when made right, the batteries are safe. It’s just that they’ve traditionally been too expensive for large-scale uses such as renewable power storage. But battery innovations such as Tesla’s could radically change how we power the economy. According to a study that appeared this year in Nature, the continued drop in the cost of battery storage has made renewable energy price-competitive with traditional fossil fuels.

This is a massive shift. Or, as David Roberts of news site Vox puts it, “Batteries are soon going to disrupt power markets at all scales.” Furthermore, if the cost of batteries continues to drop, supply chains could experience radical energy cost savings. This could disrupt energy utilities, manufacturing, transportation, and construction, to name just a few, and create many opportunities while changing established business models. (For more on how renewable energy will affect business, read the feature “Tick Tock” in this issue.)

Battery research and development has become big business. Thanks to electric cars and powerful smartphones, there has been incredible pressure to make more powerful batteries that last longer between charges.

The proof of this is in the R&D funding pudding. A Brookings Institution report notes that both the Chinese and U.S. governments offer generous subsidies for lithium-ion battery advancement. Automakers such as Daimler and BMW have established divisions marketing residential and commercial energy storage products. Boeing, Airbus, Rolls-Royce, and General Electric are all experimenting with various electric propulsion systems for aircraft—which means that hybrid airplanes are also a possibility.

Meanwhile, governments around the world are accelerating battery research investment by banning internal combustion vehicles. Britain, France, India, and Norway are seeking to go all electric as early as 2025 and by 2040 at the latest.

In the meantime, expect huge investment and new battery innovation from interested parties across industries that all share a stake in the outcome. This past September, for example, Volkswagen announced a €50 billion research investment in batteries to help bring 300 electric vehicle models to market by 2030.

At first, it sounds like a narrative device from a science fiction novel or a particularly bad urban legend.

Powerful cameras in several Chinese cities capture photographs of jaywalkers as they cross the street and, several minutes later, display their photograph, name, and home address on a large screen posted at the intersection. Several days later, a summons appears in the offender’s mailbox demanding payment of a fine or fulfillment of community service.

As Orwellian as it seems, this technology is very real for residents of Jinan and several other Chinese cities. According to a Xinhua interview with Li Yong of the Jinan traffic police, “Since the new technology has been adopted, the cases of jaywalking have been reduced from 200 to 20 each day at the major intersection of Jingshi and Shungeng roads.”

The sophisticated cameras and facial recognition systems already used in China—and their near–real-time public shaming—are an example of how machine learning, mobile phone surveillance, and internet activity tracking are being used to censor and control populations. Most worryingly, the prospect of real-time surveillance makes running surveillance states such as the former East Germany and current North Korea much more financially efficient.

According to a 2015 discussion paper by the Institute for the Study of Labor, a German research center, by the 1980s almost 0.5% of the East German population was directly employed by the Stasi, the country’s state security service and secret police—1 for every 166 citizens. An additional 1.1% of the population (1 for every 66 citizens) were working as unofficial informers, which represented a massive economic drain. Automated, real-time, algorithm-driven monitoring could potentially drive the cost of controlling the population down substantially in police states—and elsewhere.

We could see a radical new era of censorship that is much more manipulative than anything that has come before. Previously, dissidents were identified when investigators manually combed through photos, read writings, or listened in on phone calls. Real-time algorithmic monitoring means that acts of perceived defiance can be identified and deleted in the moment and their perpetrators marked for swift judgment before they can make an impression on others.

Businesses need to be aware of the wider trend toward real-time, automated censorship and how it might be used in both commercial and governmental settings. These tools can easily be used in countries with unstable political dynamics and could become a real concern for businesses that operate across borders. Businesses must learn to educate and protect employees when technology can censor and punish in real time.

Indeed, the technologies used for this kind of repression could be easily adapted from those that have already been developed for businesses. For instance, both Facebook and Google use near–real-time facial identification algorithms that automatically identify people in images uploaded by users—which helps the companies build out their social graphs and target users with profitable advertisements. Automated algorithms also flag Facebook posts that potentially violate the company’s terms of service.

China is already using these technologies to control its own people in ways that are largely hidden to outsiders.

According to a report by the University of Toronto’s Citizen Lab, the popular Chinese social network WeChat operates under a policy its authors call “One App, Two Systems.” Users with Chinese phone numbers are subjected to dynamic keyword censorship that changes depending on current events and whether a user is in a private chat or in a group. Depending on the political winds, users are blocked from accessing a range of websites that report critically on China through WeChat’s internal browser. Non-Chinese users, however, are not subject to any of these restrictions.

The censorship is also designed to be invisible. Messages are blocked without any user notification, and China has intermittently blocked WhatsApp and other foreign social networks. As a result, Chinese users are steered toward national social networks, which are more compliant with government pressure.

China’s policies play into a larger global trend: the nationalization of the internet. China, Russia, the European Union, and the United States have all adopted different approaches to censorship, user privacy, and surveillance. Although there are social networks such as WeChat or Russia’s VKontakte that are popular in primarily one country, nationalizing the internet challenges users of multinational services such as Facebook and YouTube. These different approaches, which impact everything from data safe harbor laws to legal consequences for posting inflammatory material, have implications for businesses working in multiple countries, as well.

For instance, Twitter is legally obligated to hide Nazi and neo-fascist imagery and some tweets in Germany and France—but not elsewhere. YouTube was officially banned in Turkey for two years because of videos a Turkish court deemed “insulting to the memory of Mustafa Kemal Atatürk,” father of modern Turkey. In Russia, Google must keep Russian users’ personal data on servers located inside Russia to comply with government policy.

While China is a pioneer in the field of instant censorship, tech companies in the United States are matching China’s progress, which could potentially have a chilling effect on democracy. In 2016, Apple applied for a patent on technology that censors audio streams in real time—automating the previously manual process of censoring curse words in streaming audio.

In March, after U.S. President Donald Trump told Fox News, “I think maybe I wouldn’t be [president] if it wasn’t for Twitter,” Twitter founder Evan “Ev” Williams did something highly unusual for the creator of a massive social network.

He apologized.

Speaking with David Streitfeld of The New York Times, Williams said, “It’s a very bad thing, Twitter’s role in that. If it’s true that he wouldn’t be president if it weren’t for Twitter, then yeah, I’m sorry.”

Entrepreneurs tend to be very proud of their innovations. Williams, however, offers a far more ambivalent response to his creation’s success. Much of the 2016 presidential election’s rancor was fueled by Twitter, and the instant gratification of Twitter attracts trolls, bullies, and bigots just as easily as it attracts politicians, celebrities, comedians, and sports fans.

Services such as Twitter, Facebook, YouTube, and Instagram are designed through a mix of look and feel, algorithmic wizardry, and psychological techniques to hang on to users for as long as possible—which helps the services sell more advertisements and make more money. Toxic political discourse and online harassment are unintended side effects of the economic-driven urge to keep users engaged no matter what.

Keeping users’ eyeballs on their screens requires endless hours of multivariate testing, user research, and algorithm refinement. For instance, Casey Newton of tech publication The Verge notes that Google Brain, Google’s AI division, plays a key part in generating YouTube’s video recommendations.

According to Jim McFadden, the technical lead for YouTube recommendations, “Before, if I watch this video from a comedian, our recommendations were pretty good at saying, here’s another one just like it,” he told Newton. “But the Google Brain model figures out other comedians who are similar but not exactly the same—even more adjacent relationships. It’s able to see patterns that are less obvious.”

A never-ending flow of content that is interesting without being repetitive is harder to resist. With users glued to online services, addiction and other behavioral problems occur to an unhealthy degree. According to a 2016 poll by nonprofit research company Common Sense Media, 50% of American teenagers believe they are addicted to their smartphones.

This pattern is extending into the workplace. Seventy-five percent of companies told research company Harris Poll in 2016 that two or more hours a day are lost in productivity because employees are distracted. The number one reason? Cellphones and texting, according to 55% of those companies surveyed. Another 41% pointed to the internet.

Tristan Harris, a former design ethicist at Google, argues that many product designers for online services try to exploit psychological vulnerabilities in a bid to keep users engaged for longer periods. Harris refers to an iPhone as “a slot machine in my pocket” and argues that user interface (UI) and user experience (UX) designers need to adopt something akin to a Hippocratic Oath to stop exploiting users’ psychological vulnerabilities.

In fact, there is an entire school of study devoted to “dark UX”—small design tweaks to increase profits. These can be as innocuous as a “Buy Now” button in a visually pleasing color or as controversial as when Facebook tweaked its algorithm in 2012 to show a randomly selected group of almost 700,000 users (who had not given their permission) newsfeeds that skewed more positive to some users and more negative to others to gauge the impact on their respective emotional states, according to an article in Wired.

As computers, smartphones, and televisions come ever closer to convergence, these issues matter increasingly to businesses. Some of the universal side effects of addiction are lost productivity at work and poor health. Businesses should offer training and help for employees who can’t stop checking their smartphones.

Mindfulness-centered mobile apps such as Headspace, Calm, and Forest offer one way to break the habit. Users can also choose to break internet addiction by going for a walk, turning their computers off, or using tools like StayFocusd or Freedom to block addictive websites or apps.

Most importantly, companies in the business of creating tech products need to design software and hardware that discourages addictive behavior. This means avoiding bad designs that emphasize engagement metrics over human health. A world of advertising preroll showing up on smart refrigerator touchscreens at 2 a.m. benefits no one.

According to a 2014 study in Cyberpsychology, Behavior and Social Networking, approximately 6% of the world’s population suffers from internet addiction to one degree or another. As more users in emerging economies gain access to cheap data, smartphones, and laptops, that percentage will only increase. For businesses, getting a head start on stopping internet addiction will make employees happier and more productive. D!


About the Authors

Maurizio Cattaneo is Director, Delivery Execution, Energy, and Natural Resources, at SAP.

David Delaney is Global Vice President and Chief Medical Officer, SAP Health.

Volker Hildebrand is Global Vice President for SAP Hybris solutions.

Neal Ungerleider is a Los Angeles-based technology journalist and consultant.


Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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The “Purpose” Of Data

Timo Elliott

I’ve always been passionate about the ability of data and analytics to transform the world.

It has always seemed to me to be the closest thing we have to modern-day magic, with its ability to conjure up benefits from thin air. Over the last quarter century, I’ve had the honor of working with thousands of “wizards” in organizations around the world, turning information into value in every aspect of our daily lives.

The projects have been as simple as Disney using real-time analytics to move staff from one store to another to keep lines to a minimum: shorter lines led to bigger profits (you’re more likely to buy that Winnie-the-Pooh bear if there’s only one person ahead of you), but also higher customer satisfaction and happier children.

Or they’ve been as complex as the Port of Hamburg: constrained by its urban location, it couldn’t expand to meet the growing volume of traffic. But better use of information meant it was able to dramatically increase throughput – while improving the life of city residents with reduced pollution (less truck idling) and fewer traffic jams (smart lighting that automatically adapts to bridge closures).

I’ve seen analytics used to figure out why cheese was curdling in Wisconsin; count the number of bubbles in Champagne; keep track of excessive fouls in Swiss soccer, track bear sightings in Canada; avoid flooding in Argentina; detect chewing-gum-blocked metro machines in Brussels; uncover networks of tax fraud in Australia; stop trains from being stranded in the middle of the Tuscan countryside; find air travelers exposed to radioactive substances; help abused pets find new homes; find the best people to respond to hurricanes and other disasters; and much, much more.

The reality is that there’s a lot of inefficiency in the world. Most of the time it’s invisible, or we take it for granted. But analytics can help us shine a light on what’s going on, expose the problems, and show us what we can do better – in almost every area of human endeavor.

Data is a powerful weapon. Analytics isn’t just an opportunity to reduce costs and increase profits – it’s an opportunity to make the world a better place.

So to paraphrase a famous world leader, next time you embark on a new project:

“Ask not what you can do with your data, ask what your data can do for the world.”

What are your favorite “magical” examples, where analytics helped create win/win/win situations?

Download our free eBook for more insight on How the Port of Hamburg Doubled Capacity with Digitization.

This article originally appeared on Digital Business & Business Analytics.

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Timo Elliott

About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in publications such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics.