Are You Ready For Artificially Intelligent Enterprise Applications?

Timo Elliott

We’re now clearly in an exponential technology world. Each year brings the same amount of innovation as the previous several years — and the human brain finds it hard to imagine the consequences.

For perhaps the first time in history, technology is evolving faster than we expect it to. For example, experts estimated that it would take another decade before a computer would beat a world champion at the ancient and complex game of Go — yet AlphaGo triumphed earlier this year.

It’s not hard to find parallels between how new technologies are changing the world today, and how they will affect our business systems in the future.

For example, every Tesla car in hands-free driving mode gathers huge amounts of information about current conditions. This data isn’t just used to improving the driving of that particular car — the data is shared with all the other vehicles. Tesla has essentially created a massively parallel self-learning machine devoted to better driving.

What if enterprise software could do the same thing, using the experiences of every business user to improve everybody’s experience? These intelligent applications would get automatically better and smarter every time we used them. Eventually, we could even replace existing processes with automated best practices.

Incorporating artificial intelligence and machine learning into enterprise software opens the opportunity to simplify employees’ everyday lives and allows them to focus on higher-value tasks. Here are just a few examples of what is already possible:

Intelligent invoice-matching. Accounting systems can easily match invoices and payments if they have the same reference number, or the same amount. But what happens if a customer pays for two invoices in a single payment, tries to jam both reference numbers into the same field, and the amount doesn’t quite add up to the expected amount?

Somebody in a shared service center has to wade manually through all the different possibilities, trying to figure out what has happened, and then make the required changes in the accounting system.

invoice-matching

Machine learning and predictive algorithms can help. In the screenshot above, you can see that the chosen invoice has been matched to two different invoices, with a probability of 97% — even though there’s a small discrepancy in the total.

The shared service center can now update the system and decide what to do with the remaining outstanding balance. And over time the system can automate these types of decisions based on past history.

Smart recruiting. Artificial intelligence has the potential to have an enormous impact on HR. For example, recruiters often have to go through thousands of job applications to identify suitable candidates. Machine learning can use data about existing successful hires and their resumes to identify the best candidates for a given job description, or the best job for a given candidate.

Predictive sales. Sales people can use these new intelligent technologies to sift through all the data relevant to their pipeline, helping them focus on the deals that are most likely to close.

deal-finder

Social buzz management. Social media community managers are overwhelmed by tweets and Facebook posts. Machine intelligence can be used to automatically tag and cluster inbound messages and automatically suggest responses.

What does the future hold?

Looking further ahead, there’s a real possibility that we can completely rethink existing business applications from scratch. If we can more automatically answer a full range of typical business questions, much of traditional application workflow may not be required.

new-predictive-aplications

This, in turn, may have profound effects on existing jobs and roles — for example, experts are already predicting a future for Lights-Out Finance, with all basic finance processes being networked, automated, event-driven, and inherently compliant.

Want to know more? Check out these intelligent applications and consider enrolling in a free course on Enterprise Machine Learning in a Nutshell.

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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. 

What Does AI Bring To Marketing?

Timo Elliott

Artificial intelligence (AI) and machine learning (ML) is poised to revolutionize most aspects of operational marketing. It can help automate many of the choices and tradeoffs that currently must be made by overworked marketing employees, and enable things that were previously impossible.

AI and predictive analytics can be used to optimize the end-to-end customer journey and determine what causes customer churn (including unwanted marketing!).

The result is higher customer satisfaction with lower costs—and above all, more time and resources for more human aspects of marketing, such as brand strategy, storytelling and community building.

Here are just some of the opportunities:

  • AI-enabled recommendation engines: Increase cross-selling and upselling potential with recommendations to optimize shopping cart, conversion rates, and average sales order size
  • Audience segmentation: Discover audience segments through behavioral analysis and relationship analysis leveraging consumer and customer profiles
  • Best channel and contact time prediction: Optimize interactions and response behaviors by using the most appropriate contact time by contact by channel
  • Brand perception: Automatically analyze brand exposure in videos and images by leveraging advanced computer vision techniques to provide insights advertising ROI
  • Buying propensity: Score a contact based on their purchase history and others like them
  • Channel affinity: Provide an overview on response behavior per channel for a given target group
  • Content marketing/dynamic content: Automatically determine best content for customers by channel, time, and location based on their behavior, preferences, interests, and needs
  • Image recognition: Intelligently track customer interactions in a store, track attention for windows displays or outdoor ads, gauge sentiment and demographics, etc.
  • Interest affinity: Optimize customer engagements based on a deeper understanding of a customer’s context and intent
  • Lead scoring: Predictive lead scores for engagement, conversion, and qualification
  • Opportunity scoring: Predictive opportunity scores for engagement, conversion, and qualification

To find out more, read the “Worldwide AI in Enterprise Marketing Clouds 2017 Vendor Assessment” by IDC, see this page on innovation and machine learning, or watch the video below:

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. 

By 2020, Artificial Intelligence Will Touch Everything—But How?

Drew Bates

We are all equipped with the best minds in the known universe. Between ourselves and our predecessors, humans have created virtually everything we touch. We achieved this using a tool that represents the absolute pinnacle of intelligence: our brain.

This is not hyperbole. On an intelligence scale of zero to one, despite our individual variety, we all fit within the microscopically small space occupied by the right edge of the ‘e.’ We’re up there alone, magnitudes away from the also-rans with whom we share this planet.

Now we’re taking a concerted stab at creating computer programs that are as intelligent as we are. Artificial Intelligence (AI) is—for lack of a better definition—where software acts human-like. Assuming we’re doing something right (and forgive our big-headedness), this sounds marvelous: The power of human intelligence in a predictable, non-chemically burdened chassis that is always powered-on and free from other responsibilities or predilections.

Follow this idea onwards through a few revolutions and the concept has perhaps too much potential. What if the intelligence scale goes from zero to 100, and we are simply not capable of handling the outcome?

Thankfully, we’re at the comforting level where AI means sitting back and relaxing while stuff gets done better, more easily, and with less effort.

What we recognize as AI is usually a shortcut through processes to reach a conclusion more quickly and productively. By 2020, more or less everything will be touched by AI. Truth is, this comes down to a handful of activities:

Automation

There are not suddenly new things to do. AI just helps us to transition between discrete actions.

It means we can couple workflows together faster like ordering more toilet roll when it runs low, or translating a Chinese message into English. Since computers began, we’ve been used to them handling our actions based on our rules (every app has a settings page). Dialling this up into AI involves removing the rules and training a computer algorithm to link up the actions.

Predictions

There are not suddenly more variables in the world to work with. AI simply helps us process all the variables in a very programmatic way, with a transparent level of confidence.

It may be controversial, but guessing what will happen in the future is most accurate when performed within the scientific realm of statistics. Since day one, computers have excelled in math, so we have handed over responsibility for mathematical tasks almost entirely to them. Calculating statistical significance is the building block of machine learning, whether that means assessing weather patterns, detecting diseases, or playing chess. We are on a continuous journey of increased data and processing power, which make computers better.

Decisions

We have not been making all the wrong decisions. AI just presents us with available options based on more data and with less instinct than we are accustomed to.

Assessing situations without perfect information is not just a human trait but a daily necessity and something we may not realize is constantly happening. From negotiations to ranking new business opportunities, we make informed decisions towards our desired outcomes. Moving the needle upward involves having more relevant details from more relevant sources.

Interactions

There are not undiscovered ethereal ways to interact with computers. AI just opens new interface methods with less effort.

Since the mouse and keyboard, we have been on a journey to use computers with fewer hurdles. Learning to type is something we needed to achieve for the exclusive task of programming computers and digitally communicating with each other. Now, with code to process human sentences and interpret input from cameras and sensors, we can make this interaction as natural as being with each other.

The significance of AI

Yet given these down-to-earth realities, AI is still a huge deal. We are now in a world where things get done faster, more easily, with more accuracy, and based on better knowledge. Statista made a very poignant chart of how smartphone users benefit from artificial intelligence. In short, everything we do is touched in some way by AI.

So if AI is becoming more and more ingrained into our lives, when do we get close to the machines taking over?

Impact to humans

Gartner calculated that by 2020, AI will create 2.3 million jobs while eliminating 1.8 million, but this is not destined to last. As we’ve progressed through the last three industrial revolutions on the way to the digital revolution, our working lives have fundamentally changed, and they will again. Imagine a world where work-life balance means two days of work and five days of life. It’s quite likely.

The alarm bells will ring when we morally question turning off a simulation because it has become too intelligent to be considered just code. This is still very firmly in the realm of science fiction. If the evolution of computing power is due to reach Zetta-scale (considered the minimum for human brain simulation) by 2030, at least we will have the hardware capability to make it happen. Right now, we’re firmly at the lizard/mouse stage.

In their AI Open Letter, many of the world’s greatest thinkers, including Stephen Hawking, Mustafa Suleyman, Steve Wozniak, and Elon Musk, pledged to ring the alarm before the power of AI is taken out of our grasp.

Where you stand is up to you. I’ll leave it with a quote from one of the world’s finest science fiction writers, the late Iain Banks: “We provide the machines with an end, and they provide us with the means.”

This story also appears on the SAP Community.

For more on future tech, see 4 Ways The Butterfly Effect Will Shape Emerging Technologies In 2018.

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Drew Bates

About Drew Bates

Drew Bates is responsible for SMB innovation. He writes from SAP Labs in Shanghai China on the topic of lessons learned whilst being on the forefront of modern technology.

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.