Go Hybrid To Maximize The Value Of Your Data

Steve McHugh

Here’s the thing about digital transformation: It’s all about data and what you do with it.

Yes, much of the conversation is about cool emerging technologies such as the Internet of Things (IoT), machine learning, and blockchain. But in the end, all of these technologies are simply ways of making better use of data.

An IoT deployment uses sensors to transmit machine data to home base for analysis. Machine-learning algorithms use data inputs to help improve processes. And a blockchain, by definition, is a kind of secure, transparent, and distributed database of transactions.

So when it comes to digital transformation, the starting point is to think about ways in which to put your data to better use – and into the hands of those who need it to drive business performance.

From data to analysis

The true value of your data comes from analysis – the kind of insight that drives innovation and helps you deliver what your business needs. This is why companies invest so heavily in business intelligence and data analytics solutions.

The challenge is for organizations to increase the penetration of analytics within the organization and empower more business users to make better, fact-based decisions. The smarter, more timely decisions you can make, the stronger your business will grow.

>Research by Oxford Economics from 2016 bears out this thinking. Oxford surveyed more than 2,050 executives and 2,050 non-executive employees in 21 countries, across multiple industries. For digital leaders, the research shows that:

  • 78% of decisions are data-driven
  • 62% of decisions are made in real time
  • 62% of decisions are distributed across the organization

But for other companies, what is it that stands in the way of wider use of analytics? One obstacle is the fact that data analytics historically has been an IT specialty requiring technical know-how. More recently this has changed. Today’s data analytics market offers a wide range of self-service solutions that allow end users to do sophisticated analytics on their own.

This is a good thing. But even with the rise of self-service analytics, businesses are still experiencing low adoption rates. Why? I think it has a lot to do with on-premise deployment and the solutions that have been available to them.  Companies have invested tremendous amounts in data analytics solutions – but the vast majority of these solutions have been implemented behind the enterprise firewall.

Toward the cloud option

It’s hardly a news flash that cloud deployment can provide you with the potential for flexibility and greater reach. Still, many companies operate from a mindset of wanting to get the most out of their existing investment. This is to be expected. After all, maximizing this investment makes logical business sense.

But on the other hand, data analytics in the cloud can help you expand user adoption – which is a powerful incentive to move forward. For example, with cloud-based, self-service data analytics, you could centralize all reports and make them available enterprise-wide. You could also allow users to rate reports so that the most useful float to the top and help fuel greater adoption.

Or take the example of a larger auto manufacturer coordinating with its dealership network. With the cloud, manufacturers can make available and accessible critical program data on inventory, pricing, incentives, and much more. Doing the same thing with systems at dealerships connecting to systems at the manufacturer would require a lot of time, money, and energy to deploy and maintain. For increasing analytics adoption to a wider base of stakeholders, the cloud is easier, faster, and more cost-effective.

Extend and expand with hybrid analytics

But still, a wholesale move to the cloud just isn’t in the cards for some companies that have invested in on-premise data analytics and need to realize the value of that investment. This is where hybrid analytics comes into play.

Think of hybrid analytics as the first step on your journey to the cloud – one that allows you to extend and expand your on-premise data and make it available for cloud consumption.

Hybrid analytics can be seen as a variety of bimodal IT. Here the notion is one of two lanes for IT: one lane dedicated to a “system of record” (your core data investment), the other dedicated to a system of innovation (rapidly built line-of-business solutions to capitalize on opportunities). These two lanes exist side by side, with the system of innovation leveraging the system of record to do new things in the digital economy.

The fact is, end users typically are not concerned with where the data lives. They just want access. And if your goal is to get more value out of your data, providing access to it for high-end analytics is a good first step forward. As you move forward with hybrid analytics, you can evaluate the value of the cloud deployment at a more fundamental level. Does it make sense for you to switch over to an all-cloud all-the-time model? Maybe, or maybe not. But with the experience of hybrid analytics under your belt, you’ll be in a better position to make an informed decision for the benefit of your company and your customers.

To learn more, I encourage you to watch this on-demand Webinar to learn about one of SAP’s approaches to extending user adoption of analytics in the digital economy.


Steve McHugh

About Steve McHugh

Steve, Director for BI Enterprise Marketing at SAP, has a solid background in enterprise performance management and analytics. For the last several years, Steve has been involved in leading marketing efforts for SAP’s on-premise enterprise BI solutions. Currently, his focus is directed on helping enterprise BI customers with their journey to the cloud, extending and expanding their analytics adoption and use by capitalizing on their on-premise investments and data through hybrid analytics.

The Marriage Of Big Data And Enterprise Data

Ella Brand

Data is flowing and the volume is growing. With the massive generation of information from the advent of the Internet and the increasing digitalization of business, there is tremendous opportunity in the new amounts and types of data collected. But this data explosion has also dramatically increased the complexity of the enterprise data landscape, with multiple data lakes, data warehouses, operational applications, e-commerce, online interactions, and so on.

As stated by Gartner’s Ted Friedman in a recent paper*: “Organizations struggle to design a business-relevant infrastructure that is both effective and efficient at mediating differing semantics (e.g., governance) to support data sharing and integration.” You know you have a data landscape management problem when:

  • Your data is kept in silos (files, Hadoop, data warehouses) across the enterprise.
  • Your user groups can’t access and work with data according to their needs.
  • You face organizational boundaries between IT (Big Data vs. enterprise data), as well as lines of business.

Complex problems require complex solutions

Why not just put all the data into one massive, super data lake? That’s what the Hadoop distribution providers want you to do. But as the satirist H. L. Mencken said, “For every complex problem there is an answer that is clear, simple, and wrong.”

Big Data technologies lack enterprise governance, holistic lifecycle management, and security concepts. These providers are just coming up the curve and trying to provide the level of enterprise governance and security that enterprise data warehouse and database providers have been delivering for their offerings for years.

That means that organizations are stuck with limited tools for integrating systems and creating data pipelines. As a result, it takes a lot of effort to create a data pipeline across the enterprise, including:

  • High investment in resources and many non-integrated technologies, such as Hadoop, Spark, Kafka, MongoDB, Cassandra, and so on, is needed to address the business needs.
  • Integration effort usually prevents business value creation.

Some companies have tried to solve these issues by maintaining two sets of data: one for transactions and one for analysis. But this is not only costly and inefficient; it also leads to discrepancies because it’s hard to keep them in sync. And discrepancies lead to inaccurate analytical outcomes, with the obvious negative impact on decision-making.

Challenges of data landscape and DataOps management

Better meeting the needs of business and the fast pace of today’s demands means that the landscape needs to overcome the following three challenges:

  1. Governance

We face the lack of visibility, and ask: Who changed the data? What was changed? Who is accessing it?

  1. Data pipeline

It is difficult to refine and enrich data across multiple systems. For example, this might involve improving the value of existing data by appending information, such as connecting sensor data with the asset ID and asset profile information, held in a different system.

  1. Data sharing

Unfortunately, integration is manual, point-to-point, painful, and slow. Changing an integration point usually depends on the agility and flexibility of the IT line.

A modern data landscape management strategy

The solution to better address these challenges would be a data landscape and DataOps management solution that enables agile data operations across the enterprise, and also enables data governance, pipelining, and sharing of all data in the connected landscape.

The vision should be to provide the ability to understand, connect, and drive processes across the multiple data sources and endpoints with which the enterprise struggles today. By providing visibility into the landscape of data opportunities, as well as providing an easy way to connect data sources and create powerful data pipelines that hop across the landscape, businesses would be able to better achieve the data agility and business value that they seek.

Want to learn about how SAP faces these data landscape and DataOps management challenges, and what the future Big Data warehousing is about? Then register for the upcoming Webinar on November 28 at 11:00 a.m. ET/17:00 p.m. CET. You’ll hear from Marc Hartz, product manager of SAP Data Hub. See you there!

*Gartner, Use a Data Hub Strategy to Meet Your Data and Analytics Governance and Sharing Requirements, Andrew White, Ted Friedman, 2 February 2017


Ella Brand

About Ella Brand

Ella Brand is the product marketing lead for SAP Data Hub with expertise and a general focus on analytics and Big Data solutions.

Content Management: The Surprisingly Obvious Starting Point For Digital Transformation

Bil Khan

Part 2 in the Content Management ROI series

Digital transformation is quickly rising to the top of the business agenda. Across nearly all industries, executives fear being outperformed by digital competitors. Some see the potential for breakthrough opportunities made possible through advances in digital technologies. The writing is on the wall: Digital innovators are redefining long-established business models, turning competitive landscapes upside-down, and rendering obsolete the value propositions of established products and services.

There are all kinds of studies and papers and examples focusing on how to drive digital transformation. But the challenge for most organizations is where to start. It can take time to develop the right strategy, get it approved at the board level, secure budgets, and start the implementation and change management program. Digital transformation initiatives can require far-reaching changes – including organizational structures, processes, resources, culture and values – and related networks.

The problem is that there’s no time to waste. Companies need to get started today, because by the time external pressures make apparent the need to shift strategy, it could be too late.

The question is, how?

Start simple – and start now – with ECM

Here’s an idea: While you’re busy creating your digital strategy and securing resources, lay the foundation with an enterprise content management (ECM) solution that prepares your unstructured content for use by a digitally transformed enterprise. Unstructured content includes everything from emails and attachments to Microsoft Office files, videos, CAD drawings, claims, medical notes and test reports, call center records, and recorded phone conversations. These assets contain critical information that core systems cannot capture, and yet are critical to day-to-day operations and processes. In addition, they can yield valuable insights and can be shared with partners to facilitate closer collaboration, automation, transparency, and efficiency.

Increasingly, our customers understand that if they are serious about digital transformation, they need a strategy for handling this unstructured content. 80% is a huge proportion of your information to have trapped in silos, disconnected and inaccessible for analysis or reuse within digitally transformed processes. In fact, I believe it’s impossible to realize the full benefit of digital transformation initiatives without a strategy for unstructured content. It’s essential to having an intelligent, digital, touchless end-to-end processes.

A real-world example

To illustrate, let’s consider a real-world example: invoice processing. Traditionally, it’s a very high-touch process that costs the average organization US$12.90 for every invoice.

Why so much? Think about how many invoices come into organizations as email attachments. Each attachment must be manually opened, reviewed, approved, and funneled to accounts payable for check creation, distribution, and recording in the general ledger. As long as invoices are unstructured documents – with the actual data locked up in the attachment – the degree to which this process can be digitally transformed is minimal.

Enter ECM, which can extract the data in email attachments instantly – as they flow into email inboxes – so that it can flow automatically to a “smart” system for automated invoice processing. Now network business rules alert suppliers about errors and prevent incorrect invoices from entering your process workflow or back-end system at all. And if the invoice data does match what’s in the system, payment is handled in an automated, 100% touchless process.

Think less complexity, more strategy, and lightning-fast processing that can dramatically lower costs. And instead of spending time handling paper, exceptions, and phone calls, your AP team can focus on more strategic pursuits such as helping to free up cash and optimize working capital. You can also benefit from:

  • Accelerated cycle time
  • Automated exception-handling
  • Stronger compliance with internal and regulatory policies
  • Reduced savings leakage
  • Supplier visibility into payment status
  • Improved cash flow for your suppliers
  • Automated capture of negotiated terms

ECM is foundational to enabling smart, automated invoice management – and this is just one example of the many business processes in which unstructured content is involved.

Reap immediate benefits – then more

Introducing an ECM solution is the fastest, easiest way to begin executing on your digital strategy. Start by transforming unstructured content, freeing it from silos so that it can flow where you need it to go for analysis and reuse. You can even extend ECM through digital and mobile processes to supply chain partners and employees on the go. In other words, ECM prepares all your content for use within a digitally transformed business.

Meanwhile, as I pointed out in Part 1 of this blog series, you can reap huge cost savings along the way. Read the Forrester FEI report for more information.


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.



No Longer Soft Skills: Five Crucial Workplace Skills Everyone Should Learn

Carmen O'Shea

My child’s elementary school focuses on skills they believe support children in becoming changemakers. Through use of an integrated, project-based curriculum, they explicitly teach and assess “learner values” such as iteration, risk, failure, collaboration, and perspective. Their philosophy is that these attributes long considered “soft skills” have become the crucial educational priorities for this generation.

Why do they believe this? Much knowledge is now easily accessed and readily queried, such that the acquisition of specific content or know-how is far less important than how to apply that content in different situations and how to interact with others in the pursuit of goals. This holds true in the workplace as well as the academic environment. When I think about how I operate in my job at a large technology company, it’s not really what I know but what I do with what I know, and whom I engage to get things accomplished.

Watching the school teach these skills just as they do math or language has made me stop and consider what they look like for an employee. I wanted to share my thoughts on five qualities beyond relevant academic skills or professional experience that are just as important (if not more so) in predicting top work performance. These are more qualitative skills that managers should hire for, employees should develop, and organizations should optimize for.

  • Empathythe ability to see and integrate multiple perspectives and to understand the impact of how others think. Empathy can also mean advocating and showing empathy for oneself and for others. Empathy is assuming a good intention even when someone has said or done something we dislike – to stop and pause, attempt to understand, and respond compassionately in a difficult workplace situation. Empathy also extends to intuiting beyond just the professional environment to more of a personal level to truly understand what drives a colleague or employee.
  • Resiliencethe ability to take risks even when you know you may fail and then to bounce back, sometimes repeatedly, from failure. Inherent in resilience is the idea of iteration – that it is often essential to try things multiple times, in multiple ways, from multiple angles, before achieving a desired outcome. Resilience is receiving difficult yet constructive feedback from a manager or peer and resolving to act positively on it instead of wallowing or harboring a grudge. Resilience is maintaining a sense of optimism even in a down quarter at work.
  • Creativitythe ability to think differently or expansively and to approach a problem from multiple angles. Sometimes it’s called “thinking outside the box.” Creativity often includes inquiry, the act of questioning and satisfying one’s curiosity about particular topics. Torrance defined it along several parameters – number of ideas generated, number of categories of ideas, originality of ideas, and how detailed each idea is elaborated. We see it in action during brainstorming phases of projects, but it’s also possible to apply creativity on a continual basis, by pushing colleagues to expand on their thoughts, by not being satisfied with a less than stellar answer, by taking time to understand how multiple approaches to an issue could be combined, or by simply trying something new in a familiar situation.
  • Collaborationthe ability to interact and work productively with others, in all size groups. Effective collaboration requires empathy, especially when collaborators have different backgrounds, styles, or thought processes. Collaboration also requires exemplary communication skills, both oral and written, as well as reflective listening. So much of our tasks on the job require collaboration with others, whether to inform, persuade, learn, or engage, and these interactions form the bedrock for innovation. It’s tough to innovate without collaborating.
  • Flexibilitythe ability to adapt or change course if that is what the situation demands. Flexibility includes letting go of one’s idea in the interest of attaining a goal more quickly. It can also include development a comfort level with uncertainty or ambiguity, especially in times of change. Flexibility is a willingness to absorb feedback objectively and course correct as needed without personalizing the information or demonizing the provider of it. Expounding on another’s idea (not our own) in a brainstorming session demonstrates flexibility, as does remaining calm while an org change takes effect and roles are temporarily unclear.

When employees exhibit these qualities, they are better able to understand their purpose at work and to unleash their passions in the pursuit of that purpose. When teams exhibit these qualities, achievement and employee engagement are higher.  I wager that retention and innovation will improve as well. It’s heartening that as a society we’re beginning to consider how to best prepare our children educationally for the kind of work environments they will encounter after they finish their academic journey.

Do you also see these qualities as valuable in assessing employee fit? How can managers and organizations better identify, train and reward employees for living these qualities?

For more on this topic, see Your Business Needs People With Skills, Not Just Qualifications.


Carmen O'Shea

About Carmen O'Shea

Carmen O’Shea is the Senior Vice President of HR Change & Engagement at SAP. She leads a global team supporting major transformation initiatives across the company, focused on change management, employee engagement, and creative marketing and interaction. You can follow Carmen on Twitter.