Preparing For GDPR: Ready-FIRE!-Aim Doesn’t Always Work

Jan Gardiner

In these days of romanticizing ready-FIRE!-aim as it relates to entrepreneurship and product innovation, I’ll stand up and voice my concerns when it comes to using this as a compliance strategy for GDPR.

The General Data Protection Regulation (GDPR) is a sweeping regulation that will likely have a major impact on most companies—except for those few that have absolutely nothing to do with the personal data of residents of the European Union. Chances are you have already read all about GDPR, so I won’t explain it here (and besides, that’s best left to your legal team). But think of it as SOX on steroids, but with a data privacy and protection focus.

Given the siloed world many of us live in, chances are that your company deals with the introduction of each major regulation as a one-off event. Depending upon the focus of the regulation, the company may do a quick evaluation to say, “Yup, it applies to us” and then assign responsibility to the department that is most involved. For example, SOX was probably initially assigned to the financial accounting folks, until management realized it wasn’t just about getting the debits and credits right; instead, it involved tightening up a variety of activities that spanned many groups and departments from the CEO on down.

I’m a bit concerned that companies may be approaching GDPR compliance in the same way. That is, start a spreadsheet, toss GDPR to various IT owners, let them buy and implement whatever they need, and call it good. After all, if you pour enough technology on it, it should be easy—right? But there are more than a few problems with this approach:

  • First, GDPR isn’t just about technology. It includes technology, sure, but includes also people, processes, and even company attitudes towards the protection of data.
  • And while there is a myriad of vendor offerings to help with GDPR, there is not now and likely never will be a single, comprehensive tool set that does it all. There is no magic “GDPR in a box” solution, alas. So, sending IT on a quest to find this mythical solution is not likely to be a rousing success.
  • Besides, without first carefully evaluating GDPR requirements and assessing your gaps and challenges, how can you possibly know when you have found the right combination of solutions for your company? Is a spreadsheet along with numerous e-mails enough to prove that you are compliant?
  • Also, how do you know that various risks and controls surrounding GDPR will be assessed the same way for a consistent, effective, and sustainable approach?

Take aim at GDPR compliance

So instead of a ready-FIRE!-aim approach, I propose instead taking the extra time to aim by:

  • Carefully analyzing the regulation to determine which specific requirements apply to the company
  • Fact-finding to understand the situation today—which systems, data, processes, policies, contracts, and people are related to GDPR compliance
  • Comparing the detailed GDPR requirements vs. your as-is state to determine what gaps exist
  • Creating different workstreams to investigate and fill those gaps
  • Documenting what you are doing as you go along so you know where you stand and can evaluate your progress
  • Focus on creating sustainable processes and practices, not just one-time quick fixes. There is no indication that GDPR will go away any time soon.

I am personally a fan of having several workstreams working concurrently. For one thing, GDPR goes into effect on May 25, 2018, so there is not much time left.  And there is no reason why one team cannot be working on updating policies and related education materials while another team is implementing software to help locate and correlate personal data elements. So a multi-workstream approach can be beneficial and, depending upon how far along you are with GDPR compliance, it may be the only approach that will help you be compliant on time.

Don’t underestimate the GDPR requirements for being able to provide evidence of compliance and demonstrating accountability.  A ready-FIRE!-aim approach isn’t the best way to do this (no surprise!). And having a software solution to document risk assessments, evaluate controls, monitor systems, and provide the reporting that your Data Protection Officer (DPO) needs can be a huge step up from spreadsheets and e-mails.

On a related note, if yours is one of the many companies that may not be 100% GDPR-compliant on day one, generally held opinion (not legal fact) is that being able to demonstrate strong good faith efforts will go a long way. Having a clear idea of where you are, where you are going, progress made, and priorities for continuing are key.

In short, compliance with GDPR should not be a one-time unilateral project, but instead needs to be a sustainable enterprise-wide process. If you are using a ready-FIRE!-aim approach, it’s likely you’ll end up missing the target.

To learn more about consumer expectations for data privacy, read this recent blog. And read all the GRC Tuesday series blogs on GDPR.

This article originally appeared on SAP Analytics.


Blockchain To Blockchains: Broad Adoption Enters The Realm Of The Possible, Part 4

Darshini Dalal

Eric Piscini is co-author of this blog.

Part 4 in the “Deloitte Blockchain Adoption” series

Blockchain technology and its derivatives are continuing to mature, but a number of enabling conditions need to be addressed for its mainstream potential to be realized around the world. Deloitte leaders across 10 global regions see varying levels of certainty around the anticipated impact that the technology could have on financial services, manufacturing, supply chain, government, and other applications. While there are pockets of innovation in places such as Asia Pacific, Northern Europe, and Africa, many countries in Europe and Latin America are taking it slow, awaiting more standardization and regulation.

The general expected timeframe for adoption is two to five years, with some notable exceptions. Most regions have seen an uptick in proof-of-concept (PoC) and pilot activity, mostly by financial institutions working with blockchain startups. A few countries in Africa and Northern Europe are exploring national digital currencies and blockchain-based online payment platforms. In Asia Pacific, several countries are setting up blockchains to facilitate cross-border payments.

The Middle East, while bullish on blockchain’s potential—Dubai has announced its intention to be the first blockchain-powered government by 2020, for example—finds itself in the very early phases of adoption; widespread adoption is expected to take up to five years in the region.

In most regions, the main barrier to adoption is public skepticism as well as concerns about regulation. However, as consortiums, governments, and organizations continue to develop use cases for smart contracts, and the public becomes more educated on potential benefits, viable blockchain applications should continue to evolve around the world.

Where do you start?

Though some pioneering organizations may be preparing to take their blockchain use cases and PoCs into production, no doubt many are less far down the adoption path. To begin exploring blockchain’s commercialization potential in your organization, consider taking the following foundational steps:

  • Determine if your company actually needs what blockchain offers. There is a common misconception in the marketplace that blockchain can solve any number of organizational challenges. In reality, it can be a powerful tool for only certain use cases. As you chart a path toward commercialization, it’s important to understand the extent to which blockchain can support your strategic goals and drive real value.
  • Put your money on a winning horse. Examine the blockchain uses cases you currently have in development. Chances are there are one or two designed to satisfy your curiosity and sense of adventure. Deep-six those. On the path to blockchain commercialization, focusing on use cases that have disruptive potential or those aligned tightly with strategic objectives can help build support among stakeholders and partners and demonstrate real commercialization potential.
  • Identify your minimum viable ecosystem. Who are the market players and business partners you need to make your commercialization strategy work? Some will be essential to the product development lifecycle; others will play critical roles in the transition from experimentation to commercialization. Together, these individuals comprise your minimum viable ecosystem.
  • Become a stickler for consortium rules. Blockchain ecosystems typically involve multiple parties in an industry working together in a consortium to support and leverage a blockchain platform. To work effectively, consortia need all participants to have clearly defined roles and responsibilities. Without detailed operating and governance models that address liability, participant responsibilities, and the process for joining and leaving the consortium, it can become more difficult—if not impossible—to make subsequent group decisions about technology, strategy, and ongoing operations.
  • Start thinking about talent—now. To maximize returns on blockchain investments, organizations will likely need qualified, experienced IT talent who can manage blockchain functionality, implement updates, and support participants. Yet as interest in blockchain grows, organizations looking to implement blockchain solutions may find it increasingly challenging to recruit qualified IT professionals. In this tight labor market, some CIOs are relying on technology partners and third-party vendors that have a working knowledge of their clients’ internal ecosystems to manage blockchain platforms. While external support may help meet immediate talent needs and contribute to long-term blockchain success, internal blockchain talent—individuals who accrue valuable system knowledge over time and remain with an organization after external talent has moved on to the next project—can be critical for maintaining continuity and sustainability. CIOs should consider training and developing internal talent while, at the same time, leveraging external talent on an as-needed basis.

Bottom line

With the initial hype surrounding blockchain beginning to wane, more companies are developing solid use cases and exploring opportunities for blockchain commercialization. Indeed, a few early adopters are even pushing PoCs into full production. Though a lack of standardization in technology and skills may present short-term challenges, expect broader adoption of blockchain to advance steadily in the coming years as companies push beyond these obstacles and work toward integrating and coordinating multiple blockchains within a single value chain.

Contact Darshini Dalal at ddalal@deloitte.com

www.deloitte.com/SAP

SAP@deloitte.com

@DeloitteSAP

This article originally appeared on Deloitte Insights and is republished by permission.


Darshini Dalal

About Darshini Dalal

Darshini Dalal, a technology strategist with Deloitte Consulting LLP, has deep implementation experience with complex large-scale technology transformations. She leads Deloitte's U.S. Blockchain Lab, and focuses on creating immersive experiences to help clients understand not only the applications but also implications of blockchain technology across a variety of business issues that plague today’s transaction fabrics. Darshini helps clients define their vision statement and translate this vision to reality by designing the next generation of systems and platforms.

A Look Beyond The Basics Of Cloud Database Services: What’s Next? Part 2

Ken Tsai

Part 2 in a 2-part series. Read Part 1.

When Amazon Web Services (AWS) began, it started by providing massive computing resources from an infrastructure-as-a-service (IaaS) standpoint. As other vendors followed, the first approach (and easiest thing to do) was to take existing software and put it into the cloud. But very quickly, database management system (DBMS) providers realized this was an opportunity to rethink the architecture of their products. Database systems can run faster, easier, and cheaper if you re-architect the software for cloud operation – database-as-a-service (DBaaS) – versus on-premises.

DBaaS opportunities and emerging trends

It is obvious that DBaaS adoption until now has come only from migrating an existing DBMS workload. Yes, DB in the cloud with portabilities for popular open source or best-of-breed options, such MySQL, Cassandra, and Mongo, have been no-brainers for both users and public cloud providers. I call this the GEN-1 cloud DB. I eagerly anticipate a much faster convergence of the multitude of data processing techniques within a re-factored DBaaS, such as a DBaaS that also has query and process data stored in the cloud object store.

I look forward to the development of an improved unified data computing framework that expands directly from DBaaS to query data in shared storage, such as cloud object store. This is already happening in some DB-as-a-service offers, which I refer to as GEN-2 cloud DB. This generation promises to strike the right balance between value, performance, and ease of access.

The promise of hybrid cloud environments and the apps we have yet to invent

One of the hypotheses I’ve been testing is the possibility of a new class of application scenarios that can naturally take advantage of the distributed nature of data and workload between on-premises and cloud. I see ample evidence in both enterprise and consumer companies dealing with distributed data everywhere.

I see first-hand examples of many of these application scenarios, ranging from marketing automation, service and support, customer service and sales, or even in a business-to-business or a business-to-network collaboration.

For example, Google Photos was originally built to automatically sync our photos to the cloud, but it rapidly evolved to use machine learning and AI to search, detect, and display a photo when you search for a keyword. It also creates a montage of photos, with background music to share as a video or a memory/timeline. This is all possible based on the understanding of where the data is and what it must be used for.

Opportunities in security, data privacy, and data anonymization

Security offers another area of opportunity for DBaaS. Increased data privacy regulations in the European Union are bringing even more pressure to bear on companies that handle consumer data. The EU’s General Data Protection Regulation (GDPR), which goes into effect May 25th, 2018, guarantees new levels of data privacy for customers by requiring companies who utilize any type of customer information to inform (explain why, where, and what their information is used for) and receive explicit consent.

This is good news for consumers, but it poses some challenges for companies who want to gain business insights from data. Innovative companies are responding by heavily investing in new technology called data anonymization. This technology enables businesses to completely anonymize the data itself so that you can’t tell where or whom the data is attributed to.

Eventually, I see that the rise of data privacy and security regulation will become global. (For further information, here’s a Webinar that dives deeper into security, data privacy, and anonymization.)

Cloud-based databases have a role to play in digital transformation

For many companies, when digital transformation becomes the goal of the C-suite, adoption of DBaaS naturally follows. Digital transformation in its simplest terms is about the digitization of assets, relationships, and engagements in the context of bringing together customers, partners, and employees. Decisions – about them and the company – must be highly data driven. To this end, databases become the critical component of facilitating the successful digital transformation of a company.

I’d like to hear what you think. What do you see as the challenges for DBaaS? What are you most excited about? Leave a comment or connect with me on LinkedIn and Twitter.

Learn more

To learn how your organization can benefit from a hosted data management platform in multi-clouds and cloud/on-premises deployments, visit sap.com/HANA.

If you’re missing the live launch at SAPPHIRE NOW, register for our webcast June 21, 11 a.m. EDT/5 p.m. CET to get the latest information.

This article originally appeared on Hackernoon.com and is republished by permission. Special thanks to David Fletcher and CloudTweaks.com for the use of their cartoons. You can follow them on Twitter at @CloudTweaks


Ken Tsai

About Ken Tsai

Ken Tsai is the global VP and head of database and data management at SAP, and leads the global product marketing efforts for SAP’s flagship SAP HANA platform and the portfolio of SAP data management solutions. Ken has more than 20 years of experiences in the IT industry, responsible for application development, services, presales, business development, and marketing. Ken is a graduate of the University of California, Berkeley.

The Human Angle

By Jenny Dearborn, David Judge, Tom Raftery, and Neal Ungerleider

In a future teeming with robots and artificial intelligence, humans seem to be on the verge of being crowded out. But in reality the opposite is true.

To be successful, organizations need to become more human than ever.

Organizations that focus only on automation will automate away their competitive edge. The most successful will focus instead on skills that set them apart and that can’t be duplicated by AI or machine learning. Those skills can be summed up in one word: humanness.

You can see it in the numbers. According to David J. Deming of the Harvard Kennedy School, demand for jobs that require social skills has risen nearly 12 percentage points since 1980, while less-social jobs, such as computer coding, have declined by a little over 3 percentage points.

AI is in its infancy, which means that it cannot yet come close to duplicating our most human skills. Stefan van Duin and Naser Bakhshi, consultants at professional services company Deloitte, break down artificial intelligence into two types: narrow and general. Narrow AI is good at specific tasks, such as playing chess or identifying facial expressions. General AI, which can learn and solve complex, multifaceted problems the way a human being does, exists today only in the minds of futurists.

The only thing narrow artificial intelligence can do is automate. It can’t empathize. It can’t collaborate. It can’t innovate. Those abilities, if they ever come, are still a long way off. In the meantime, AI’s biggest value is in augmentation. When human beings work with AI tools, the process results in a sort of augmented intelligence. This augmented intelligence outperforms the work of either human beings or AI software tools on their own.

AI-powered tools will be the partners that free employees and management to tackle higher-level challenges.

Those challenges will, by default, be more human and social in nature because many rote, repetitive tasks will be automated away. Companies will find that developing fundamental human skills, such as critical thinking and problem solving, within the organization will take on a new importance. These skills can’t be automated and they won’t become process steps for algorithms anytime soon.

In a world where technology change is constant and unpredictable, those organizations that make the fullest use of uniquely human skills will win. These skills will be used in collaboration with both other humans and AI-fueled software and hardware tools. The degree of humanness an organization possesses will become a competitive advantage.

This means that today’s companies must think about hiring, training, and leading differently. Most of today’s corporate training programs focus on imparting specific knowledge that will likely become obsolete over time.

Instead of hiring for portfolios of specific subject knowledge, organizations should instead hire—and train—for more foundational skills, whose value can’t erode away as easily.

Recently, educational consulting firm Hanover Research looked at high-growth occupations identified by the U.S. Bureau of Labor Statistics and determined the core skills required in each of them based on a database that it had developed. The most valuable skills were active listening, speaking, and critical thinking—giving lie to the dismissive term soft skills. They’re not soft; they’re human.


This doesn’t mean that STEM skills won’t be important in the future. But organizations will find that their most valuable employees are those with both math and social skills.

That’s because technical skills will become more perishable as AI shifts the pace of technology change from linear to exponential. Employees will require constant retraining over time. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is already outdated by the time students graduate, according to The Future of Jobs, a report from the World Economic Forum (WEF).

The WEF’s report further notes that “65% of children entering primary school today will ultimately end up working in jobs that don’t yet exist.” By contrast, human skills such as interpersonal communication and project management will remain consistent over the years.

For example, organizations already report that they are having difficulty finding people equipped for the Big Data era’s hot job: data scientist. That’s because data scientists need a combination of hard and soft skills. Data scientists can’t just be good programmers and statisticians; they also need to be intuitive and inquisitive and have good communication skills. We don’t expect all these qualities from our engineering graduates, nor from most of our employees.

But we need to start.

From Self-Help to Self-Skills

Even if most schools and employers have yet to see it, employees are starting to understand that their future viability depends on improving their innately human qualities. One of the most popular courses on Coursera, an online learning platform, is called Learning How to Learn. Created by the University of California, San Diego, the course is essentially a master class in human skills: students learn everything from memory techniques to dealing with procrastination and communicating complicated ideas, according to an article in The New York Times.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing.

Although there is a longstanding assumption that social skills are innate, nothing is further from the truth. As the popularity of Learning How to Learn attests, human skills—everything from learning skills to communication skills to empathy—can, and indeed must, be taught.

These human skills are integral for training workers for a workplace where artificial intelligence and automation are part of the daily routine. According to the WEF’s New Vision for Education report, the skills that employees will need in the future fall into three primary categories:

  • Foundational literacies: These core skills needed for the coming age of robotics and AI include understanding the basics of math, science, computing, finance, civics, and culture. While mastery of every topic isn’t required, workers who have a basic comprehension of many different areas will be richly rewarded in the coming economy.
  • Competencies: Developing competencies requires mastering very human skills, such as active listening, critical thinking, problem solving, creativity, communication, and collaboration.
  • Character qualities: Over the next decade, employees will need to master the skills that will help them grasp changing job duties and responsibilities. This means learning the skills that help employees acquire curiosity, initiative, persistence, grit, adaptability, leadership, and social and cultural awareness.


The good news is that learning human skills is not completely divorced from how work is structured today. Yonatan Zunger, a Google engineer with a background working with AI, argues that there is a considerable need for human skills in the workplace already—especially in the tech world. Many employees are simply unaware that when they are working on complicated software or hardware projects, they are using empathy, strategic problem solving, intuition, and interpersonal communication.

The unconscious deployment of human skills takes place even more frequently when employees climb the corporate ladder into management. “This is closely tied to the deeper difference between junior and senior roles: a junior person’s job is to find answers to questions; a senior person’s job is to find the right questions to ask,” says Zunger.

Human skills will be crucial to navigating the AI-infused workplace. There will be no shortage of need for the right questions to ask.

One of the biggest changes narrow AI tools will bring to the workplace is an evolution in how work is performed. AI-based tools will automate repetitive tasks across a wide swath of industries, which means that the day-to-day work for many white-collar workers will become far more focused on tasks requiring problem solving and critical thinking. These tasks will present challenges centered on interpersonal collaboration, clear communication, and autonomous decision-making—all human skills.

Being More Human Is Hard

However, the human skills that are essential for tomorrow’s AI-ified workplace, such as interpersonal communication, project planning, and conflict management, require a different approach from traditional learning. Often, these skills don’t just require people to learn new facts and techniques; they also call for basic changes in the ways individuals behave on—and off—the job.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing. As science gains a better understanding of how the human brain works, many behaviors that affect employees on the job are understood to be universal and natural rather than individual (see “Human Skills 101”).

Human Skills 101

As neuroscience has improved our understanding of the brain, human skills have become increasingly quantifiable—and teachable.

Though the term soft skills has managed to hang on in the popular lexicon, our understanding of these human skills has increased to the point where they aren’t soft at all: they are a clearly definable set of skills that are crucial for organizations in the AI era.

Active listening: Paying close attention when receiving information and drawing out more information than received in normal discourse

Critical thinking: Gathering, analyzing, and evaluating issues and information to come to an unbiased conclusion

Problem solving: Finding solutions to problems and understanding the steps used to solve the problem

Decision-making: Weighing the evidence and options at hand to determine a specific course of action

Monitoring: Paying close attention to an issue, topic, or interaction in order to retain information for the future

Coordination: Working with individuals and other groups to achieve common goals

Social perceptiveness: Inferring what others are thinking by observing them

Time management: Budgeting and allocating time for projects and goals and structuring schedules to minimize conflicts and maximize productivity

Creativity: Generating ideas, concepts, or inferences that can be used to create new things

Curiosity: Desiring to learn and understand new or unfamiliar concepts

Imagination: Conceiving and thinking about new ideas, concepts, or images

Storytelling: Building narratives and concepts out of both new and existing ideas

Experimentation: Trying out new ideas, theories, and activities

Ethics: Practicing rules and standards that guide conduct and guarantee rights and fairness

Empathy: Identifying and understanding the emotional states of others

Collaboration: Working with others, coordinating efforts, and sharing resources to accomplish a common project

Resiliency: Withstanding setbacks, avoiding discouragement, and persisting toward a larger goal

Resistance to change, for example, is now known to result from an involuntary chemical reaction in the brain known as the fight-or-flight response, not from a weakness of character. Scientists and psychologists have developed objective ways of identifying these kinds of behaviors and have come up with universally applicable ways for employees to learn how to deal with them.

Organizations that emphasize such individual behavioral traits as active listening, social perceptiveness, and experimentation will have both an easier transition to a workplace that uses AI tools and more success operating in it.

Framing behavioral training in ways that emphasize its practical application at work and in advancing career goals helps employees feel more comfortable confronting behavioral roadblocks without feeling bad about themselves or stigmatized by others. It also helps organizations see the potential ROI of investing in what has traditionally been dismissed as touchy-feely stuff.

In fact, offering objective means for examining inner behaviors and tools for modifying them is more beneficial than just leaving the job to employees. For example, according to research by psychologist Tasha Eurich, introspection, which is how most of us try to understand our behaviors, can actually be counterproductive.

Human beings are complex creatures. There is generally way too much going on inside our minds to be able to pinpoint the conscious and unconscious behaviors that drive us to act the way we do. We wind up inventing explanations—usually negative—for our behaviors, which can lead to anxiety and depression, according to Eurich’s research.

Structured, objective training can help employees improve their human skills without the negative side effects. At SAP, for example, we offer employees a course on conflict resolution that uses objective research techniques for determining what happens when people get into conflicts. Employees learn about the different conflict styles that researchers have identified and take an assessment to determine their own style of dealing with conflict. Then employees work in teams to discuss their different styles and work together to resolve a specific conflict that one of the group members is currently experiencing.

How Knowing One’s Self Helps the Organization

Courses like this are helpful not just for reducing conflicts between individuals and among teams (and improving organizational productivity); they also contribute to greater self-awareness, which is the basis for enabling people to take fullest advantage of their human skills.

Self-awareness is a powerful tool for improving performance at both the individual and organizational levels. Self-aware people are more confident and creative, make better decisions, build stronger relationships, and communicate more effectively. They are also less likely to lie, cheat, and steal, according to Eurich.

It naturally follows that such people make better employees and are more likely to be promoted. They also make more effective leaders with happier employees, which makes the organization more profitable, according to research by Atuma Okpara and Agwu M. Edwin.

There are two types of self-awareness, writes Eurich. One is having a clear view inside of one’s self: one’s own thoughts, feelings, behaviors, strengths, and weaknesses. The second type is understanding how others view us in terms of these same categories.

Interestingly, while we often assume that those who possess one type of awareness also possess the other, there is no direct correlation between the two. In fact, just 10% to 15% of people have both, according to a survey by Eurich. That means that the vast majority of us must learn one or the other—or both.

Gaining self-awareness is a process that can take many years. But training that gives employees the opportunity to examine their own behaviors against objective standards and gain feedback from expert instructors and peers can help speed up the journey. Just like the conflict management course, there are many ways to do this in a practical context that benefits employees and the organization alike.

For example, SAP also offers courses on building self-confidence, increasing trust with peers, creating connections with others, solving complex problems, and increasing resiliency in the face of difficult situations—all of which increase self-awareness in constructive ways. These human-skills courses are as popular with our employees as the hard-skill courses in new technologies or new programming techniques.

Depending on an organization’s size, budget, and goals, learning programs like these can include small group training, large lectures, online courses, licensing of third-party online content, reimbursement for students to attain certification, and many other models.

Human Skills Are the Constant

Automation and artificial intelligence will change the workplace in unpredictable ways. One thing we can predict, however, is that human skills will be needed more than ever.

The connection between conflict resolution skills, critical thinking courses, and the rise of AI-aided technology might not be immediately obvious. But these new AI tools are leading us down the path to a much more human workplace.

Employees will interact with their computers through voice conversations and image recognition. Machine learning will find unexpected correlations in massive amounts of data but empathy and creativity will be required for data scientists to figure out the right questions to ask. Interpersonal communication will become even more important as teams coordinate between offices, remote workplaces, and AI aides.

While the future might be filled with artificial intelligence, deep learning, and untold amounts of data, uniquely human capabilities will be the ones that matter. Machines can’t write a symphony, design a building, teach a college course, or manage a department. The future belongs to humans working with machines, and for that, you need human skills. D!


About the Authors

Jenny Dearborn is Chief Learning Officer at SAP.

David Judge is Vice President, SAP Leonardo, at SAP.

Tom Raftery is Global Vice President and Internet of Things Evangelist at SAP.

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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Machine Learning In The Real World

Paul Taylor

Over the past few decades, machine learning has emerged as the real-world face of what is often mistakenly called “artificial intelligence.” It is establishing itself as a mainstream technology tool for companies, enabling them to improve productivity, planning, and ultimately, profits.

Michael Jordan, professor of Computer Science and Statistics at the University of California, Berkeley, noted in a recent Medium post: “Most of what is being called ‘AI’ today, particularly in the public sphere, is what has been called ‘machine learning’ for the past several decades.”

Jordan argues that unlike much that is mislabeled “artificial intelligence,” ML is the real thing. He maintains that it was already clear in the early 1990s that ML would grow to have massive industrial relevance. He notes that by the turn of the century, forward-looking companies such as Amazon were already using ML throughout their business, solving mission-critical back-end problems in fraud detection and logistics-chain prediction and building innovative consumer-facing services such as recommendation systems.

“Although not visible to the general public, research and systems-building in areas such as document retrieval, text classification, fraud detection, recommendation systems, personalized search, social network analysis, planning, diagnostics, and A/B testing have been a major success — these are the advances that have powered companies such as Google, Netflix, Facebook, and Amazon,” Jordan says.

Amazon, which has been investing deeply in artificial intelligence for over 20 years, acknowledges, “ML algorithms drive many of our internal systems. It’s also core to the capabilities our customers’ experience – from the path optimization in our fulfillment centers and Amazon’s recommendations engine o Echo powered by Alexa, our drone initiative Prime Air, and our new retail experience, Amazon Go. “

The fact that tech industry leaders like Google, Netflix, Facebook, and Amazon have used ML to help fuel their growth is not news. For example, it has been widely reported that sites with recommendation engines, including Netflix, use ML algorithms to generate user-specific suggestions. Most dynamic map/routing apps, including Google Maps, also use ML to suggest route changes in real time based upon traffic speed and other data gleaned from multiple users’ smartphones.

In a recent article detailing real-world examples of ML in action, Kelly McNulty, a senior content writer at Salt Lake City-based Prowess Consulting, notes: “ML isn’t just something that will happen in the future. It’s happening now, and it will only get more advanced and pervasive in the future.”

However, the broader uptake of ML by enterprises – big and small – is less much less known. A recently published study prepared for SAP by the Economist Intelligence Unit and based on a survey of 360 organizations revealed that 68 percent of respondents are already using ML, at least to some extent, to enhance their business processes.

The report adds: “Some are aiming even higher: to use ML to change their business models and offer entirely new value propositions to customers…… ML is not just a technology.” The report’s authors continue, “It is core to the business strategies that have led to the surging value of organizations that incorporate it into their operating models – think Amazon, Uber, and Airbnb.”

McNulty notes that there are both internal and external uses for ML. Among the internal uses, she cites Thomson Reuters, the news and data services group, which, after its merger in 2008, used ML to prepare large quantities of data with Tamr, an enterprise data-unification company. She says the two partners used ML to unify more than three million data points with an accuracy of 95 percent, reducing the time needed to manually unify the data by several months and cutting the manual labor required by an estimated 40 percent.

In another example of enterprise use of ML, she notes that GlaxoSmithKline, the pharmaceuticals group, used the technology to develop information aimed at allaying concerns about vaccines. The ML algorithms were used to sift through parents’ comments about vaccines in forums and messaging boards, enabling GSK to develop content specifically designed to address these concerns.

In the financial sector, ML has been widely used for some time to help detect fraudulent transactions and assess risk. PayPal uses the technology to “distinguish the good customers from the bad customers,” according to Vadim Kutsyy, a data scientist at the online payments company.

PayPal’s deep learning system is also able to filter out deceptive merchants and crack down on sales of illegal products. Additionally, the models are optimizing operations. Kutsyy explained the machines can identify “why transactions fail, monitoring businesses more efficiently,” avoiding the need to buy more hardware for problem-solving.

ML algorithms also underpin many of the corporate chatbots and virtual assistants being deployed by enterprise customers and others. For Example, Allstate partnered with technology consultancy Earley Information Science to develop a virtual assistant called ABIe (the Allstate Business Insurance Expert). ABIe was designed to assist Allstate’s 12,000 agents to understand and sell the company’s commercial insurance products, reportedly handling 25,000 inquires a month.

Other big U.S. insurance companies, including Progressive, are applying ML algorithms to interpret driver data and identify new business opportunities.

Meanwhile, four years ago, Royal Dutch Shell became the first company in the lubricants sector to use ML to help develop the Shell Virtual Assistant. The virtual assistant enables customers and distributors to ask common lubricant-related questions.

As the company noted at the time, “customers and distributors type in their question via an online message window, and avatars Emma and Ethan reply back with an appropriate answer within seconds.” The tool was initially launched in the U.S. and UK but has since expanded to other countries and reportedly can now understand and respond to queries in multiple languages, including Chinese and Russian.

In the retail sector, Walmart, which already uses ML to optimize home delivery routes, also uses it to help reduce theft and improve customer service. The retail giant has reportedly developed facial recognition software that automatically detects frustration in the faces of shoppers at checkout, prompting customer service representatives to intervene.

Among SAP’s own customers, a growing number are implementing ML tools, including those built into SAP’s own platforms and applications. As SAP notes, “Many different industries and lines of business are ripe for machine learning—particularly the ones that amass large volumes of data.”

The manufacturing, finance, and healthcare sectors are leading the way. For example, a large European chemicals company has improved the efficiency and effectiveness of its customer service process by using ML algorithms to automatically categorize and send responses to customer inquiries.

In the mining sector, Vale, the Brazilian mining group, is using ML to optimize maintenance processes and reduce the number of purchase requisitions that were being rejected causing maintenance and operational delays in its mines. Before implementation, between 25 percent and 40 percent of purchase requisitions were being rejected by procurement because of errors. Since implementation, 86 percent of these rejections have been eliminated.

Elsewhere a large consumer goods company, the Austrian-based consumer good company, is using ML and computer vision to identify images of broken products submitted by customers from the over 40,000 products in the company’s catalog. The application enables the company to speed up repairs and replacements, thereby improving customer service and the customer experience.

Similarly, a global automotive manufacturer is using image recognition to help consumers learn more about vehicles and direct them to local dealer showrooms, and a major French telecommunications firm reduced the length of customer service conversations by 50 percent using chatbots that now manage 20 percent of all calls.

But not every enterprise ML deployment has worked out so well. In a highly publicized case, Target hired a ML expert to analyze shopper data and create a model that could predict which female customers were most likely to be pregnant and when they were expected to give birth. (If a woman started buying a lot of supplements, for example, she was probably in her first 20 weeks of pregnancy, whereas buying a lot of unscented lotion indicated the start of the second trimester.)

Target used this information to provide pregnancy- and parenting-related coupons to women who matched the profile. But Target was forced to modify its strategy after some customers said they felt uncomfortable with this level of personalization. A New York Times story reported that a Minneapolis parent learned of their 16-year-old daughter’s unplanned pregnancy when the Target coupons arrived in the mail.

Target’s experience notwithstanding, most enterprise ML projects generate significant benefits for customers, employees, and investors while putting the huge volumes of data generated in our digital era to real use.

For more insight on the implications of machine learning technology, download the study Making the Most of Machine Learning: 5 Lessons from Fast Learners.