Big Data Myths...BUSTED

Jen Cohen Crompton

Since Big Data is such a “big” and diverse topic, there are plenty of assumptions, misunderstandings, and confusion surrounding the concept. Through discussions with colleagues, I’ve noticed a few recurring themes, uncertainties and generalizations. I’ve also heard plenty of assumptions and claims…some of which are true and some lack validity. After hearing from industry experts, I’ve compiled three Big Data Myths, which we will bust, to uncover some of the Big Data facts.

Myth One: Big Data is really “BIG.”

When it is stated that big data is really big, that infers that big data translates into a lot of information, more than most companies collect. In that case, this myth is not true at all. At the latest Hadoop conference, big data was defined as any data that cannot fit into Excel, which in reality, is the amount of data most companies collect and store, especially since the rise in social media and the volumes of data collected from those sources.

“Social media releases floods of data that hold massive promises for business – both in terms of branding and opening up new channels to market, in which large populations of consumers are speaking. They’re [consumers] communicating who they are and what they do and do not like. Such a relentless flow of data provides extraordinary levels of feedback and an unrivaled chance for companies to listen to the voices of their customers and target audience(s), garner intelligence, and participate in a collaborative dialogue to boost competitive advantage and new opportunity,” explains Upasna Gautam of MagicLogix.com. [Stay tuned for another post about the link between social media and big data.] Most businesses are using social media outlets for this type of insight and the amount of data collected and needed to be analyzed is considered “big.”

So it isn’t just enterprises who are struggling to manage big data. The general rule – if you have multiple spreadsheets with data, you have big data on your hands. As Margaret Dawson, VP of Marketing for Symform points out, “SMBs also struggle to keep up with skyrocketing data volumes. In fact, a recent data and backup trends survey of SMBs found that respondents average one terabyte to over 500 TBs (1 TB = 1000 GB) of data, with most forecasting data growth of 10-40% over the next year.” Wow.

Myth Two: Big Data makes BETTER analytics.

Is bigger really better? Sometimes, but in the case of big data, it seems that in certain circumstances, bigger is simply bigger and quantity does not always equal quality; the gap occurs in the analysis and translation of the data. It’s [almost] comparable to giving someone a hammer, nails, wood, and all the tools they need to build a house, but without the blueprint, they don’t know what to do and where to start. This means that with our data, we have to be sure we are collecting the valuable data to help solve the most prominent problems – the problems that relate back to our KPIs and bottomlines – and following the right blueprint to get what we need.

Erin Bartolo, Data Science Program Manager at the School of Information Studies at Syracuse University, agrees and provides a strategy to ensure bigger data turns into something meaningful. “Entertain your inner skeptic by questioning everything from what data are meaningful to how you project your own biases on findings. Without objective, analytical skills, analytics merely backs up our own biases with data,” she advises. She explains that the “whys” and “hows” need to be infused into the data analysis to really find value. She says, “…increasing one’s awareness of data and appreciation of its objectivity reveals insights whether the data is stored in an Excel spreadsheet or in a massive data warehouse.”

So in this case, size doesn’t really matter, unless you need the size of the data to answer the questions that relate back to your ultimate goals.

Myth Three: You need a team of Hadoop engineers and Analytics platforms to be on premise to work with Big Data.

While it is quite a challenge to merge data collected from various sources and analyze the information (and Hadoop professionals could be an advantage), there are other solutions and platforms that can help transform the unstructured data into structured data and merge with business intelligence (BI) tools.

Werner Hopf, CEO, Dolphin believes, “There are compelling [software] solutions to help companies meet those goals, achieve significant savings and performance improvements, and lay the foundation for leveraging SAP HANA – the vehicle for truly maximizing the potential benefits of big data – in the future.” These options can be cost effective and easily navigated by an intelligent, but not expert users.

The other idea of housing analytics platforms on premise is busted by Keith Metcalfe, Vice President of Sales and Marketing at WCI Consulting as he adds, “Integrating and cleansing data to a targeted place for reporting is a core concept behind any enterprise approach to analytics/business intelligence, and there is no technical reason why that target cannot reside in a hosted/SaaS environment. Cloud platforms and analytics tools are great applications for hosted (e.g. Amazon Web Services) or SaaS analytics platforms (e.g. SAP BusinessObjects BI OnDemand). Having said this, core to the topic of SaaS and hosted environments is that an organization sees value in replacing IT infrastructure, as this is where the financial return justifies the cost of investing in such environments.”

Two last pieces of big data management advice from Hopf, “From a data management perspective, making the most of the big data opportunity requires the adoption of two key strategies: 1) augmenting data archiving capabilities with nearline storage; and 2) re-architecting the business warehouse (BW) data model for lean, flexible, organized “views” of information that serve up agile reporting without increasing administrative
overhead.” Do this, add the human aspect, and solve big business problems using your big data.

If you have questions about these myths, feel free to reach out to a Top Big Data Twitter Influencers.

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About Jen Cohen Crompton

Jen Cohen Crompton is a SAP Blogging Correspondent reporting on big data, cloud computing, enterprise mobility, analytics, sports and tech, and anything else innovation-related. When she's not blogging, she can be caught marketing, using social media and/or presenting at conferences around the world. Disclosure: Jen is being compensated by SAP to produce a series of articles on the innovation topics covered on this site. The opinions reflected here are her own.

The Promise And The Peril Of Blockchain

Andre Smith

This past year has seen the integration of blockchain technologies into businesses around the globe. Serious technology professionals regard the technology as a great leap forward for distributed computing, transparency, and security. The blockchain may well be the panacea that they envision it to be, but that doesn’t mean that it is without its share of risk.

The overwhelming hype about blockchain-based services (aided by the explosive rise in the value of Bitcoin and other cryptocurrencies) has created an investing frenzy that calls to mind the dotcom bubble of the late 1990’s or the more recent derivative-fueled financial crisis of 2008. The problem is that the level of excitement far exceeds the tech sector’s ability to bring meaningful and innovative blockchain products to market. This reality has resulted in a speculative vacuum.

Hype breeds fraud

As is usually the case, the first people to notice the overwhelming potential of blockchain technology as a moneymaker were those who would use it for nefarious purposes. As investors clamored to pour money into any ICO they could find, crypto pioneers and financial moguls sounded alarms that were mostly ignored. There have already been some notable red flags.

In November, the team behind a startup called Confido disappeared, taking $375,000 of investor funds with them. The company had claimed to be creating a blockchain-based escrow platform. Investors, in their rush to get involved, were duped by their false promises. In December, the U.S. SEC intervened in the ICO of a company known as PlexCoin, putting a stop to what they identified as a plot by long-time fraudsters to cash in on the ICO craze.

Secure reputation, insecure products

Defrauding investors isn’t the only trend associated with blockchain technologies that should be cause for concern. There is also the potential for the technology to be misused by criminal enterprises to hide illicit transactions, and by startups relying on the public perception of the blockchain as inherently secure as a means of selling products that are anything but. Both have already become a problem.

There are a number of ways that cryptocurrencies, underpinned by the blockchain, may be used as a conduit for illegal activity. There are already real-world examples of the technology being utilized to funnel money to terrorist organizations. Then there are companies like Privatix. Once a consumer VPN service, similar to wink-and-nod offerings like the VPN Hidemyass, Privatix suddenly rebranded itself as a blockchain VPN bandwidth marketplace. In practice, this has the same inherent risks as the Tor network, and they seem to be conflating “blockchain” with “secure” in an effort to mislead consumers.

Guilt by association

What’s at stake in these early days of the blockchain story may be the fate of the technology itself. As large financial institutions and consulting firms seek to position the blockchain in the public consciousness as the ultimate trust platform, there are no shortage of damaging incidents and examples working to undermine them. It also isn’t reasonable to expect that the public at large will draw a distinction between public and private blockchains, nor that they will even comprehend the difference.

It’s far too early to know if big business will be able to co-opt the blockchain and disassociate it from an external market that has been likened to the Wild West. The only thing that is certain is that they have great incentives to do so, since the blockchain could, at least internally, be as transformative as advertised. For now, all we can do is to stay tuned to see what comes next.

To learn more about the blockchain as a trusted platform see Blockchain: Pharma’s Answer to Restoring Trust in Healthcare.

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About Andre Smith

An Internet, Marketing and E-Commerce specialist with several years of experience in the industry. He has watched as the world of online business has grown and adapted to new technologies, and he has made it his mission to help keep businesses informed and up to date.

Underfit Vs. Overfit: Why Your Machine Learning Model May Be Wrong

Paul Kurchina

Just shy of 60 years old, machine learning has never looked so good. Exponential data growth, advanced algorithms, and powerful computer processing are enabling the technology to fulfill its ultimate destiny: identifying profitable opportunities and avoiding unknown risks by evaluating massive volumes of complex data and delivering accurate results in real time.

However, during the Americas’ SAP Users’ Group (ASUG) Webcast “Guide to the Machine Learning Galaxy: How Your ERP Knowledge Enables Value-Driven Intelligent Processes,” Darwin Deano, principal and chief SAP Leonardo officer, and Denise McGuigan, senior manager and Deloitte reimagine platform leader (both from Deloitte Consulting LLP), forewarned that machine learning is only as good as the algorithm. And the algorithm is only as good as the data.

Deano advised, “Data evolves over time. Even though ERP systems provide a strong foundation for identifying opportunities and delivering on the promise of machine learning, it does not factor in information outside the core structure, nor does it move with information as it changes.”

Adding to Deano’s observation, McGuigan noted the importance of understanding data well. “Businesses must know all of the variables and data sets that drive certain decisions. Doing so will reduce the risk of bringing information into the analysis that will only cause noise or false positives within machine learning results,” she said.

Machine learning success depends on finding the right data “fit”

Although it’s tempting to jump into machine learning by automating heavily used transactions, McGuigan warned that this view misses the cognitive advantages of machine learning. “Companies have a considerable opportunity to operate with tremendous efficiency and speed,” she said. “They should also consider enabling processes and tasks that free up resources, time, and talent for entering new markets; offering breakthrough products and services; and innovating industry-disruptive business models.”

To successfully execute such an advanced form of machine learning, organizations must ensure that the right data is being applied to the machine learning model. Understanding how each data category impacts the training data helps businesses fine-tune the model to increase prediction accuracy and efficient automation. However, as McGuigan suggested, one of the most common causes of underperforming or inaccurate models can be attributed to an imbalance of data used, commonly referred as biased invariance.

One form of disparity is experienced when the model underfits the training data when assumptions are oversimplified to the point where either the wrong information or too little insight is applied. This condition leads to the inability to capture the relationship between the programmed input examples and the targeted outcomes.

On the flip side, a model can overfit training data when too much information is used and there is too much complexity. Even though it performs well with training data, the model cannot accurately evaluate data to deliver the expected outcome. The model only memorizes data, instead of learning from it to generalize how unseen examples should be treated.

It’s also important to remember that this exercise is an iterative process of trial and error. The model may be calibrated well enough at one moment to deliver expected outcomes consistently and predictively; however, as Deano suggested, “what may be overfitting today may not be the same situation six months from now as data evolves.”

For more insights into putting machine intelligence to work for your organization, watch the replay of the Americas’ SAP Users’ Group (ASUG) Webcast “Guide to the Machine Learning Galaxy: How Your ERP Knowledge Enables Value-Driven Intelligent Processes,” featuring Darwin Deano, principal and chief SAP Leonardo officer for Deloitte Consulting LLP, and Denise McGuigan, senior manager and Deloitte reimagine platform leader for Lights Out Finance at Deloitte Consulting LLP

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Paul Kurchina

About Paul Kurchina

Paul Kurchina is a community builder and evangelist with the Americas’ SAP Users Group (ASUG), responsible for developing a change management program for ASUG members.

Why Strategic Plans Need Multiple Futures

By Dan Wellers, Kai Goerlich, and Stephanie Overby , Kai Goerlich and Stephanie Overby

When members of Lowe’s Innovation Labs first began talking with the home improvement retailer’s senior executives about how disruptive technologies would affect the future, the presentations were well received but nothing stuck.

“We’d give a really great presentation and everyone would say, ‘Great job,’ but nothing would really happen,” says Amanda Manna, head of narratives and partnerships for the lab.

The team realized that it needed to ditch the PowerPoints and try something radical. The team’s leader, Kyle Nel, is a behavioral scientist by training. He knows people are wired to receive new information best through stories. Sharing far-future concepts through narrative, he surmised, could unlock hidden potential to drive meaningful change.

So Nel hired science fiction writers to pen the future in comic book format, with characters and a narrative arc revealed pane by pane.

The first storyline, written several years before Oculus Rift became a household name, told the tale of a couple envisioning their kitchen renovation using virtual reality headsets. The comic might have been fun and fanciful, but its intent was deadly serious. It was a vision of a future in which Lowe’s might solve one of its long-standing struggles: the approximately US$70 billion left on the table when people are unable to start a home improvement project because they can’t envision what it will look like.

When the lab presented leaders with the first comic, “it was like a light bulb went on,” says Manna. “Not only did they immediately understand the value of the concept, they were convinced that if we didn’t build it, someone else would.”

Today, Lowe’s customers in select stores can use the HoloRoom How To virtual reality tool to learn basic DIY skills in an interactive and immersive environment.

Other comics followed and were greeted with similar enthusiasm—and investment, where possible. One tells the story of robots that help customers navigate stores. That comic spawned the LoweBot, which roamed the aisles of several Lowe’s stores during a pilot program in California and is being evaluated to determine next steps.

And the comic about tools that can be 3D-printed in space? Last year, Lowe’s partnered with Made in Space, which specializes in making 3D printers that can operate in zero gravity, to install the first commercial 3D printer in the International Space Station, where it was used to make tools and parts for astronauts.

The comics are the result of sending writers out on an open-ended assignment, armed with trends, market research, and other input, to envision what home improvement planning might look like in the future or what the experience of shopping will be in 10 years. The writers come back with several potential story ideas in a given area and work collaboratively with lab team members to refine it over time.

The process of working with writers and business partners to develop the comics helps the future strategy team at Lowe’s, working under chief development officer Richard D. Maltsbarger, to inhabit that future. They can imagine how it might play out, what obstacles might surface, and what steps the company would need to take to bring that future to life.

Once the final vision hits the page, the lab team can clearly envision how to work backward to enable the innovation. Importantly, the narrative is shared not only within the company but also out in the world. It serves as a kind of “bat signal” to potential technology partners with capabilities that might be required to make it happen, says Manna. “It’s all part of our strategy for staking a claim in the future.”

Planning must become completely oriented toward—and sourced from—the future.

Companies like Lowe’s are realizing that standard ways of planning for the future won’t get them where they need to go. The problem with traditional strategic planning is that the approach, which dates back to the 1950s and has remained largely unchanged since then, is based on the company’s existing mission, resources, core competencies, and competitors.

Yet the future rarely looks like the past. What’s more, digital technology is now driving change at exponential rates. Companies must be able to analyze and assess the potential impacts of the many variables at play, determine the possible futures they want to pursue, and develop the agility to pivot as conditions change along the way.

This is why planning must become completely oriented toward—and sourced from—the future, rather than from the past or the present. “Every winning strategy is based on a compelling insight, but most strategic planning originates in today’s marketplace, which means the resulting plans are constrained to incremental innovation,” says Bob Johansen, distinguished fellow at the Institute for the Future. “Most corporate strategists and CEOs are just inching their way to the future.” (Read more from Bob Johansen in the Thinkers story, “Fear Factor.”)

Inching forward won’t cut it anymore. Half of the S&P 500 organizations will be replaced over the next decade, according to research company Innosight. The reason? They can’t see the portfolio of possible futures, they can’t act on them, or both. Indeed, when SAP conducts future planning workshops with clients, we find that they usually struggle to look beyond current models and assumptions and lack clear ideas about how to work toward radically different futures.

Companies that want to increase their chances of long-term survival are incorporating three steps: envisioning, planning for, and executing on possible futures. And doing so all while the actual future is unfolding in expected and unexpected ways.

Those that pull it off are rewarded. A 2017 benchmarking report from the Strategic Foresight Research Network (SFRN) revealed that vigilant companies (those with the most mature processes for identifying, interpreting, and responding to factors that induce change) achieved 200% greater market capitalization growth and 33% higher profitability than the average, while the least mature companies experienced negative market-cap growth and had 44% lower profitability.

Looking Outside the Margins

“Most organizations lack sufficient capacity to detect, interpret, and act on the critically important but weak and ambiguous signals of fresh threats or new opportunities that emerge on the periphery of their usual business environment,” write George S. Day and Paul J. H. Schoemaker in their book Peripheral Vision.

But that’s exactly where effective future planning begins: examining what is happening outside the margins of day-to-day business as usual in order to peer into the future.

Business leaders who take this approach understand that despite the uncertainties of the future there are drivers of change that can be identified and studied and actions that can be taken to better prepare for—and influence—how events unfold.

That starts with developing foresight, typically a decade out. Ten years, most future planners agree, is the sweet spot. “It is far enough out that it gives you a bit more latitude to come up with a broader way to the future, allowing for disruption and innovation,” says Brian David Johnson, former chief futurist for Intel and current futurist in residence at Arizona State University’s Center for Science and the Imagination. “But you can still see the light from it.”

The process involves gathering information about the factors and forces—technological, business, sociological, and industry or ecosystem trends—that are effecting change to envision a range of potential impacts.

Seeing New Worlds

Intel, for example, looks beyond its own industry boundaries to envision possible future developments in adjacent businesses in the larger ecosystem it operates in. In 2008, the Intel Labs team, led by anthropologist Genevieve Bell, determined that the introduction of flexible glass displays would open up a whole new category of foldable consumer electronic devices.

To take advantage of that advance, Intel would need to be able to make silicon small enough to fit into some imagined device of the future. By the time glass manufacturer Corning unveiled its ultra-slim, flexible glass surface for mobile devices, laptops, televisions, and other displays of the future in 2012, Intel had already created design prototypes and kicked its development into higher gear. “Because we had done the future casting, we were already imagining how people might use flexible glass to create consumer devices,” says Johnson.

Because future planning relies so heavily on the quality of the input it receives, bringing in experts can elevate the practice. They can come from inside an organization, but the most influential insight may come from the outside and span a wide range of disciplines, says Steve Brown, a futurist, consultant, and CEO of BaldFuturist.com who worked for Intel Labs from 2007 to 2016.

Companies may look to sociologists or behaviorists who have insight into the needs and wants of people and how that influences their actions. Some organizations bring in an applied futurist, skilled at scanning many different forces and factors likely to coalesce in important ways (see Do You Need a Futurist?).

Do You Need a Futurist?

Most organizations need an outsider to help envision their future. Futurists are good at looking beyond the big picture to the biggest picture.

Business leaders who want to be better prepared for an uncertain and disruptive future will build future planning as a strategic capability into their organizations and create an organizational culture that embraces the approach. But working with credible futurists, at least in the beginning, can jump-start the process.

“The present can be so noisy and business leaders are so close to it that it’s helpful to provide a fresh outside-in point of view,” says veteran futurist Bob Johansen.

To put it simply, futurists like Johansen are good at connecting dots—lots of them. They look beyond the boundaries of a single company or even an industry, incorporating into their work social science, technical research, cultural movements, economic data, trends, and the input of other experts.

They can also factor in the cultural history of the specific company with whom they’re working, says Brian David Johnson, futurist in residence at Arizona State University’s Center for Science and the Imagination. “These large corporations have processes and procedures in place—typically for good reasons,” Johnson explains. “But all of those reasons have everything to do with the past and nothing to do with the future. Looking at that is important so you can understand the inertia that you need to overcome.”

One thing the best futurists will say they can’t do: predict the future. That’s not the point. “The future punishes certainty,” Johansen says, “but it rewards clarity.” The methods futurists employ are designed to trigger discussions and considerations of possibilities corporate leaders might not otherwise consider.

You don’t even necessarily have to buy into all the foresight that results, says Johansen. Many leaders don’t. “Every forecast is debatable,” Johansen says. “Foresight is a way to provoke insight, even if you don’t believe it. The value is in letting yourself be provoked.”

External expert input serves several purposes. It brings everyone up to a common level of knowledge. It can stimulate and shift the thinking of participants by introducing them to new information or ideas. And it can challenge the status quo by illustrating how people and organizations in different sectors are harnessing emerging trends.

The goal is not to come up with one definitive future but multiple possibilities—positive and negative—along with a list of the likely obstacles or accelerants that could surface on the road ahead. The result: increased clarity—rather than certainty—in the face of the unknown that enables business decision makers to execute and refine business plans and strategy over time.

Plotting the Steps Along the Way

Coming up with potential trends is an important first step in futuring, but even more critical is figuring out what steps need to be taken along the way: eight years from now, four years from now, two years from now, and now. Considerations include technologies to develop, infrastructure to deploy, talent to hire, partnerships to forge, and acquisitions to make. Without this vital step, says Brown, everybody goes back to their day jobs and the new thinking generated by future planning is wasted. To work, the future steps must be tangible, concrete, and actionable.

Organizations must build a roadmap for the desired future state that anticipates both developments and detours, complete with signals that will let them know if they’re headed in the right direction. Brown works with corporate leaders to set indicator flags to look out for on the way to the anticipated future. “If we see these flagged events occurring in the ecosystem, they help to confirm the strength of our hypothesis that a particular imagined future is likely to occur,” he explains.

For example, one of Brown’s clients envisioned two potential futures: one in which gestural interfaces took hold and another in which voice control dominated. The team set a flag to look out for early examples of the interfaces that emerged in areas such as home appliances and automobiles. “Once you saw not just Amazon Echo but also Google Home and other copycat speakers, it would increase your confidence that you were moving more towards a voice-first era rather than a gesture-first era,” Brown says. “It doesn’t mean that gesture won’t happen, but it’s less likely to be the predominant modality for communication.”

How to Keep Experiments from Being Stifled

Once organizations have a vision for the future, making it a reality requires testing ideas in the marketplace and then scaling them across the enterprise. “There’s a huge change piece involved,”
says Frank Diana, futurist and global consultant with Tata Consultancy Services, “and that’s the place where most
businesses will fall down.”

Many large firms have forgotten what it’s like to experiment in several new markets on a small scale to determine what will stick and what won’t, says René Rohrbeck, professor of strategy at the Aarhus School of Business and Social Sciences. Companies must be able to fail quickly, bring the lessons learned back in, adapt, and try again.

Lowe’s increases its chances of success by creating master narratives across a number of different areas at once, such as robotics, mixed-reality tools, on-demand manufacturing, sustainability, and startup acceleration. The lab maps components of each by expected timelines: short, medium, and long term. “From there, we’ll try to build as many of them as quickly as we can,” says Manna. “And we’re always looking for that next suite of things that we should be working on.” Along the way certain innovations, like the HoloRoom How-To, become developed enough to integrate into the larger business as part of the core strategy.

One way Lowe’s accelerates the process of deciding what is ready to scale is by being open about its nascent plans with the world. “In the past, Lowe’s would never talk about projects that weren’t at scale,” says Manna. Now the company is sharing its future plans with the media and, as a result, attracting partners that can jump-start their realization.

Seeing a Lowe’s comic about employee exoskeletons, for example, led Virginia Tech engineering professor Alan Asbeck to the retailer. He helped develop a prototype for a three-month pilot with stock employees at a Christiansburg, Virginia, store.

The high-tech suit makes it easier to move heavy objects. Employees trying out the suits are also fitted with an EEG headset that the lab incorporates into all its pilots to gauge unstated, subconscious reactions. That direct feedback on the user experience helps the company refine its innovations over time.

Make the Future Part of the Culture

Regardless of whether all the elements of its master narratives come to pass, Lowe’s has already accomplished something important: It has embedded future thinking into the culture of the company.

Companies like Lowe’s constantly scan the environment for meaningful economic, technology, and cultural changes that could impact its future assessments and plans. “They can regularly draw on future planning to answer challenges,” says Rohrbeck. “This intensive, ongoing, agile strategizing is only possible because they’ve done their homework up front and they keep it updated.”

It’s impossible to predict what’s going to happen in the future, but companies can help to shape it, says Manna of Lowe’s. “It’s really about painting a picture of a preferred future state that we can try to achieve while being flexible and capable of change as we learn things along the way.” D!


About the Authors

Dan Wellers is Global Lead, Digital Futures, at SAP.

Kai Goerlich is Chief Futurist at SAP’s Innovation Center Network.

Stephanie Overby is a Boston-based business and technology journalist.


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

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About Dan Wellers

Dan Wellers is founder and leader of Digital Futures at SAP, a strategic insights and thought leadership discipline that explores how digital technologies drive exponential change in business and society.

Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu

About Stephanie Overby

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Future Of Work 2018: 10 Predictions You Can’t Ignore

Steven Hunt

The start of winter is often referred to as the “holiday season.” But it might also be called the “prediction season.” When it comes to human capital management (HCM), most predictions tend to be variations of the same things.

A colleague and I even created a scale to rate HCM predictions based on whether they are new or just “old wine in new bottles.”  The reason HCM predictions do not change much over time is because the “H” in HCM is about people. People do not evolve as fast as technology. Consequently, the basic challenges of HCM are constant: getting the right people in the right roles and providing them with the right work environments while complying with employment laws.

The following are my “top ten” predictions about how these will change in 2018.

Workforce agility will become the most critical concept in HCM. It is often said that the only constant is change. It is now more accurate to say the only constant is an ever-accelerating rate of change. The only way companies can survive in the modern economy is to excel at adapting to changing markets, technologies, and business landscapes. This requires tapping into people’s innate capacity for learning, growth, and innovation.

Staffing will reach new levels of complexity. For over 100 years, most people interpreted “staffing” to mean hiring employees to work onsite in full-time or part-time roles.  This concept is changing due to shifting skill shortages, global labor pools, and a massive rise in virtual work and contract employment.  Staffing no longer means hiring employees.  It means finding the right mix of skills and matching them to business demands by tapping into an increasingly global, virtual, and contingent labor force. Companies will be forced to redefine workforce planning, recruiting, staffing, and management to work in this much more complex labor market.

The experience of work will greatly improve.  Technology has made a lot of things about our lives much easier and more enjoyable. Finding our way around a city, buying products, staying in touch with our friends, watching movies, and hundreds of other life experiences have been transformed by social and mobile technologies leveraging artificially intelligent interfaces and machine learning algorithms. We will see exponential growth in the use of artificial intelligence, chatbots, intelligent services, machine learning, mobile solutions, and social platforms to make work more enjoyable, simple, and engaging.

Performance management will become a solution, not a problem. People have hated performance management for decades.  This is changing thanks to companies rethinking performance management to focus on ongoing coaching and team based decision making.  We will soon reach a tipping point where the dreaded annual review will be nothing more than a painful memory, having been replaced by mobile technology enabled continuous performance management solutions that employees and managers both appreciate and like.

Re-conceptualizing compensation. Companies spend billions of dollars each year on merit increases, bonuses, and other form of compensation.  Yet few of them can confidently answer this question: “What is the return on investment you get from the money spent on compensation in terms of increased employee engagement, productivity, and retention?” Companies can tell down to the last penny how much is spent on compensation, but they cannot tell if that money is being spent wisely. The future of compensation will involve more continuous processes where employees receive different types of rewards throughout the year from different sources.  And analytics will be used to link investments in compensation to returns in workforce productivity.

Intolerance of inequity. For too long, companies have viewed inequity as a problem, but not a problem worth solving. With the workforce becoming increasingly diverse, particularly the rise of women who now represent 50 percent or more of the employees in many fields, society is reaching a long-awaited tipping point where inequitable treatment based on non-job relevant factors such as gender, ethnicity, and age is being openly acknowledged and addressed. Smart companies will proactively redesign their talent management practices to ensure bias is identified and addressed before it happens.

The rise of well-being tech. People are not meant to live in an “always on” 24-7 world.  The pace of work is literally burning people out.  Companies need employees to be highly engaged, creative, and service oriented.  But this is impossible to do if employees are tired, stressed, and distracted.  In the coming year, companies will continue to make more well-being tools available to their employees. With the explosion of well-being technology at the consumer level, such as smartwatches and fitness technology, many employers will be looking to bring these tools into the workplace.  However, successful organizations will be those who make such technologies accessible, enjoyable, and cultural for their employees.

Org charts will begin to phase out. There is a lot of talk about updating businesses for the digital age, and yet companies continue to manage work forces using a tool that has changed little since the Roman Empire: the hierarchical organization chart (“org chart”). Relying on org charts to guide workforce management decisions is both foolish and dangerous in a digitalized world. And while 2018 will not be “the year the org chart died,” some progressive organizations will begin to phase out traditional org charts for more modern, digital approaches.

Companies will ditch all-or-nothing retirement. 2018 will bring about a major shift in workplace dynamics with regards to older generations. Today, individuals are living longer, and thus working longer – past 60, 70, and even 80.  Forward-thinking organizations realize the need to keep this skilled talent in their organization, particularly as many industries face increasing skills shortages. However, this transition will also force companies to rethink jobs; for example, many positions that used to be full time will become part time.  In the coming year, organizations will begin to move away from the traditional, all-or-nothing view of retirement.

Growth in HR cybersecurity threats. Ransom ware made its main stage debut in 2017 with the WannaCry and NotPetya attacks.  In 2018, ransom ware threats will continue to proliferate.  HR systems have not historically been a major target of cyber criminals.  Unfortunately, this will change.  There will be a growing number of attacks against human resources departments, with cyber-criminals posing as potential applicants in the hopes of infecting the larger organization.

We should feel confident these trends will continue to evolve over the coming years. If there is one thing psychologists have proven over the years about predictions, it is that the best predictor of future behavior is past behavior.

For more on technology and HR, see Why (And How) Technology Is Bringing HR And The CFO Together.

This article originally appeared on Forbes SAPVoice.

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Steven Hunt

About Steven Hunt

Steven Hunt is the Senior Vice President of Customer Value at SAP. He is responsible for guiding the strategy and deployment of knowledge, tools and process improvements that increase the value customers receive from SuccessFactors & SAP Cloud software as a service solutions.