The Big Data Job Boom

John R. Platt

Everywhere you go, everything you do, you’re generating data, and so is everyone around you. big data jobYour mobile phone usage, your internet browsing behavior, the way you drive your car, the number of times you buy turkey at the grocery store…all of that data is being collected and used by companies around the world. The massive growth in this information — which has exploded in volume, velocity and variety — has given rise to a new name for a new field: Big data.

But the explosion of data has also given rise to a tremendous need for skilled professionals capable of dealing with all of this information. In fact, the numbers of people needed in big data are simply staggering. According to one new projection from McKinsey & Company, the U.S. alone faces a shortfall of 140,000 to 190,000 big data professionals in the next five years. Another recent study from Gartner suggests that 4.4 million IT jobs worldwide will be needed to support big data by 2015. That’s a lot of potential employment for the right people.

Too Much Data, Not Enough People

But where will all of these new employees come from? While some of those thousands or millions of people will likely end up working in traditional areas such as storage or infrastructure or security, experts say the data scientists that are truly needed to make sense of all of this data remain a rare breed.

“The ability to successfully harness big data requires a unique combination of skills and attributes,” says Richard Rodts, manager of global analytics academic programs at IBM. “On the technical side, it’s essential to understand how to operate analytics technology solutions to read into the data for hidden insights and build predictive models that help business decision-makers chart smarter courses for their organizations.” Beyond that, it’s important to understand the business model and culture of your company or client so you can ask the right questions of your data. And then, Rodts says, “there are the very human attributes, such as a knack for both strategic and creative thinking, the ability to collaborate with colleagues across the business, and strong communication skills that enable you to convey data-driven findings to senior decision-makers in a compelling way.”

That’s a lot of skills for a single person. As Mark A. Herschberg, CTO of Madison Logic puts it, “That combination doesn’t exactly grow on trees.”

So What Does a Big Data Person Do?

The roots of big data lie in the older, still valid term business intelligence. “Big data is just business intelligence on steroids,” says Marty Carney, CEO of WCI. “People doing BI data warehousing can do big data. They just need more experience dealing with bigger data sets and larger architectures.”

Rodts takes it a bit further. “Data scientists or analytics professionals are part digital trend-spotter and part storyteller,” he says. “These are people, teams and centers of excellence at businesses and organizations who sift through vast amounts of data to uncover insights that can yield revenue-growing opportunities, spot risks before they occur, save money, time — and even lives.”

The exact tasks for a big-data professional can vary depending on the goals at a particular company or project. “We start with a very simple question,” says Samer Forzley, VP of marketing at the data-management company Pythian. “What are you trying to achieve from a business point of view? Are you trying to save money? Are you trying to increase revenue? Do you need to create insight on the fly? Are you trying to create a condition engine on your website that will recommend other products?” Each answer has a different set of solutions, he says.

Meanwhile, a lot of the work being done in big data today isn’t directly analysis but the transition from older systems in silo, legacy databases. “The biggest enemy of big data is silo data,” says Ali Riaz, CEO of Attivio. Companies may have been collecting disparate forms of data in various silos for years, but getting the full value of that information is a step many aren’t ready to take. “When we talk about big data, we’re talking about actually pulling all of your structured and unstructured information assets together,” Riaz says. “We can’t get to the big-data goals if everyone is married to smaller data.”

Getting In

To help address the need for big data professionals, several universities around the country have added new data analytics programs. Some, like the program at the University of Tennessee, focus not just on the technology but the business side of big data. “We think it is really important that our students have the technical skills, but that they also have some business savvy and understand the importance of subject-matter expertise in deciding both how you collect the data and how you will analyze it,” says Dr. Kenneth Gilbert, head of the university’s business analytics department. Toward that end, the school’s MS in business analytics program includes concentrations on teamwork, giving presentations to managers, and related skills.

For coursework, the best place to start is with statistics, says Dr. Olly Downs, senior VP of Data Sciences at Globys, who recently helped assemble the curriculum for the new data sciences certificate program at the University of Washington. But statistics alone isn’t enough, and Downs suggests that students get to know distributed computation and programs such as Hadoop, Python and R. At that point, you can “start getting into data and visualizing it and gaining insight from it,” he says. The next step is to start to understand how to communicate and visualize the output of your data, since a key part of every data scientist’s job is getting managers to understand their conclusions.

Unlike more traditional data fields — which often specialized in a single tool — working in big data requires a broad knowledge base. “You can’t know just one tool,” says Riaz. “You have to be multifunctional. You have to be multidimensional.”

Even with the need for multidimensionality, Riaz suggests finding the big-data specialty that appeals most to you by talking to data scientists who are already in the field to see what they do. “Then you map it to who you are,” he says. “Are you an infrastructure guy, or are you a board-level guy? Do you want to interact with people? Do you want to educate? Do you want to consume? Do you want to make decisions? Do you want to enable? Do you want to drive?” He suggests talking to as many people as you can, being open to trying new things, and applying for internships. “Don’t get in a decision mode until you have finished your discovery mode.”

Once you’re in the field, it’s important to keep moving forward. “Get into a continuous learning mode,” Riaz says. “What it means to be a data scientist today is going to radically change the next time a big new technology comes your way.”

What’s Next?

Although companies area already basing more decisions than ever on data, experts say the full scope of how big data will impact business remains to be seen. “I have a colleague who compares the whole big data thing to Eisenhower’s interstate system,” Gilbert says. “It’s going to create business opportunities that people can’t even imagine at this point.”

But even with its rapid growth, big data may actually be due for a shakeup in the next few years. In part, because it is so new. “Big data is in a way not fully defined yet because it is still emerging,” Forzley says. The rapid expansion we see today could eventually cause a similar contraction as processes work themselves out – and as companies realize that they may have hired too many people. “We’re going to find efficiencies,” says Riaz, who expects the short-term projections of the number of people needed in the field to fall considerably by the end of the decade.

According to Downs, the role of data scientists will continue to evolve. “Data scientists are no longer going to just be modelers and visualizers of data,” he predicts. “They will also be creating near-product-worthy pieces of software that a software engineer can then integrate into a bigger system.”

Experts say the future of the field could bring more regulation to protect consumers’ data, but it will certainly require more security. “Now that we’re housing more sensitive information, you’re going to have to have more locks on your door and more gates around your castle and more guard dogs and policemen,” Carney says. “The securitizing of big data is going to be a huge business,” he predicts.

The biggest risk for the future of big data may be entrenched business practices that don’t yet see the value of analytics. Gilbert points at McKinsey’s study, which predicts a need not just for a few hundred thousand big-data professionals but also for 1.5 million data-savvy managers. “What is going to determine the winners and losers in the business world are the ones that learn how to use this new resource for strategic advantage,” he says.

This article originally appeared on IEEE and has been republished with permission. 

Comments

Tags:

awareness

Will AI And Machine Learning Spell The End Of Retail As We Know It?

Joerg Koesters

According to IDC, artificial intelligence (AI) is expected to become pervasive across customer journeys, supply networks, merchandizing, and marketing and commerce because it provides better insights to optimize retail execution. For example, in the next two years:

  • 40% of digital transformation initiatives will be supported by cognitive computing and AI capabilities to provide critical, on-time insights for new operating and monetization models.
  • 30% of major retailers will adopt a retail omnichannel commerce platform that integrates a data analytics layer that centrally orchestrates omnichannel capabilities.

One thing is clear: New analytic technologies are expected to radically change analytics—and retail—as we know it.

AI and machine learning defined in a retail context

AI is defined broadly as the ability of computers to mimic human thinking and logic. Machine learning is a subset of AI that focuses on how computers can learn from data without being programmed through the use of algorithms that adapt to change; in other words, they can “learn” continuously in response to new data. We’re seeing these breakthroughs now because of massive improvements in hardware (for example, GPUs and multicore processing) that can handle Big Data volumes and run deep learning algorithms needed to analyze and learn from the data. Ivano Ortis, vice president at IDC, recently predicted:

“Artificial intelligence will take analytics to the next level, and will be the foundation for retail innovation, as reported by one out of every two retailers globally. AI enables scale, automation, and unprecedented precision, and will drive customer experience innovation when applied to both hyper micro customer segmentation and contextual interaction.”

Given the capabilities of AI and machine learning, it’s easy to see how they can be powerful tools for retailers. Now computers can read and listen to data, understand and learn from it, and instantly and accurately recommend the next best action without needing to be explicitly programmed. This is a boon for retailers seeking to accurately predict demand, anticipate customer behavior, and optimize and personalize customer experiences. For example, it can be used to automate:

  • Personalized product recommendations based on data about each customer’s unique interests and buying propensity
  • The selection of additional upsell and cross-sell options that drive greater customer value
  • Chat bots that can drive intelligent and meaningful engagement with customers
  • Recommendations on additional services and offerings based on past and current buying data and customer data
  • Planogram analyses, which support in-store merchandizing by telling people what’s missing, comparing sales to shelf space, and accelerating shelf replenishment by automating reorders
  • Pricing engines used to make tailored, situational pricing decisions

Particularly in the U.S., retailers are already able to collect large volumes of transaction-based and behavioral data from their customers. And as data volumes grow and processing power improves, machine learning becomes increasingly applicable in a wider range of retail areas to further optimize business processes and drive more impactful personalized and contextual consumer experiences and products.

The transformation of retail has already begun

The impacts of AI and machine learning are already being felt. For example:

  • Retailers are predicting demand with machine learning in combination with IoT technologies to optimize store businesses and relieve workforces.
  • Advertisements are being personalized based on in-store camera detections and taking over semi-manual “clienteling” tasks of store employees.
  • Retailers can monitor wait times in checkout lines to understand store traffic and merchandising effectiveness at the individual store level—and then tailor assortments and store layouts to maximize basket size, satisfaction and sell through.
  • Systems can now recognize and predict customer behavior and improve employee productivity by turning scheduled tasks into on-demand activities.
  • Camera systems can detect the “fresh” status of perishable products before on-site employees.
  • Brick-and-mortar stores are automating operational tasks, such as setting shelf pricing, determining product assortments and mixes, and optimizing trade promotions.
  • In-store apps can tell how long a customer has been in a certain aisle and deliver targeted offers and recommendations (via his or her mobile device) based on data about data about personal consumption histories and preferences.

A recent McKinsey study provided examples that quantify the potential value of these technologies in transforming how retailers operate and compete. For example:

  • Retailer supply chain operations that have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Using data and analytics to improve merchandising including pricing, assortment, and placement optimization is leading to an additional 16% in operating margin improvement.
  • Personalizing advertising is one of the strongest use cases for machine learning today. Additional retail use cases with high potential include optimizing pricing, routing, and scheduling based on real-time data in travel and logistics, as well as optimizing merchandising strategies.

Exploiting the full value of data

Thin margins (especially in the grocery sector) and pressure from industry-leading early adopters such as Amazon and Walmart have created strong incentives to put customer data to work to improve everything from cross-selling additional products to reducing costs throughout the entire value chain.

But McKinsey has assessed that the U.S. retail sector has only realized 30-40% of the potential margin improvements and productivity growth their analysts envisioned in 2011—and a large share of the value of this growth has gone to consumers through lower prices. So thus far, only a fraction of the potential value from AI and machine learning has been realized.

According to Forbes, U.S. retailers have the potential to see a 60%+ increase in net margin and 0.5–1.0% annual productivity growth. But there are major barriers to realizing this value include lack of analytical talent and siloed data within companies.

This is where machine learning and analytics kick in. AI and machine learning can help scale the repetitive analytics tasks required to drive better leverage of the available data. When deployed on a companywide, real-time analytics platform, they can become the single source of truth that all enterprise functions rely on to make better decisions.

How will this change retail analytics?

So how will AI and machine learning change retail analytics, as they are currently defined? We expect that AI and machine learning won’t kill analytics as we know it, but rather give analytics a new and even more impactful role in driving the future of retail. For example, we anticipate that:

  • Retailers will include machine learning algorithms as an additional factor in analyzing and monitoring business outcomes in relation to machine learning algorithms.
  • They will use AI and machine learning to sharpen analytic algorithms, detect more early warning signals, anticipate trends and have accurate answers before competitors do.
  • Analytics will happen in real time and act as the glue between all areas of the business.
  • Analytics will increasingly focus on analyzing manufacturing machine behavior, not just business and consumer behavior.

Ivano Ortis at IDC authored a recent report, “Why Retail Analytics are a Foundation for Retail Profits,” in which he provides further insights on this topic. He notes how retail leaders will use new kinds of analytics to drive greater profitability, further differentiate the customer experience, and compete more effectively:

“In conclusion, commerce and technology will converge, enabling retailers to achieve short-term ROI objectives while discovering untapped demand. But implementing analytics will require coordination across key management roles and business processes up and down each retail organization. Early adopters are realizing demonstrably significant value from their initiatives—double-digit improvements in margins, same-store and ecommerce revenue, inventory positions and sell-through, and core marketing metrics. A huge opportunity awaits.

So how do you see your retail business adopting advanced analytics like AI and machine learning? I encourage you to read IDC’s report in detail, as it provides valuable insights to help you invest in—and apply—new kinds of analytics that will be essential to profitable growth.

Comments

About Joerg Koesters

Joerg Koesters is the Head of Retail Marketing and Communication at SAP. He is a Technology Marketing executive with 20 years of experience in Marketing, Sales and Consulting, Joerg has deep knowledge in retail and consumer products having worked both in the industry and in the technology sector.

Small And Midsize Companies Find Their Edge With Advanced Analytics

Nic Smith

Part 1 of “Analytics Connection for SMBs” series

Small and midsize companies are constantly under pressure to differentiate themselves in a highly disruptive environment. Every rival – from one-man startups to large conglomerates – can rewrite the competition playbook forever with one new business model, one breakthrough offering, or one creative process. It doesn’t matter if the industry is mature; the company must create and exploit value immediately to squeeze out every last drop of value from operational efficiency, data-driven insights, and revenue growth.

Small and midsize companies are well-known for pivoting and changing direction with a speed and tenacity that’s difficult for even a multi-million-dollar enterprise with unlimited resources to duplicate. However, this quality leads to significant advantages only when coupled with outcome-oriented, real-time insights made possible by the latest analytics technology.

According to the IDC Analyst Connection whitepaper “Analytics for SMBs: Sharpen Operations, Capitalize on Business Opportunities,sponsored by SAP, Ray Boggs, vice president of small and medium business research at IDC, acknowledged that “business analytics and business intelligence can inform almost every aspect of a growing company’s operations.”

Prime future success with data and advanced analytics

Whether its classic performance measurement and financial scrutiny; regular sales, costs, and profit reporting; or HR and workforce measurement, analytics can help identify areas for greater efficiency, untapped revenue generation, process improvement, and employee training. Essentially, businesses have no choice but to add advanced analytics to their digital repertoire. If we take a moment to think about the big brands that have disappeared in recent years, it is clear that their demise was the result of limited or delayed insight on the evolution of customer behavior and market dynamics.

Everything that a small and midsize company does is centered on the customer. For this reason, advanced analytics is a great fit when it comes to dissecting and truly understanding customer needs and shopping patterns with a swift, in-the-moment experience. More important, as most successful companies have shown, the business model must also leverage that information to add value to the customer experience through, for instance, micro-personalized recommendations, content, and campaigns.

A prime example is Snow Peak, an outdoor gear retailer that has grown from a single store in the mountains of Japan to a multinational enterprise with over 100 stores. The company attributes its growth to its commitment to understanding customers and offering products that closely meet their needs. However, Snow Peak realized that its use of Microsoft Excel, an outdated enterprise resource planning (ERP) system, and handwritten notes were not effective ways to share customer tastes, preferences, and buying histories with other salespeople and event planners. For example, staff may identify the right product for a customer – only to later find that the item is out of stock and miss an opportunity to achieve a sale and make a customer happy.

By adopting predictive analytics in the cloud, Snow Peak centralized, unified, and controlled fragmented information about customers, inventories, and all other aspects of the business and made this data available to executives, salespeople, and other business users in real time. Furthermore, it optimized inventories by coupling supply and demand data and keeping it up to date.

Snow Peak’s decision to scale its customer experience with advanced analytics in the cloud is one of a variety of use cases that can greatly improve the performance of a small and midsize business.

Additional applications that are just as impactful – if not, more – include:

  • Real-time collaboration: Employees, suppliers, partners, and customers can collaborate together with access to in-the-moment, accurate data, which is a critical component of keeping everyone in the value chain engaged and informed
  • Operational optimization: Companies can balance profitability, quality, and cost control with on-the-fly what-if analysis and insight acceleration through machine learning
  • Extended supply chain: Predictive analytics and the Internet of Things provide supply chain operations with the information needed to respond to ever-evolving market expectations while maintaining profitable sales and operations, demand fulfillment, response and supply planning, and inventory optimization
  • Core business processes: Emerging analytics technology – including machine learning, artificial intelligence, and blockchain – can help create a well-skilled, productive workforce; free employees from repetitive, low-value tasks; optimize supplier negotiations; and speed accurate decision making and planning

Even though small and midsize companies have fewer employees, less cash flow, smaller inventory, and less diverse product lines than their larger counterparts, the ability to know everything about themselves and their customers brings an opportunity to stay one step ahead of the competition. But the data is only as good as the business’ ability to capture, process, analyze, communicate, and act on it in a timely, efficient way. By using advanced analytics, small and midsize companies can acquire the skills and mindset needed to turn decision-making processes and strategies into transformational, leading-edge innovation.

Read the IDC Analyst Connection whitepaper “Analytics for SMBs: Sharpen Operations, Capitalize on Business Opportunities,” sponsored by SAP, to find out how businesses worldwide can benefit from business analytics. And don’t forget to check every Tuesday for new installments to our blog series “Analytics Connection for SMBs” to explore the possibilities for your company.

This article originally appeared on Growth Matters Networks.

Comments

Nic Smith

About Nic Smith

Nic leads the global product marketing organization for BI and cloud analytics at SAP. A data driven marketing leader, his experience in enterprise and business consumer marketing strategies supports customer innovation and consistently drives growth targets. Nic brings a unique blend of experience in product marketing, storytelling and narrative, field marketing, product management, digital marketing, and customer experience. Nic has a proven record of leading highly effective teams and initiatives that excite and engage audiences. Connect on LinkedIn and on twitter at @nicfish

Diving Deep Into Digital Experiences

Kai Goerlich

 

Google Cardboard VR goggles cost US$8
By 2019, immersive solutions
will be adopted in 20% of enterprise businesses
By 2025, the market for immersive hardware and software technology could be $182 billion
In 2017, Lowe’s launched
Holoroom How To VR DIY clinics

From Dipping a Toe to Fully Immersed

The first wave of virtual reality (VR) and augmented reality (AR) is here,

using smartphones, glasses, and goggles to place us in the middle of 360-degree digital environments or overlay digital artifacts on the physical world. Prototypes, pilot projects, and first movers have already emerged:

  • Guiding warehouse pickers, cargo loaders, and truck drivers with AR
  • Overlaying constantly updated blueprints, measurements, and other construction data on building sites in real time with AR
  • Building 3D machine prototypes in VR for virtual testing and maintenance planning
  • Exhibiting new appliances and fixtures in a VR mockup of the customer’s home
  • Teaching medicine with AR tools that overlay diagnostics and instructions on patients’ bodies

A Vast Sea of Possibilities

Immersive technologies leapt forward in spring 2017 with the introduction of three new products:

  • Nvidia’s Project Holodeck, which generates shared photorealistic VR environments
  • A cloud-based platform for industrial AR from Lenovo New Vision AR and Wikitude
  • A workspace and headset from Meta that lets users use their hands to interact with AR artifacts

The Truly Digital Workplace

New immersive experiences won’t simply be new tools for existing tasks. They promise to create entirely new ways of working.

VR avatars that look and sound like their owners will soon be able to meet in realistic virtual meeting spaces without requiring users to leave their desks or even their homes. With enough computing power and a smart-enough AI, we could soon let VR avatars act as our proxies while we’re doing other things—and (theoretically) do it well enough that no one can tell the difference.

We’ll need a way to signal when an avatar is being human driven in real time, when it’s on autopilot, and when it’s owned by a bot.


What Is Immersion?

A completely immersive experience that’s indistinguishable from real life is impossible given the current constraints on power, throughput, and battery life.

To make current digital experiences more convincing, we’ll need interactive sensors in objects and materials, more powerful infrastructure to create realistic images, and smarter interfaces to interpret and interact with data.

When everything around us is intelligent and interactive, every environment could have an AR overlay or VR presence, with use cases ranging from gaming to firefighting.

We could see a backlash touting the superiority of the unmediated physical world—but multisensory immersive experiences that we can navigate in 360-degree space will change what we consider “real.”


Download the executive brief Diving Deep Into Digital Experiences.


Read the full article Swimming in the Immersive Digital Experience.

Comments

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

Tags:

Jenny Dearborn: Soft Skills Will Be Essential for Future Careers

Jenny Dearborn

The Japanese culture has always shown a special reverence for its elderly. That’s why, in 1963, the government began a tradition of giving a silver dish, called a sakazuki, to each citizen who reached the age of 100 by Keiro no Hi (Respect for the Elders Day), which is celebrated on the third Monday of each September.

That first year, there were 153 recipients, according to The Japan Times. By 2016, the number had swelled to more than 65,000, and the dishes cost the already cash-strapped government more than US$2 million, Business Insider reports. Despite the country’s continued devotion to its seniors, the article continues, the government felt obliged to downgrade the finish of the dishes to silver plating to save money.

What tends to get lost in discussions about automation taking over jobs and Millennials taking over the workplace is the impact of increased longevity. In the future, people will need to be in the workforce much longer than they are today. Half of the people born in Japan today, for example, are predicted to live to 107, making their ancestors seem fragile, according to Lynda Gratton and Andrew Scott, professors at the London Business School and authors of The 100-Year Life: Living and Working in an Age of Longevity.

The End of the Three-Stage Career

Assuming that advances in healthcare continue, future generations in wealthier societies could be looking at careers lasting 65 or more years, rather than at the roughly 40 years for today’s 70-year-olds, write Gratton and Scott. The three-stage model of employment that dominates the global economy today—education, work, and retirement—will be blown out of the water.

It will be replaced by a new model in which people continually learn new skills and shed old ones. Consider that today’s most in-demand occupations and specialties did not exist 10 years ago, according to The Future of Jobs, a report from the World Economic Forum.

And the pace of change is only going to accelerate. Sixty-five percent of children entering primary school today will ultimately end up working in jobs that don’t yet exist, the report notes.

Our current educational systems are not equipped to cope with this degree of change. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is outdated by the time students graduate, the report continues.

Skills That Transcend the Job Market

Instead of treating post-secondary education as a jumping-off point for a specific career path, we may see a switch to a shorter school career that focuses more on skills that transcend a constantly shifting job market. Today, some of these skills, such as complex problem solving and critical thinking, are taught mostly in the context of broader disciplines, such as math or the humanities.

Other competencies that will become critically important in the future are currently treated as if they come naturally or over time with maturity or experience. We receive little, if any, formal training, for example, in creativity and innovation, empathy, emotional intelligence, cross-cultural awareness, persuasion, active listening, and acceptance of change. (No wonder the self-help marketplace continues to thrive!)

The three-stage model of employment that dominates the global economy today—education, work, and retirement—will be blown out of the water.

These skills, which today are heaped together under the dismissive “soft” rubric, are going to harden up to become indispensable. They will become more important, thanks to artificial intelligence and machine learning, which will usher in an era of infinite information, rendering the concept of an expert in most of today’s job disciplines a quaint relic. As our ability to know more than those around us decreases, our need to be able to collaborate well (with both humans and machines) will help define our success in the future.

Individuals and organizations alike will have to learn how to become more flexible and ready to give up set-in-stone ideas about how businesses and careers are supposed to operate. Given the rapid advances in knowledge and attendant skills that the future will bring, we must be willing to say, repeatedly, that whatever we’ve learned to that point doesn’t apply anymore.

Careers will become more like life itself: a series of unpredictable, fluid experiences rather than a tightly scripted narrative. We need to think about the way forward and be more willing to accept change at the individual and organizational levels.

Rethink Employee Training

One way that organizations can help employees manage this shift is by rethinking training. Today, overworked and overwhelmed employees devote just 1% of their workweek to learning, according to a study by consultancy Bersin by Deloitte. Meanwhile, top business leaders such as Bill Gates and Nike founder Phil Knight spend about five hours a week reading, thinking, and experimenting, according to an article in Inc. magazine.

If organizations are to avoid high turnover costs in a world where the need for new skills is shifting constantly, they must give employees more time for learning and make training courses more relevant to the future needs of organizations and individuals, not just to their current needs.

The amount of learning required will vary by role. That’s why at SAP we’re creating learning personas for specific roles in the company and determining how many hours will be required for each. We’re also dividing up training hours into distinct topics:

  • Law: 10%. This is training required by law, such as training to prevent sexual harassment in the workplace.

  • Company: 20%. Company training includes internal policies and systems.

  • Business: 30%. Employees learn skills required for their current roles in their business units.

  • Future: 40%. This is internal, external, and employee-driven training to close critical skill gaps for jobs of the future.

In the future, we will always need to learn, grow, read, seek out knowledge and truth, and better ourselves with new skills. With the support of employers and educators, we will transform our hardwired fear of change into excitement for change.

We must be able to say to ourselves, “I’m excited to learn something new that I never thought I could do or that never seemed possible before.” D!

Comments