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Fraud Attacks Often Come From Unexpected Places – Can Predictive Analytics Help?

Jerome Pugnet

Looking at companies’ experiences, of various sizes and across all industries, I think we would all agree that fraud attacks often don’t come from where one would expect! Companies still rely too much on guesswork and empiric methods while investigating potentially fraudulent transactions.

And to make things worse, fraud patterns evolve quickly and constantly. Thus, as companies put in place measures to prevent fraud, perpetrators quickly adapt and find ways to circumvent them. There’s clearly a need for better processes and tools to enhance their fraud detection and investigation.

Investigators’ experience isn’t sufficient anymore

To analyse and understand how and where fraud happens, one can’t just rely on the experience and intuitions of even the best investigators, or the analysis of standard fraud reports and basic metrics. Also, the more common analytical tools appear ineffective to scan very high and fast-growing volumes of data – where critical information to understand fraud patterns and hidden paths is buried.

Moreover, the range of data to examine to properly identify fraud trends is increasingly diverse – structured and unstructured. More than ever, fraud detection is a Big Data problem!

Fast-developing predictive technologies offer great potential for improvement

On the other hand, predictive analysis technologies are fast developing, becoming more widely available and easier to use, yet more powerful. They can help companies get deep insights into how and where fraudulent transactions originate, and analyze changing fraud patterns, in order to enhance their fraud detection strategies and adapt faster to new types of attacks.

So the combination of traditional fraud management solutions complemented by predictive analytics not only enhances capabilities to detect fraud, but also contributes to better prevention of potential future fraud. It enables a deeper, more forensic approach against fraud, helping users to improve the effectiveness of their investigations by better focusing on new types of fraud risks, and continuously updating and refining their fraud detection strategies using the data from predictive analyses.

Today’s best fraud management and predictive analytics solutions have many benefits. They:

  • Identify fraud patterns and trends more precisely: where fraud comes from, how it happens, who is involved, what areas of the business it impacts, and so on.
  • Enable going after the less known and more complex patterns and networks, and detecting earlier to minimize the damage of cleverly hidden suspicious transactions.
  • Provide the needed capabilities to analyze a wide variety and very high volume of data very fast, leveraging in-memory computing technology.
  • Help fraud investigators by reducing false alerts resulting from inadequate fraud detection mechanisms— a critical issue today for many fraud investigators as they’re faced with an excessive workload of potential alerts to analyse, and wasted efforts as many turn out to be false positives.

Can predictive analytics benefit a wider audience?

The innovation brought by predictive analytics touches many other areas of the business, and in areas such as governance, risk and compliance (GRC), its use will develop to enable better predictability of risk, increased insight in areas of control weakness, support for internal audit programs, and so on.

These multiple applications create a high demand for experts such as data analysts and specialized business analysts, but the scarcity and high cost of these resources pushes for better usability of the tools. In the area of fraud in particular, invaluable expertise resides within fraud investigation teams who don’t have these skills as their primary asset.

For them, and others, it’s important that new predictive technologies become approachable for the non-experts, and more readily consumable by their most interested audience—which is just what the latest generations of predictive technologies enable.

For more on security strategies, see Cybersecurity: Is It Time To Change Our Mindset?

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Reinventing the Fashion Business Model

Angela Schuller

If you’re a Project Runway enthusiast, you know that “in fashion, one day you’re in, and the next day, you’re out.” Host and model Heidi Klum speaks to the relevance of the contestants’ fashions on the show, but it also applies to the central theme three experts discussed on a recent SAP Game-Changers Radio broadcast titled Reinventing the Fashion Business Model.

The panelists believe that most fashion brands know they have to innovate in order to meet the needs of their customers, and the most successful ones are blurring the lines in their supply chains and weaving customer data into the fabric of their business strategies.

Branding disparities are part of the fashion retailer’s biggest challenge today. Many companies are trading products under different names, cataloguing products with different style numbers, and communicating to their customers in slightly different ways. Brands need to be close to their customers, and one of the ways to cultivate that relationship is by having digital at the core of their business models.

For Jeff Goldberg, managing director, North America retail, at Accenture, this means brands must have “consistent, harmonized information across your company [that’s] connected in a way that’s easy to communicate and collaborate with customers, suppliers, and ever more important, your associates.”

Good infrastructure isn’t just about ease in communication or in other tactical methods of connecting with the customer. It’s about getting the data to help businesses do it in the most effective way. “The more we can actually get one truth and systems and processes and data that’s simple, then tell the story, then people have more time to actually work on analyzing to come up with better ideas,” said Matt Marcotte, founder of M2 Collaborative. For him, it means businesses that invest in their infrastructure and understand the data they collect will be freed up to do more innovative things that meet their customers’ needs.

The source of one truth has to be integrated into every part of the business, from manufacturing to purchase, with the customer at the center of every decision along the way. Businesses need to have “one view” of their customers through their supply chains.

“[Brands that do] can truly unite that customer sentiment, that customer feedback on the runway, at the fashion campaign, all the way back to the supply chain so they can be much more responsive and deliver that experience much more quickly,” said Matt Laukaitis, managing director, SAP retail in the U.S. “Customers want to make sure that the brands they choose to shop with understand who they are, and the brand needs to make sure that they are extremely relevant to that customer in the moment that customer is making a decision or thinking about making a purchase.”

But what happens when the customer wants to make that purchase immediately? Brands have to be on the ready to meet that demand, especially in light of new players in  the fashion retail space that are agile enough to keep up in the world of the Primarks, Veras, and Etsy shops. At their core, businesses that are doing this are “reinventing the supply chain,” according to Laukaitis.

The profile of fashion brands that will still be around after this season to walk the runway?

“The companies that really understand their customers, that know how to use data to tell stories, to create personalization, customization, a relationship with that customer, those are the ones that will win,” said Marcotte.

Fashion brands that use data to create personalized interactions with customers will thrive

The fashion retail model will change again with developments in augmented reality, among other trends. What does that mean for fashionistas coveting a faux leather jacket from a recent runway show? The fabrics may one day soon be grown in a lab which helps get the product to the customer quicker, according to Marcotte.

Listen to the entirety of this Digital Industries: Changing the Game Radio broadcast, hosted by SAP’s Bonnie D. Graham.

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About Angela Schuller

Angela Schuller is a communications specialist at SAP.

How Predictive Analysis Is Radically Transforming Wholesale Distribution

Frank Marguier

Predictive algorithms have been available for a very long time. Initially, analytics were run within the night batch process, with results distributed only to a happy few recipients.

Those analytics were not very well received, especially in an industry like wholesale distribution, as you needed a data scientist to extract the information. The high cost of data scientists exceeded budgets in an already low-margin, B2B distribution marketplace.

Two things have fundamentally changed that make predictive analytics very attractive for wholesale distribution:

  1. The availability of real-time technology databases makes results immediate instead of relying on lengthy night batch processes
  1. The predictive model can be easily utilized without the expense of a data scientist. The model can be developed during programming, making it easy for distributors to implement analytics and bringing tremendous value to their business

Wholesale distributors work with large product volumes in their catalogs, often in the millions, and serve hundreds of thousands of customers that each require individual invoices with unique, negotiated pricing.

The greatest benefit to wholesale distributors is that they don’t need to build Big Data models as they already have analytics embedded within their business processes.

Because wholesale distributors need to invoice every customer and record every transaction, Big Data models are extremely valuable to daily operations. By comparison, the retail industry has to build loyalty programs – and rely on customers registering for them – in order to reach the same level of customer detail, and therefore may only have partial access to data.

Leveraging Big Data is a springboard for digital transformation, and here are some ways early adopters are already succeeding.

  • Predictive analytics based on recent sales orders and profiles enhances understanding between customers and sales representatives. The data allows reps to detect risk for churn at an early stage and also provides insight into new product categories to be included in upcoming orders.
  • Sales calls can be more productive. The average sales visit is 20 minutes, so a wholesale distribution sales rep may only have time to introduce three to five of the thousands of products in their catalog. Predictive analytics provides the sales rep with the three to five products that the customer is most apt to purchase.
  • After solving a customer’s problem or answering a question, customer service center operators can recommend relevant products to the customer, based on their needs and profile, then take a sales order.
  • Special promotions are being used more frequently in wholesale distribution. When introducing new categories, sales reps can leverage data about the optimal customer profiles to introduce new products and penetrate the market.
  • Certain post-sales customer behaviors can indicate a higher risk of payment failure. For example, in a wholesale construction situation, an unprecedented large order may indicate a project or company is going bankrupt in the near future.
  • Distributors can leverage Big Data to analyze which customers purchased certain products to predict future sales. This is a strong service for suppliers, who typically only have data on quantities sold, and enables them to take advantage of precision marketing and customer segmentation to improve product strategy and grow their business.

Wholesale distributors, commonly known as the “middle man,” are using Big Data and predictive analytics to create new sources of revenue and increase their margins to foster profitable growth.

New ways have emerged for data to add value to products, become the product, or even become the business. Learn more about Data – The Hidden Treasure Inside Your Business.

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Frank Marguier

About Frank Marguier

Frank Marguier joined SAP in 1997 as presales and consultant in consumer products, wholesale, and retail industries. In 2005, he was appointed a presales manager, and after for years he moved to a service sales executive, always a focus on retail and wholesale distribution. He joined wholesale distribution industry business unit in 2012 as solution expert developing strong relationships with wholesale distribution companies across Europe. Prior to joining SAP, Frank was a supply-chain manager for the AFH division, southern Europe, at Kimberly Clark. Frank has a master's degree in Technical Sciences Engineering from Mines Douai, France. He is also a CPIM graduate and volunteer in for an association of young entrepreneurs.

More Than Noise: 5 Digital Stories From 2016 That Are Bigger Than You Think

Dan Wellers, Michael Rander, Kai Göerlich, Josh Waddell, Saravana Chandran, and Stephanie Overby

These days it seems that we are witnessing waves of extreme disruption rather than incremental technology change. While some tech news stories have been just so much noise, unlikely to have long-term impact, a few are important signals of much bigger, longer-term changes afoot.

From bots to blockchains, augmented realities to human-machine convergence, a number of rapidly advancing technological capabilities hit important inflection points in 2016. We looked at five important emerging technology news stories that happened this year and the trends set in motion that will have an impact for a long time to come.

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Immersive experiences were one of three top-level trends identified by Gartner for 2016, and that was evident in the enormous popularity of Pokémon Go. While the hype may have come and gone, the immersive technologies that have been quietly advancing in the background for years are ready to boil over into the big time—and into the enterprise.

The free location-based augmented reality (AR) game took off shortly after Nintendo launched it in July, and it became the most downloaded app in Apple’s app store history in its first week, as reported by TechCrunch. Average daily usage of the app on Android devices in July 2016 exceeded that of the standard-bearers Snapchat, Instagram, and Facebook, according to SimilarWeb. Within two months, Pokémon Go had generated more than US$440 million, according to Sensor Tower.

Unlike virtual reality (VR), which immerses us in a simulated world, AR layers computer-generated information such as graphics, sound, or other data on top of our view of the real world. In the case of Pokémon Go, players venture through the physical world using a digital map to search for Pokémon characters.

The game’s instant global acceptance was a surprise. Most watching this space expected an immersive headset device like Oculus Rift or Google Cardboard to steal the headlines. But it took Pikachu and the gang to break through. Pokémon Go capitalized on a generation’s nostalgia for its childhood and harnessed the latest advancements in key AR enabling technologies such as geolocation and computer vision.

sap_q416_digital_double_feature1_images8Just as mobile technologies percolated inside companies for several years before the iPhone exploded onto the market, companies have been dabbling in AR since the beginning of the decade. IKEA created an AR catalog app in 2013 to help customers visualize how their KIVIK modular sofa, for example, would look in their living rooms. Mitsubishi Electric has been perfecting an AR application, introduced in 2011, that enables homeowners to visualize its HVAC products in their homes. Newport News Shipbuilding has launched some 30 AR projects to help the company build and maintain its vessels. Tech giants including Facebook, HP, and Apple have been snapping up immersive tech startups for some time.

The overnight success of Pokémon Go will fuel interest in and understanding of all mediated reality technology—virtual and augmented. It’s created a shorthand for describing immersive reality and could launch a wave of technology consumerization the likes of which we haven’t seen since the iPhone instigated a tsunami of smartphone usage. Enterprises would be wise to figure out the role of immersive technology sooner rather than later. “AR and VR will both be the new normal within five years,” says futurist Gerd Leonhard, noting that the biggest hurdles may be mobile bandwidth availability and concerns about sensory overload. “Pokémon is an obvious opening scene only—professional use of AR and VR will explode.”

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Blockchains, the decentralized digital ledgers of transactions that are processed by a distributed network, first made headlines as the foundation for new types of financial transactions beginning with Bitcoin in 2009. According to Greenwich Associates, financial and technology companies will invest an estimated $1 billion in blockchain technology in 2016. But, as Gartner recently pointed out, there could be even more rapid evolution and acceptance in the areas of manufacturing, government, healthcare, and education.

By the 2020s, blockchain-based systems will reduce or eliminate many points of friction for a variety of business transactions. Individuals and companies will be able to exchange a wide range of digitized or digitally represented assets and value with anyone else, according to PwC. The supervised peer-to-peer network concept “is the future,” says Leonhard.

But the most important blockchain-related news of 2016 revealed a weak link in the application of technology that is touted as an immutable record.

In theory, blockchain technology creates a highly tamper-resistant structure that makes transactions secure and verifiable through a massively distributed digital ledger. All the transactions that take place are recorded in this ledger, which lives on many computers. High-grade encryption makes it nearly impossible for someone to cheat the system.

In practice, however, blockchain-based transactions and contracts are only as good as the code that enables them.

Case in point: The DAO, one of the first major implementations of a “Decentralized Autonomous Organization” (for which the fund is named). The DAO was a crowdfunded venture capital fund using cryptocurrency for investments and run through smart contracts. The rules that govern those smart contracts, along with all financial transaction records, are maintained on the blockchain. In June, the DAO revealed that an individual exploited a vulnerability in the company’s smart contract code to take control of nearly $60 million worth of the company’s digital currency.

The fund’s investors voted to basically rewrite the smart contract code and roll back the transaction, in essence going against the intent of blockchain-based smart contracts, which are supposed to be irreversible once they self-execute.

The DAO’s experience confirmed one of the inherent risks of distributed ledger technology—and, in particular, the risk of running a very large fund autonomously through smart contracts based on blockchain technology. Smart contract code must be as error-free as possible. As Cornell University professor and hacker Emin Gün Sirer wrote in his blog, “writing a robust, secure smart contract requires extreme amounts of diligence. It’s more similar to writing code for a nuclear power reactor, than to writing loose web code.” Since smart contracts are intended to be executed irreversibly on the blockchain, their code should not be rewritten and improved over time, as software typically is. But since no code can ever be completely airtight, smart contracts may have to build in contingency plans for when weaknesses in their code are exploited.

Importantly, the incident was not a result of any inherent weakness in the blockchain or distributed ledger technology generally. It will not be the end of cryptocurrencies or smart contracts. And it’s leading to more consideration of editable blockchains, which proponents say would only be used in extraordinary circumstances, according to Technology Review.

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Application programming interfaces (APIs), the computer codes that serve as a bridge between software applications, are not traditionally a hot topic outside of coder circles. But they are critical components in much of the consumer technology we’ve all come to rely on day-to-day.

One of the most important events in API history was the introduction of such an interface for Google Maps a decade ago. The map app was so popular that everyone wanted to incorporate its capabilities into their own systems. So Google released an API that enabled developers to connect to and use the technology without having to hack into it. The result was the launch of hundreds of inventive location-enabled apps using Google technology. Today, millions of web sites and apps use Google Maps APIs, from Allstate’s GoodHome app, which shows homeowners a personalized risk assessment of their properties, to Harley-Davidson’s Ride Planner to 7-Eleven’s app for finding the nearest Slurpee.

sap_q416_digital_double_feature1_images6Ultimately, it became de rigueur for apps to open up their systems in a safe way for experimentation by others through APIs. Technology professional Kin Lane, who tracks the now enormous world of APIs, has said, “APIs bring together a unique blend of technology, business, and politics into a transparent, self-service mix that can foster innovation.”

Thus it was significant when Apple announced in June that it would open up Siri to third-party developers through an API, giving the wider world the ability to integrate Siri’s voice commands into their apps. The move came on the heels of similar decisions by Amazon, Facebook, and Microsoft, all of which have AI bots or assistants of their own. And in October, Google opened up its Google Assistant as well.

The introduction of APIs confirms that the AI technology behind these bots has matured significantly—and that a new wave of AI-based innovation is nigh.

The best way to spark that innovation is to open up AI technologies such as Siri so that coders can use them as platforms to build new apps that can more rapidly expand AI uses and capabilities. Call it the “platformication” of AI. The value will be less in the specific AI products a company introduces than in the value of the platform for innovation. And that depends on the quality of the API. The tech company that attracts the best and brightest will win. AI platforms are just beginning to emerge and the question is: Who will be the platform leader?

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In June, Swiss citizens voted on a proposal to introduce a guaranteed basic income for all of its citizens, as reported by BBC News. It was the first country to take the issue to the polls, but it won’t be the last. Discussions about the impact of both automation and the advancing gig economy on individual livelihoods are happening around the world. Other countries—including the United States—are looking at solutions to the problem. Both Finland and the Netherlands have universal guaranteed income pilots planned for next year. Meanwhile, American startup incubator Y Combinator is launching an experiment to give 100 families in Oakland, California, a minimum wage for five years with no strings attached, according to Quartz.

The world is on the verge of potential job loss at a scale and speed never seen before. The Industrial Revolution was more of an evolution, happening over more than a century. The ongoing digital revolution is happening in relative hyper speed.

No one is exactly sure how increased automation and digitization will affect the world’s workforce. One 2013 study suggests as much as 47% of the U.S workforce is at risk of being replaced by machines over the next two decades, but even a conservative estimate of 10% could have a dramatic impact, not just on workers but on society as a whole.

The proposed solution in Switzerland did not pass, in part because a major political party did not introduce it, and citizens are only beginning to consider the potential implications of digitization on their incomes. What’s more, the idea of simply guaranteeing pay runs contrary to long-held notions in many societies that humans ought to earn their keep.

Whether or not state-funded support is the answer is just one of the questions that must be answered. The votes and pilots underway make it clear that governments will have to respond with some policy measures. The question is: What will those measures be? The larger impact of mass job displacement, what future employment conditions might look like, and what the responsibilities of institutions are in ensuring that we can support ourselves are among the issues that policy makers will need to address.

New business models resulting from digitization will create some new types of roles—but those will require training and perhaps continued education. And not all of those who will be displaced will be in a position to remake their careers. Just consider taxi drivers: In the United States, about 223,000 people currently earn their living behind the wheel of a hired car. The average New York livery driver is 46 years old, according to the New York City Taxi and Limousine Commission, and no formal education is required. When self-driving cars take over, those jobs will go away and the men and women who held them may not be qualified for the new positions that emerge.

As digitization dramatically changes the constructs of commerce and work, no one is quite sure how people will be impacted. But waiting to see how it all shakes out is not a winning strategy. Companies and governments today will have to experiment with potential solutions before the severity of the problem is clear. Among the questions that will have to be answered: How can we retrain large parts of the workforce? How will we support those who fall through the cracks? Will we prioritize and fund education? Technological progress and shifting work models will continue, whether or not we plan for their consequences.

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In April, a young man, who was believed to have permanently lost feeling in and control over his hands and legs as the result of a devastating spine injury, became able to use his right hand and fingers again. He used technology that transmits his thoughts directly to his hand muscles, bypassing his injured spinal cord. Doctors implanted a computer chip into the quadriplegic’s brain two years ago and—with ongoing training and practice—he can now perform everyday tasks like pouring from a bottle and playing video games.

The system reconnected the man’s brain directly to his muscles—the first time that engineers have successfully bypassed the nervous system’s information superhighway, the spinal cord. It’s the medical equivalent of moving from wired to wireless computing.

The man has in essence become a cyborg, that term first coined in 1960 to describe “self-regulating human-machine systems.” Yet the beneficiary of this scientific advance himself said, “You’re not going to be looked on as, ‘Oh, I’m a cyborg now because I have this big huge prosthetic on the side of my arm.’ It’s something a lot more natural and intuitive to learn because I can see my own hand reacting.”

As described in IEEE Spectrum, the “neural-bypass system” records signals that the man generates when thinking about moving his hand, decodes those signals, and routes them to the electric sleeve around his arm to stimulate movement: “The result looks surprisingly simple and natural: When Burkhart thinks about picking up a bottle, he picks up the bottle. When he thinks about playing a chord in Guitar Hero, he plays the chord.”

sap_q416_digital_double_feature1_images5What seems straightforward on the surface is powered by a sophisticated algorithm that can analyze the vast amounts of data the man’s brain produces, separating important signals from noise.

The fact that engineers have begun to unlock the complex code that controls brain-body communication opens up enormous possibilities. Neural prostheses (cochlear implants) have already reversed hearing loss. Light-sensitive chips serving as artificial retinas are showing progress in restoring vision. Other researchers are exploring computer implants that can read human thoughts directly to signal an external computer to help people speak or move in new ways. “Human and machine are converging,” says Leonhard.

The National Academy of Engineering predicts that “the intersection of engineering and neuroscience promises great advances in healthcare, manufacturing, and communication.”

Burkhart spent two years in training with the computer that has helped power his arm to get this far. It’s the result of more than a decade of development in brain-computer interfaces. And it can currently be used only in the lab; researchers are working on a system for home use. But it’s a clear indication of how quickly the lines between man and machine are blurring—and it opens the door for further computerized reanimation in many new scenarios.

This fall, Switzerland hosted its first cyborg Olympics, in which disabled patients compete using the latest assistive technologies, including robot exoskeletons and brainwave-readers. Paraplegic athletes use electrical simulation systems to compete in cycling, for example. The winners are those who can control their device the best. “Instead of celebrating the human body moving under its own power,” said a recent article in the IEEE Spectrum, “the cyborg games will celebrate the strength and ingenuity of human-machine collaborations.” D!

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 the Global Lead of Digital Futures at SAP, which explores how organizations can anticipate the future impact of exponential technologies. Dan has extensive experience in technology marketing and business strategy, plus management, consulting, and sales.

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The Future Of Work Is Now

Stefan Ries

Far beyond collaboration, the digitization of work determines how we work and engage people. Technologies – such as artificial intelligence, machine learning, robotics, analytics, and cloud technologies – change the way we recruit, develop talent, and make our workforce more inclusive. They also introduce new jobs, largely with different skill set requirements. Some of the most-wanted jobs today did not exist five years ago – and many jobs we wouldn’t even imagine today will arise in the near future. Our workplace is changing at light speed.

“Beyond collaboration, the digitization of work determines how we work and engage people”

Technology accelerates the transformation of businesses and industries. We need to prepare our businesses for the future, anticipate skills requirements and workforce changes. While some of the developments are unpredictable, it is up to thought and industry leaders like us to take control and shape the future of work.

SAP Future Factor, an interactive Web series: Engaging with thought leaders about the future of work

Welcome to the SAP Future Factor Web Salon, an interactive Web series featuring perspectives of thought leaders from academia, business, and government about the workplace of the future. The series drives a continuous exchange about the impacts of digitization on organizations and shares insight on innovative practices already in place.

The inaugural episode features SAP chief human resources officer Stefan Ries and Kevin Kruse, leadership expert and author of the New York Times best-seller “We: How to Increase Performance and Profits Through Full Engagement.” The two thought leaders exchange views on the opportunities and challenges of a digitized workplace and business culture. Their discussion will touch on the rising digital workplace, new ways to collaborate, the role technology plays to foster diversity and inclusion, employee engagement, and talent development.

Choose the topics that match your needs

Tomorrow’s workplace is all about choices – and so is the format of the SAP Future Factor Web series. All episodes are fully interactive, giving you the opportunity to interact with the content of the video by choosing topics of interest to you and your business. You determine what you would like to view and learn about, and in what order.

Episode 1 features the following topics:

  • Impacts of Digitization
  • HR’s Role in a Digitized World
  • Cloud Culture
  • Business Beyond Bias
  • Man vs. Machine
  • Rise of Social Intelligence

The future is now. Engage with us in the SAP Future Factor!

We hope you will enjoy the first episode. Tell us what you think.

Are the biggest trends from the last year on your radar screen? See More Than Noise: 5 Digital Stories From 2016 That Are Bigger Than You Think.

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Stefan Ries

About Stefan Ries

Stefan Ries is Chief Human Resources Officer (CHRO), Labor Relations Director, and a member of the Executive Board of SAP SE. Stefan was born in Bavaria and raised in Constance, Germany, where he spent most of his youth. After receiving his masters of business in economics from the University of Constance in 1991, he moved to Munich. He started his career as HR Manager at Microsoft, overseeing HR duties in Austria, Switzerland, and East European countries. In July 1994, he went on to lead the HR function for Compaq Computer in Europe, Middle East, and Africa. Following the company’s acquisitions of Tandem Computers and Digital Equipment Corporation in 1999 and 2000, Stefan led the entire HR organization for Compaq in Germany. Stefan first joined SAP in 2002 and later became responsible for various HR functions, heading up the HR business partner organization and overseeing all HR functions on an operational level. To support innovation, Stefan attaches great importance to a diverse working culture. He is convinced that appreciating the differences among people, their unique backgrounds and personalities is a key success factor for SAP.