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Collaboration Tech Development Rests On Community-Enabled Innovation

James Penfold

The most recent findings of Gallup’s 2017 “State of the American Workplace” report opened my eyes to a potential opportunity for IT developers. A dramatic shift in the workforce, once viewed as a threat to the IT function, is now an open invitation for developers to flex their innovation muscle and add value to the employee experience.

According to Gallup, the percentage of employees engaged in remote workplaces has quadrupled over the last two decades, from only nine percent in 1996 to 37% in 2016. And this trend is expected to continue to escalate; Society for Human Resources Management research indicates that 60% of companies offer employees telecommuting opportunities – a threefold increase from the 20% who offered them in 1996.

This dramatic workforce shift is bringing a broad range of collaboration tools into the world of work. Because employees are empowered to work the way they want, they are purchasing and implementing their own technology, ranging from enterprise solutions to mobile apps and small team applications. But in reality, this move just undercuts the efficiencies and performance improvements that can only be realized through development innovation.

The desire for enterprise collaboration maturity opens the door to development opportunities

Enterprise collaboration is still happening outside of the solutions and tools used to access and analyze data. And it’s done for a justifiable reason: Very few of these technologies feature components that support it. Although designed to reduce barriers to information, more often than not enterprise collaboration tools reside in a tangled web of poorly integrated business applications and impenetrable information silos.

“As the level of acceptance of social technologies has increased over the past few years, the way we think about social business has undergone rapid change,” says Vanessa Thompson, research manager, Enterprise Social Networks and Collaborative Technologies, at IDC. “This change also comes with the confluence of a number of intersecting market trends – cloud, mobile, and Big Data. This exacerbates the ways we can use these new communication and collaboration channels to connect with employees, customers, partners, and suppliers in order to meet future potential needs.”

For developers, Thompson’s observation sends an urgent message to start looking at the existing IT landscape to determine where enterprise collaboration capabilities could add value to the way people work. Better yet, this may be an excellent opportunity to break down data silos that have plagued the business environment for decades.

Community-driven innovation paves the way to data democratization

After spending most of my career surrounded by developers, I have seen firsthand why successful change does not happen in a vacuum. Developers who leverage expert content, support, and innovative technology to extend enterprise investments are more likely to drive a competitive advantage that is valued by the business.

Through a community of innovators, developers can embed complementary interfaces in existing systems and applications to support the collaboration needs of any company, department, or industry. This environment should provide flexible capabilities including:

  • Customizable work patterns to address unique business demands and enable repeatable work
  • Integration of in-context business data from native and third-party systems with work patterns through APIs that help ensure that real-time data is available for assessment and decision making
  • Capabilities that are embedded through widgets to support enterprise collaboration in existing applications
  • Development of extension applications that take advantage of the power of the cloud platform based on in-memory computing to deliver rapid analysis, storage, transformation, and rendering

Development is an important part of integrating capabilities into existing software to create engaging experiences for employees of all levels and functions. However, it’s not innovation that should be done by scratch and alone. A community of expertise, best practices, content, and tools can help developers quickly set up a foundation for enterprise collaboration and devise new ways of work that reflect the business’ culture, preferred engagement models, and digital strategy.

Get started on your enterprise collaboration initiatives. Sign up for a free SAP Jam Collaboration, developer edition, and get full access to all of the capabilities of SAP Jam.

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James Penfold

About James Penfold

James Penfold is Vice President of Business Development at SAP, responsible for the ISV and Developer Programs for SAP Jam. Prior to joining SAP, James managed the EMEA Web experience business for Akamai Technologies and was Senior Director of Applications Development (EMEA) for Oracle. James has more than 20 years’ technology leadership experience in product management, product marketing, and presales for global technology including Salesforce and Siebel Systems. James earned his BSc in Computer Science from the University of Portsmouth in conjunction with IBM.

Another Global Ransomware Attack Highlights The Need For Comprehensive Cybersecurity

Lane Leskela

Here we go again! In the aftermath of the WannaCry ransomware attack in May, on June 27, a “copycat” entity identified as Petya/Not Petya perpetrated a ransomware-style worm that exploited the known Microsoft Windows vulnerabilities EternalBlue and DoublePulsar. The EternalBlue exploit is generally believed to have been developed by the U.S. National Security Agency (NSA) and was also used by the WannaCry ransomware. As with WannaCry, this attack also affected computer systems worldwide, quickly spreading to at least 60 countries. Several large businesses, transportation networks, public utilities, and government agencies in Europe and the United States were hit.

This attack was initially focused in Ukraine and Russia. ATMs at the National Bank of Ukraine were disabled across the country, and systems used to monitor radiation at the former Chernobyl nuclear power facility were interrupted. Rosneft, the largest oil company in Russia, was also attacked. Petya/NotPetya spread like WannaCry, hitting one of the world’s largest container shipping companies, Copenhagen-based A.P. Moller-Maersk, as well as WPP in London, one of the world’s largest advertising agencies, and entities in Spain and France.

Like WannaCry, Petya/NotPetya encrypted hard drives, and the message from the attackers demanded a ransom of $300 to be paid in the form of Bitcoin. The message read, “If you see this text, then your files are no longer accessible, because they have been encrypted. Perhaps you are busy looking for a way to recover your files, but don’t waste your time. Nobody can recover your files without our decryption service.”

Differences between WannaCry and Petya/NotPetya

Petya/NotPetya was more sophisticated than the WannaCry worm in its scope, resistance to neutralization, and range of targets. This attack spread rapidly within organizations using common IT administration tools, which are not recognized as malware by typical security defenses. The Petya/NotPetya worm appeared to have hit a third-party software vendor. Such approaches, which have historically involved targeted intrusions, now appear to have spread to the large-scale global malware attack spectrum.

Unlike WannaCry, unfortunately, there is apparently no “kill switch” embedded in Petya/NotPetya. Thus, the potential to recover lost data by paying the requested ransom is clearly in doubt. The low amount of the initial ransom (which falls in the WannaCry ransom request range) and the attackers’ inability to be contacted has caused confusion over the origin and purpose of the attack. It is still not clear whether state actors or freelance blackmailers (or a combination of both) are responsible. The fact remains that the only known method for retrieving the data encrypted by Petya/NotPetya is from a backup copy.

To date, most ransomware has been able to avoid detection because these strains are zero-day exploits unknown to signature-based antivirus software. Their creators research antivirus solutions to uncover the weaknesses they can exploit to avoid discovery. Ransomware distributors generally encrypt their software to help shield it from detection.

Recommendations for broader cybersecurity protections

Obsolete versions of Microsoft Windows continue to reveal their vulnerability to these attacks. Clearly, your organization should already have or should now be taking steps to update your Windows operating systems. If you cannot eliminate outdated, unpatched Windows systems, we recommend segmenting your networks to reduce the available attack surface.

Petya/NotPetya spread within organizations using the administrative tools Windows Management Instrumentation Command-line (WMIC) and PsExec. The exploitation of these and other common IT admin tools by attackers allows malware to move undetected within networks. Their use in a widespread, automated global attack is a fresh approach. This fact underscores the urgency of implementing threat detection and response solutions and leveraging trained cybersecurity staff and experienced partners to help identify and contain the Petya/NotPetya type of attack.

In addition, frequent backups and comprehensive system recovery plans will help sustain business continuity. Critical data and programs should be backed up in a manner that will enable rapid recovery, given the expectation that we’ll continue to see new forms and unknown sources of cyber attacks. This holds true across the spectrum of cyber attacks and intrusion threats.

Your organization should continue to focus on the imminent security risks posed by third parties, review risk-management processes, and institute necessary controls that will help mitigate potential damage. To this end, the secure operations map can be a powerful tool to manage a comprehensive approach to cybersecurity.

We now face a globally interconnected digital environment that is subject to the threat of sudden and costly cyber attacks from highly sophisticated organizations. SAP’s comprehensive GRC and security solutions portfolio offers powerful tools for encryption, threat definition, identification, analysis, and protection in SAP and non-SAP systems.

For more on this topic, read Improving Security in the Aftermath of the World’s Largest Ransomware Attack and The Secret to Avoiding Hacks that Can Wipe Out Your Business.

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Lane Leskela

About Lane Leskela

Lane Leskela is Global Business Development Principal, Governance, Risk, Compliance and Security at SAP.

Co-Innovation Matters To A Digital Ecosystem

David Cruickshank

Part 1 in a 4-part series

I was recently asked to contribute as a blogger to the Digitalist Magazine, exploring trends and use cases in digital innovation and transformation. The work I am doing as lead of the SAP Co-Innovation Lab in Palo Alto explains why I was quick to opt in.

For a long time, I’ve been keen to share some use cases surrounding digital transformation projects that I believe represent the sorts of problems innovators typically face. Some of the projects I’ve helped guide to completion take advantage of integration between complementary components to forge end-to-end and other co-innovated solutions. In these projects, two or more firms come together through real openness to create useful and compelling innovation that a single firm is unlikely to do alone. My objective in this initial blog series is to help CIOs and their peers think about different approaches to tackling digital transformation projects and to learn of existing co-innovation outcomes that may well be relevant.

Consider the risk factor

For example, look at the problems you need to solve among both your current transformation projects as well as those you still need to tackle – but don’t. You hold off on an important project when you recognize a high chance for failure; yet not acting does not eliminate risk. It’s a principle described by the term “Auribus Teneo Lupum,” which refers to an unsustainable matter where risk exists regardless of action. There are many reasons for companies to embrace and champion digital transformation, but not embarking on the journey is to risk becoming an also-ran in the market.

Sharing the presumed risk through co-innovation, then, can prove to be a reasonable approach. It’s challenging to adequately address solving a complex problem when faced with limited internal resources and knowledge of technologies like artificial intelligence. How can you know how to use these technologies to extract useful business insights from something like machine sensor data? Combining mutual business interest with resources and talent from sources both internally and externally opens options for making insights actionable.

Potential limitations of the traditional approach

In most organizations, enterprise IT must securely, and to scale, deliver and manage the core applications and business systems of record and all tools essential for running the company. IT teams are often organized around key operations, data center engineering, network and server security, database management, network engineering, client applications, and help desks. Digital transformation projects may be driven across each of these various domains, and many will intersect.

Whether led from within or outside of IT, digital transformation projects pursued across any of these domains will depend on the existing technical subject-matter experts available to drive relevant project work. When the goals and scope of the project align well with the available talent, progress results. When the needs of the business exceed internal experience, what other options might exist for taking on more challenging projects other than acquiring, at some cost, the skills and expertise needed to advance?

A broader innovation ecosystem

Through a business ecosystem, there can be many ways for a company to engage with the outside world. A large multinational firm might work within its own expansive ecosystem, for example, or as a relevant technology company connected to and engaged in innovation activity across one or more ecosystems.

For me, the term digital transformation refers to more than simply digitizing processes or swapping pixels for paper. A transformed company is agile and can rapidly adapt to change in customers, markets, and industries. That said, there is ample argument for your company to look for opportunities to not only co-innovate with leading technology vendors, but to harness the co-innovation they are already doing, or capable of doing, with partners that can solve a difficult problem you now face. Wrangling with a complex problem can be difficult and involve risk from doing nothing or doing the wrong thing. Working the problem from multiple sides, leveraging a spectrum of subject-matter and domain experts to comprise a co-innovation approach, is invaluable. It can reduce overall risk, further enrich a desired solution, accelerate getting innovation deployed, and establish a precedent for solving more problems through collaboration.

Personal observation of the power of co-innovation

As a regular contributor to the Digitalist, I plan to describe in a series of blogs three different co-innovation projects focusing on the Internet of Things for analytics, cognitive computing in supply chain, and cybersecurity to serve as examples.

If you’ve spent any time as part of a team overseeing digital transformation projects, you might have been trying to collect or extract insights from thousands of remote sensors into a real-time analytics environment. Or you might be investigating the use of homomorphic encryption at scale, or trying to find a way to consume data from drones managed as a service.

Instead of doing everything alone, you might look to work with your leading suppliers in collaboration or leveraging what some proactive vendors are doing with partners today or could co-innovate. There may be teams out there trying and failing – and therefore open to a co-innovation approach. Don’t miss my future posts to learn more about why co-innovation matters.

For more insight on innovation strategies, see Three Character Traits Of A Good Innovation Manager.

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David Cruickshank

About David Cruickshank

David Cruickshank is senior director for strategy and operations for the SAP Co-Innovation Lab. He leads the lab's efforts in Silicon Valley to enable ecosystem-driven co-innovation between SAP, its partners, and customers. Additionally, he manages all operational aspects necessary to run a multimillion-dollar data center to provision private cloud infrastructures to deliver productive SAP landscapes consumed by co-innovation projects seeking a faster track to market for commercially successful innovations.

Running Future Cities on Blockchain

Dan Wellers , Raimund Gross and Ulrich Scholl

Building on the Blockchain Framework

Some experts say these seemingly far-future speculations about the possibilities of combining technologies using blockchain are actually both inevitable and imminent:


Democratizing design and manufacturing by enabling individuals and small businesses to buy, sell, share, and digitally remix products affordably while protecting intellectual property rights.
Decentralizing warehousing and logistics by combining autonomous vehicles, 3D printers, and smart contracts to optimize delivery of products and materials, and even to create them on site as needed.
Distributing commerce by mixing virtual reality, 3D scanning and printing, self-driving vehicles, and artificial intelligence into immersive, personalized, on-demand shopping experiences that still protect buyers’ personal and proprietary data.

The City of the Future

Imagine that every agency, building, office, residence, and piece of infrastructure has an entry on a blockchain used as a city’s digital ledger. This “digital twin” could transform the delivery of city services.

For example:

  • Property owners could easily monetize assets by renting rooms, selling solar power back to the grid, and more.
  • Utilities could use customer data and AIs to make energy-saving recommendations, and smart contracts to automatically adjust power usage for greater efficiency.
  • Embedded sensors could sense problems (like a water main break) and alert an AI to send a technician with the right parts, tools, and training.
  • Autonomous vehicles could route themselves to open parking spaces or charging stations, and pay for services safely and automatically.
  • Cities could improve traffic monitoring and routing, saving commuters’ time and fuel while increasing productivity.

Every interaction would be transparent and verifiable, providing more data to analyze for future improvements.


Welcome to the Next Industrial Revolution

When exponential technologies intersect and combine, transformation happens on a massive scale. It’s time to start thinking through outcomes in a disciplined, proactive way to prepare for a future we’re only just beginning to imagine.

Download the executive brief Running Future Cities on Blockchain.


Read the full article Pulling Cities Into The Future With Blockchain

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Dan Wellers

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.

Raimund Gross

About Raimund Gross

Raimund Gross is a solution architect and futurist at SAP Innovation Center Network, where he evaluates emerging technologies and trends to address the challenges of businesses arising from digitization. He is currently evaluating the impact of blockchain for SAP and our enterprise customers.

Ulrich Scholl

About Ulrich Scholl

Ulrich Scholl is Vice President of Industry Cloud and Custom Development at SAP. In this role, Ulrich discovers and implements best practices to help further the understanding and adoption of the SAP portfolio of industry cloud innovations.

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Are AI And Machine Learning Killing Analytics 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 them.

AI and machine learning defined in the context of retail

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 shared with me how he believes, “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 having 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 United States, 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 onsite employees can
  • 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:

  • U.S. 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 realized only 30-40% of the potential margin improvements and productivity growth its 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, including 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 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 analytics?

So how will AI and machine learning change retail analytics? We expect that AI and machine learning will not kill analytics as we know it, but rather give it 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 e-commerce 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.

For more information, download IDC’s “Why Retail Analytics are a Foundation for Retail Profits.

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Joerg Koesters

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.