Machine Learning: Is Citizen Data Science Real?

Richard Mooney

We hear a lot these days about the “citizen data scientist.” Everyone wants to use data science and machine learning to understand their business and automate tasks to improve efficiency. But we have a shortage of people with data science skills, so much so that salaries are high for properly qualified people. To chief data officers, it’s an attractive proposition to take people from within their business who understand data and have a strong mathematical background and convert them to data scientists through self-study and online courses.

We have a new generation of visual composition framework tools that enable a business user to visually compose pipelines of algorithms, using techniques such as R and Python selectively to solve more complex problems. These techniques can get impressive visualizations back to the user and help them understand the business using statistics.

Challenges for citizen data scientists

But there are challenges with this approach. It’s not simply a matter of choosing the best algorithm:

  1. It’s very easy for a nonprofessional to misinterpret the results of a predictive model, making decisions based on poor results. It’s very difficult for a manager to recognize it until it’s too late.
  1. They need to master numerous skillsets to maximize model accuracy:
    • They need to understand feature engineering to extract useful insights from the data by deriving variables.
    • The mechanisms needed vary across data types. Date/time is very different from ordinal and continuous variables.
    • They need to extract how these variables change over time.
    • They also need to master complex techniques to make sure the data can be handled by the chosen algorithm and that missing values are correctly dealt with.
    • They need to understand how to deploy the model into production.
  1. They also need to deploy these models to production to generate the needed ROI. They need to understand how to keep the models current on an ongoing basis and how to make sure they are accurate, not just on training data but also validation, test, and new data.

Automation makes it easier

With automation throughout the predictive lifecycle, it’s possible to avoid or simplify these challenges.

  • You can train people to use automated predictive tooling to get a good model quickly and enforce best practices for model accuracy and robustness.
  • You can give them clear guidance on how models perform and enable them to deploy successfully into a wide variety of environments.
  • In parallel, they can hone their skills using a pipeline editor to experiment with other approaches while enforcing the same standards of model debriefing.

Most importantly, this reduces the risk of making a bad decision through an inadvertent but costly error. And the cost of entry to successfully utilizing and deploying predictive analytics is lowered, making it much easier to scale.

Don’t get me wrong, you still need training to take advantage of this. You need to know how to ask the question and how to maximize the results.

Even easier insights with an analytics cloud solution

Finally, business users can take advantage of advanced analytics for business exploration without needing to use any algorithms directly. This can be deployed to normal business users. The interface is set up to give them a simple way to frame the question. The insights are displayed in ways that help the user understand what they can and can’t infer from the data.

Is citizen data science real? 

So, to answer the original question: Yes, citizen data science is real, but we should think about what is the best way to enable people of different skillsets to successfully use data science in their business. This trend will only multiply as the automation techniques and helper tools advance and continue to lower the entry bar for data science and predictive analytics.

Learn more

To learn more about this subject, see:

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Richard Mooney

About Richard Mooney

Richard is the lead product manager for the Predictive Analytics Product Portfolio including Predictive Analytics, Predictive Analytics Integrator & SAP Cloud Platform predictive services. He has 18 years experience in the software industry starting off in development and transitioning to customer facing roles including Product Management, Sales & Marketing. Richard also spent 2 years working as an innovation expert using techniques like Design Thinking, ROI Analysis and Ideation to drive customer innovation and value.

Embracing Digital Transformation: The Future Of Banking

Falk Rieker

The face of the banking industry has changed in the last few years.

Financial technology companies (fintechs) have begun disrupting the market with cryptocurrency, bitcoin, blockchain, and more. In the United Kingdom, a new breed of banks called “challenger banks” have emerged, focusing on delivering digital-only services and exceptional customer interactions. In the United Kingdom alone, there are currently more than 20 challenger banks.

Forward-thinking banks have responded to these market disruptions by expanding their in-house capabilities. Others have partnered with fintechs to develop new digital offerings. And some simply acquired their competitors.

Banking goes digital

Digital transformation looks different in every industry and every company. In general terms, it is the integration of digital technology into all areas of a business. That integration leads to fundamental changes in how the business operates and delivers value to its customers.

Banks running on a digital core can see reduced costs and streamlined processes. This end-to-end integration also helps provide a more seamless, engaging customer experience. And it makes room for further business transformation with new digital technologies like blockchain and artificial intelligence.

Going digital has also affected the banking workforce, with automation sometimes resulting in layoffs and staff reductions. But there is a growing demand for data scientists with banking experience—a skill set not easy to find in today’s market. It is time for the industry to develop a new workforce model to educate existing staff and recruit new talent.

Big Data and its impact on the customer journey

The banking industry is among the most data-driven of industries. Regulatory and insurance requirements mean banks must store many years of transaction data. The challenge is knowing how to translate that information into meaningful insights.

Big Data provides significant opportunities for banks to outshine their competition. Moving data onto a cloud platform provides a 360-degree view of every customer. This deep insight shows banks where they can provide a higher level of service and create more value. Big Data also allows the use of disruptive technologies like artificial intelligence, blockchain, and IoT to map the customer journey and gain a competitive edge.

Leveraging technology to reinvent the banking business model

New advanced technologies allow banks to strengthen customer engagement with personalized, innovative offerings. The industry already leverages IoT with mobile apps, swipe cards, ATMs, card readers, and sensors. It also provides a new opportunity for real-time asset financing.

Some banks are already using blockchain technology to transform their business processes, as it offers secure, convenient alternatives to traditional bank processes. Lately, blockchain has been in the spotlight because of its ability to reduce fraud in the financial world. Blockchain is already used in the financial instruments areas of banking, including payments (cross-border, peer-to-peer, corporate and interbank); private equity asset transfers; tracking derivative commodities; the management of trading, spending, mortgage and loan records, microfinance applications, and customer service records.

Looking at cross-border payments, for example, blockchain can be used to reduce processing time to minutes from standard times of three to six days. This elevates the customer experience to a new level with lower cost real-time transactions. Stack processes improved by blockchain include clearing networks; international transfers; clearing and settlement; auditing, reconciliation, and reporting; and asset ownership.

Other technologies, such as machine learning, can help automate manual processes, of benefit to trading, fraud management, and customer segmentation activities.

Banking on the cloud

Banks are racing to take advantage of market opportunities available through digital transformation. At the same time, they must manage the risks created by the new digital economy. There is a critical need for affordable computing platforms that provide greater agility.

There is no doubt new digital technologies are changing the banking industry. Banks that embrace innovation and adopt new technologies have unprecedented opportunities to change and improve how they provide financial services including offering the ability to:

  1. Collaborate with financial technology partners to develop digital products.
  2. Provide customers with seamless real-time, multichannel digital interactions.
  3. Simplify and optimize business processes through standardization, optimization, and adoption of cloud solutions.
  4. Build an open and agile platform that makes it easy to meet regulatory requirements.
  5. Innovate with disruptive technologies like artificial intelligence (AI), IoT, and blockchain.

Restructuring the business model and processes is critical to any bank’s successful digitalization. Leveraging innovative capabilities in a cloud deployment can not only speed up digital transformation initiatives but also deliver business-wide process improvements as well.

For more insight on digital leaders, check out the SAP Center for Business Insight report, conducted in collaboration with Oxford Economics, “SAP Digital Transformation Executive Study: 4 Ways Leaders Set Themselves Apart.”

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Falk Rieker

About Falk Rieker

Falk Rieker, Global Vice President and Global Head of the Banking Business Unit at SAP, is a senior level financial services professional and SAP veteran with over 20 years’ experience. He is responsible for leading the SAP banking solution strategy and connecting bankers with the technology they need to succeed in today´s workplace. As a thought leader in the banking space, Falk frequently speaks at international banking conferences and has been published and quoted in leading industry publications like Forbes, American Banker, IDG and Wall Street and Technology. Follow Falk on Twitter (@FalkRieker), LinkedIn, Youtube, and Instagram.

Charted: Lessons from Machine Learning Fast Learners

Michael S. Goldberg , Christopher Koch and Dan Wellers

Companies that are benefiting soonest from their investments in machine learning have gained by going all in, according to a recent global survey by the Economist Intelligence Unit (EIU) and SAP.

Among 360 executives at companies in a range of industries, 21% report that they have already seen tangible gains from implementing machine learning applications. Labeled “Fast Learners,” these companies are more likely than others that are experimenting with machine learning to have an enterprise-wide strategy, high-level leadership support, and acceptance of the technology throughout the organization.

Fast Learners are increasing their profits. Meanwhile, their experience points to potentially profound changes in how companies will be organized in the future. These companies are shifting away from outsourcing to far-flung, low-cost regions and are tapping more in-house and local people.

“One of the most interesting characteristics of the Fast Learners was their big-picture approach to machine learning,” says Kevin Plumberg, EIU managing editor. “They were more likely than other kinds of machine learners to apply its applications across the entire enterprise. This was the case whether it was a big or small company.”

“That doesn’t mean that the Fast Learners didn’t start to introduce machine learning into their business in a limited way. They scaled it out, certainly. But they were thinking about it in a more holistic way,” he added.

Strategy Comes First

As researchers such as Erik Brynjolfsson and Andrew McAfee point out, machine learning systems, in play since the 1950s, have seen their fortunes rise recently thanks to the proliferation of data, order-of-magnitude increases in algorithm quality, and robust gains in computer processing.

The EIU survey found that Fast Learners are more likely to take an enterprise-wide approach to implementing machine learning systems compared to firms that have deployed the systems but have yet to realize benefits.

The findings indicate that among the Fast Learners, machine learning is part of a larger strategy in which organizations are rethinking their business models and value propositions to customers. Fast Learners cited the following challenges less frequently than the machine learning users that have not yet seen benefits: a lack of strategic clarity, a lack of organizational leadership, and the need to counter organizational resistance to changes involved in implementing machine learning systems (see Figure 1).

According to Plumberg, these results suggest that Fast Learners have been carrying out their machine learning programs with the necessary forethought and care to manage both business process changes and employees’ adaptations to them.

About one-third (31%) of Fast Learners also credited machine learning implementations as having helped them pursue innovations in their business processes or business model. While this was not among the most frequently cited benefits, Plumberg says the association of machine learning with innovation suggests that Fast Learners recognize the strategic value of machine learning systems and how they positively influence their business.

Poised to Profit

Almost half (48%) of Fast Learners cite increased profitability as the most significant benefit from applying machine learning to their business processes, compared to 32% of executives who have not seen the same benefit but expect to by 2020. Meanwhile, cost savings, cited by 34% of Fast Learners, was the most anticipated benefit among the other executives (44%).

Another key difference between Fast Learners and other firms that have deployed machine learning is their expectations for the future. Nearly half (48%) of Fast Learners said they anticipate revenue growth of more than 6% through 2019, compared with 30% of other machine learning users (see Figure 2).

Machine learning can be a means to reshape a business model. Top executives at Pinsent Masons, a global law firm based in London, are examining how machine learning applications can restructure their client relationships by changing the traditional services-based model. The firm aims to become a provider of knowledge-based systems and turn hourly rates into licensing fees.

The Machine Learning Organization

As they apply machine learning, Fast Learners are rethinking how they source their business processes—decisions that Plumberg suggests signal potentially far-reaching changes in how companies function. Fast Learners are more likely to rely on in-house and locally sourced resources instead of outsourcing those tasks to low-cost regions around the globe, compared to other machine learning users (see Figure 3).

Overall, 74% of machine learning users expect to increase their use of in-house and locally sourced resources by 2020. Fast Learners appear to have accelerated these sourcing decisions, spending more on locally sourced resources today than others.

The correlation between deployment of machine learning and local sourcing suggests that companies want to keep a close eye on their newly automated business processes. Consider an automated customer service system that replaces a traditional call center. Customers interact with an application enabled by artificial intelligence to get help on a question or request service instead of calling into a service office to talk to a person. This is the kind of system that a company would want to keep nearby instead of shipping it overseas, says Plumberg, because the customer experience is viewed as key to competitive advantage.

The shift in sourcing priorities probably does not predict a sudden surge in onshoring, says Plumberg. Rather, it points to a slow but steady change in how companies evaluate the benefits of outsourcing: from a traditional cost-based focus to one based on business relevance and customer value.

An example at Intel illustrates the potential to use machine learning to bolster core functions like sales and marketing, Plumberg adds. Faced with limited resources for its sales and marketing organization, Intel could have considered outsourcing to support its efforts. Instead, Intel built a machine learning platform to enhance an internal process, helping its sales and marketing teams identify which resellers their customers should work with in specific vertical industries.

Moving Forward with Machine Learning

Even Fast Learners have challenges; in particular, as illustrated in Figure 1, they identified a lack of machine learning expertise both within and outside of their companies.

This is a common complaint among companies seeking data-savvy talent. To address it successfully, the capabilities needed for machine learning must be included in strategic discussions in the C-suite, Plumberg says. For companies still early in their machine learning efforts, Fast Learners engage in several behaviors worth emulating:

  • Think beyond your current business. As with any major technology initiative, you’ll pilot one function or process first. But view the project through a wide-angle lens. The effectiveness of machine learning relies partly on its ability to analyze data from, and coordinate actions across, several different enterprise functions. Machine learning–enabled improvements can lead to new business models.
  • Don’t be cowed by tech giants. The biggest technology companies may be leading the machine learning charge today, but firms of all sizes have unprecedented access to online machine learning innovators and cloud-based computing power. Small companies’ classic advantages of speed and entrepreneurialism may count for more. In fact, companies with less than US$750 million in annual revenue accounted for 69% of the Fast Learners identified in the EIU survey.
  • Don’t wait. Machine learning is moving out of the science lab and finding its way into everyday business. Companies that began testing the waters a few years ago are now moving ahead. By 2020, the gap between Fast Learners and the rest will have widened. Delaying the implementation of a machine learning strategy will likely mean falling behind. D!
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Michael S. Goldberg

About Michael S. Goldberg

Michael S. Goldberg is an independent writer and editor focusing on management and technology issues.

About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing. Share your thoughts with Chris on Twitter @Ckochster.

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.

The Blockchain Solution

By Gil Perez, Tom Raftery, Hans Thalbauer, Dan Wellers, and Fawn Fitter

In 2013, several UK supermarket chains discovered that products they were selling as beef were actually made at least partly—and in some cases, entirely—from horsemeat. The resulting uproar led to a series of product recalls, prompted stricter food testing, and spurred the European food industry to take a closer look at how unlabeled or mislabeled ingredients were finding their way into the food chain.

By 2020, a scandal like this will be eminently preventable.

The separation between bovine and equine will become immutable with Internet of Things (IoT) sensors, which will track the provenance and identity of every animal from stall to store, adding the data to a blockchain that anyone can check but no one can alter.

Food processing companies will be able to use that blockchain to confirm and label the contents of their products accordingly—down to the specific farms and animals represented in every individual package. That level of detail may be too much information for shoppers, but they will at least be able to trust that their meatballs come from the appropriate species.

The Spine of Digitalization

Keeping food safer and more traceable is just the beginning, however. Improvements in the supply chain, which have been incremental for decades despite billions of dollars of technology investments, are about to go exponential. Emerging technologies are converging to transform the supply chain from tactical to strategic, from an easily replicable commodity to a new source of competitive differentiation.

You may already be thinking about how to take advantage of blockchain technology, which makes data and transactions immutable, transparent, and verifiable (see “What Is Blockchain and How Does It Work?”). That will be a powerful tool to boost supply chain speed and efficiency—always a worthy goal, but hardly a disruptive one.

However, if you think of blockchain as the spine of digitalization and technologies such as AI, the IoT, 3D printing, autonomous vehicles, and drones as the limbs, you have a powerful supply chain body that can leapfrog ahead of its competition.

What Is Blockchain and How Does It Work?

Here’s why blockchain technology is critical to transforming the supply chain.

Blockchain is essentially a sequential, distributed ledger of transactions that is constantly updated on a global network of computers. The ownership and history of a transaction is embedded in the blockchain at the transaction’s earliest stages and verified at every subsequent stage.

A blockchain network uses vast amounts of computing power to encrypt the ledger as it’s being written. This makes it possible for every computer in the network to verify the transactions safely and transparently. The more organizations that participate in the ledger, the more complex and secure the encryption becomes, making it increasingly tamperproof.

Why does blockchain matter for the supply chain?

  • It enables the safe exchange of value without a central verifying partner, which makes transactions faster and less expensive.
  • It dramatically simplifies recordkeeping by establishing a single, authoritative view of the truth across all parties.
  • It builds a secure, immutable history and chain of custody as different parties handle the items being shipped, and it updates the relevant documentation.
  • By doing these things, blockchain allows companies to create smart contracts based on programmable business logic, which can execute themselves autonomously and thereby save time and money by reducing friction and intermediaries.

Hints of the Future

In the mid-1990s, when the World Wide Web was in its infancy, we had no idea that the internet would become so large and pervasive, nor that we’d find a way to carry it all in our pockets on small slabs of glass.

But we could tell that it had vast potential.

Today, with the combination of emerging technologies that promise to turbocharge digital transformation, we’re just beginning to see how we might turn the supply chain into a source of competitive advantage (see “What’s the Magic Combination?”).

What’s the Magic Combination?

Those who focus on blockchain in isolation will miss out on a much bigger supply chain opportunity.

Many experts believe emerging technologies will work with blockchain to digitalize the supply chain and create new business models:

  • Blockchain will provide the foundation of automated trust for all parties in the supply chain.
  • The IoT will link objects—from tiny devices to large machines—and generate data about status, locations, and transactions that will be recorded on the blockchain.
  • 3D printing will extend the supply chain to the customer’s doorstep with hyperlocal manufacturing of parts and products with IoT sensors built into the items and/or their packaging. Every manufactured object will be smart, connected, and able to communicate so that it can be tracked and traced as needed.
  • Big Data management tools will process all the information streaming in around the clock from IoT sensors.
  • AI and machine learning will analyze this enormous amount of data to reveal patterns and enable true predictability in every area of the supply chain.

Combining these technologies with powerful analytics tools to predict trends will make lack of visibility into the supply chain a thing of the past. Organizations will be able to examine a single machine across its entire lifecycle and identify areas where they can improve performance and increase return on investment. They’ll be able to follow and monitor every component of a product, from design through delivery and service. They’ll be able to trigger and track automated actions between and among partners and customers to provide customized transactions in real time based on real data.

After decades of talk about markets of one, companies will finally have the power to create them—at scale and profitably.

Amazon, for example, is becoming as much a logistics company as a retailer. Its ordering and delivery systems are so streamlined that its customers can launch and complete a same-day transaction with a push of a single IP-enabled button or a word to its ever-attentive AI device, Alexa. And this level of experimentation and innovation is bubbling up across industries.

Consider manufacturing, where the IoT is transforming automation inside already highly automated factories. Machine-to-machine communication is enabling robots to set up, provision, and unload equipment quickly and accurately with minimal human intervention. Meanwhile, sensors across the factory floor are already capable of gathering such information as how often each machine needs maintenance or how much raw material to order given current production trends.

Once they harvest enough data, businesses will be able to feed it through machine learning algorithms to identify trends that forecast future outcomes. At that point, the supply chain will start to become both automated and predictive. We’ll begin to see business models that include proactively scheduling maintenance, replacing parts just before they’re likely to break, and automatically ordering materials and initiating customer shipments.

Italian train operator Trenitalia, for example, has put IoT sensors on its locomotives and passenger cars and is using analytics and in-memory computing to gauge the health of its trains in real time, according to an article in Computer Weekly. “It is now possible to affordably collect huge amounts of data from hundreds of sensors in a single train, analyse that data in real time and detect problems before they actually happen,” Trenitalia’s CIO Danilo Gismondi told Computer Weekly.

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials.

The project, which is scheduled to be completed in 2018, will change Trenitalia’s business model, allowing it to schedule more trips and make each one more profitable. The railway company will be able to better plan parts inventories and determine which lines are consistently performing poorly and need upgrades. The new system will save €100 million a year, according to ARC Advisory Group.

New business models continue to evolve as 3D printers become more sophisticated and affordable, making it possible to move the end of the supply chain closer to the customer. Companies can design parts and products in materials ranging from carbon fiber to chocolate and then print those items in their warehouse, at a conveniently located third-party vendor, or even on the client’s premises.

In addition to minimizing their shipping expenses and reducing fulfillment time, companies will be able to offer more personalized or customized items affordably in small quantities. For example, clothing retailer Ministry of Supply recently installed a 3D printer at its Boston store that enables it to make an article of clothing to a customer’s specifications in under 90 minutes, according to an article in Forbes.

This kind of highly distributed manufacturing has potential across many industries. It could even create a market for secure manufacturing for highly regulated sectors, allowing a manufacturer to transmit encrypted templates to printers in tightly protected locations, for example.

Meanwhile, organizations are investigating ways of using blockchain technology to authenticate, track and trace, automate, and otherwise manage transactions and interactions, both internally and within their vendor and customer networks. The ability to collect data, record it on the blockchain for immediate verification, and make that trustworthy data available for any application delivers indisputable value in any business context. The supply chain will be no exception.

Blockchain Is the Change Driver

The supply chain is configured as we know it today because it’s impossible to create a contract that accounts for every possible contingency. Consider cross-border financial transfers, which are so complex and must meet so many regulations that they require a tremendous number of intermediaries to plug the gaps: lawyers, accountants, customer service reps, warehouse operators, bankers, and more. By reducing that complexity, blockchain technology makes intermediaries less necessary—a transformation that is revolutionary even when measured only in cost savings.

“If you’re selling 100 items a minute, 24 hours a day, reducing the cost of the supply chain by just $1 per item saves you more than $52.5 million a year,” notes Dirk Lonser, SAP go-to-market leader at DXC Technology, an IT services company. “By replacing manual processes and multiple peer-to-peer connections through fax or e-mail with a single medium where everyone can exchange verified information instantaneously, blockchain will boost profit margins exponentially without raising prices or even increasing individual productivity.”

But the potential for blockchain extends far beyond cost cutting and streamlining, says Irfan Khan, CEO of supply chain management consulting and systems integration firm Bristlecone, a Mahindra Group company. It will give companies ways to differentiate.

“Blockchain will let enterprises more accurately trace faulty parts or products from end users back to factories for recalls,” Khan says. “It will streamline supplier onboarding, contracting, and management by creating an integrated platform that the company’s entire network can access in real time. It will give vendors secure, transparent visibility into inventory 24×7. And at a time when counterfeiting is a real concern in multiple industries, it will make it easy for both retailers and customers to check product authenticity.”

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials. Although the key parts of the process remain the same as in today’s analog supply chain, performing them electronically with blockchain technology shortens each stage from hours or days to seconds while eliminating reams of wasteful paperwork. With goods moving that quickly, companies have ample room for designing new business models around manufacturing, service, and delivery.

Challenges on the Path to Adoption

For all this to work, however, the data on the blockchain must be correct from the beginning. The pills, produce, or parts on the delivery truck need to be the same as the items listed on the manifest at the loading dock. Every use case assumes that the data is accurate—and that will only happen when everything that’s manufactured is smart, connected, and able to self-verify automatically with the help of machine learning tuned to detect errors and potential fraud.

Companies are already seeing the possibilities of applying this bundle of emerging technologies to the supply chain. IDC projects that by 2021, at least 25% of Forbes Global 2000 (G2000) companies will use blockchain services as a foundation for digital trust at scale; 30% of top global manufacturers and retailers will do so by 2020. IDC also predicts that by 2020, up to 10% of pilot and production blockchain-distributed ledgers will incorporate data from IoT sensors.

Despite IDC’s optimism, though, the biggest barrier to adoption is the early stage level of enterprise use cases, particularly around blockchain. Currently, the sole significant enterprise blockchain production system is the virtual currency Bitcoin, which has unfortunately been tainted by its associations with speculation, dubious financial transactions, and the so-called dark web.

The technology is still in a sufficiently early stage that there’s significant uncertainty about its ability to handle the massive amounts of data a global enterprise supply chain generates daily. Never mind that it’s completely unregulated, with no global standard. There’s also a critical global shortage of experts who can explain emerging technologies like blockchain, the IoT, and machine learning to nontechnology industries and educate organizations in how the technologies can improve their supply chain processes. Finally, there is concern about how blockchain’s complex algorithms gobble computing power—and electricity (see “Blockchain Blackouts”).

Blockchain Blackouts

Blockchain is a power glutton. Can technology mediate the issue?

A major concern today is the enormous carbon footprint of the networks creating and solving the algorithmic problems that keep blockchains secure. Although virtual currency enthusiasts claim the problem is overstated, Michael Reed, head of blockchain technology for Intel, has been widely quoted as saying that the energy demands of blockchains are a significant drain on the world’s electricity resources.

Indeed, Wired magazine has estimated that by July 2019, the Bitcoin network alone will require more energy than the entire United States currently uses and that by February 2020 it will use as much electricity as the entire world does today.

Still, computing power is becoming more energy efficient by the day and sticking with paperwork will become too slow, so experts—Intel’s Reed among them—consider this a solvable problem.

“We don’t know yet what the market will adopt. In a decade, it might be status quo or best practice, or it could be the next Betamax, a great technology for which there was no demand,” Lonser says. “Even highly regulated industries that need greater transparency in the entire supply chain are moving fairly slowly.”

Blockchain will require acceptance by a critical mass of companies, governments, and other organizations before it displaces paper documentation. It’s a chicken-and-egg issue: multiple companies need to adopt these technologies at the same time so they can build a blockchain to exchange information, yet getting multiple companies to do anything simultaneously is a challenge. Some early initiatives are already underway, though:

  • A London-based startup called Everledger is using blockchain and IoT technology to track the provenance, ownership, and lifecycles of valuable assets. The company began by tracking diamonds from mine to jewelry using roughly 200 different characteristics, with a goal of stopping both the demand for and the supply of “conflict diamonds”—diamonds mined in war zones and sold to finance insurgencies. It has since expanded to cover wine, artwork, and other high-value items to prevent fraud and verify authenticity.
  • In September 2017, SAP announced the creation of its SAP Leonardo Blockchain Co-Innovation program, a group of 27 enterprise customers interested in co-innovating around blockchain and creating business buy-in. The diverse group of participants includes management and technology services companies Capgemini and Deloitte, cosmetics company Natura Cosméticos S.A., and Moog Inc., a manufacturer of precision motion control systems.
  • Two of Europe’s largest shipping ports—Rotterdam and Antwerp—are working on blockchain projects to streamline interaction with port customers. The Antwerp terminal authority says eliminating paperwork could cut the costs of container transport by as much as 50%.
  • The Chinese online shopping behemoth Alibaba is experimenting with blockchain to verify the authenticity of food products and catch counterfeits before they endanger people’s health and lives.
  • Technology and transportation executives have teamed up to create the Blockchain in Transport Alliance (BiTA), a forum for developing blockchain standards and education for the freight industry.

It’s likely that the first blockchain-based enterprise supply chain use case will emerge in the next year among companies that see it as an opportunity to bolster their legal compliance and improve business processes. Once that happens, expect others to follow.

Customers Will Expect Change

It’s only a matter of time before the supply chain becomes a competitive driver. The question for today’s enterprises is how to prepare for the shift. Customers are going to expect constant, granular visibility into their transactions and faster, more customized service every step of the way. Organizations will need to be ready to meet those expectations.

If organizations have manual business processes that could never be automated before, now is the time to see if it’s possible. Organizations that have made initial investments in emerging technologies are looking at how their pilot projects are paying off and where they might extend to the supply chain. They are starting to think creatively about how to combine technologies to offer a product, service, or business model not possible before.

A manufacturer will load a self-driving truck with a 3D printer capable of creating a customer’s ordered item en route to delivering it. A vendor will capture the market for a socially responsible product by allowing its customers to track the product’s production and verify that none of its subcontractors use slave labor. And a supermarket chain will win over customers by persuading them that their choice of supermarket is also a choice between being certain of what’s in their food and simply hoping that what’s on the label matches what’s inside.

At that point, a smart supply chain won’t just be a competitive edge. It will become a competitive necessity. D!


About the Authors

Gil Perez is Senior Vice President, Internet of Things and Digital Supply Chain, at SAP.

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

Hans Thalbauer is Senior Vice President, Internet of Things and Digital Supply Chain, at SAP.

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

Fawn Fitter is a freelance writer specializing in business and technology.

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

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The “Purpose” Of Data

Timo Elliott

I’ve always been passionate about the ability of data and analytics to transform the world.

It has always seemed to me to be the closest thing we have to modern-day magic, with its ability to conjure up benefits from thin air. Over the last quarter century, I’ve had the honor of working with thousands of “wizards” in organizations around the world, turning information into value in every aspect of our daily lives.

The projects have been as simple as Disney using real-time analytics to move staff from one store to another to keep lines to a minimum: shorter lines led to bigger profits (you’re more likely to buy that Winnie-the-Pooh bear if there’s only one person ahead of you), but also higher customer satisfaction and happier children.

Or they’ve been as complex as the Port of Hamburg: constrained by its urban location, it couldn’t expand to meet the growing volume of traffic. But better use of information meant it was able to dramatically increase throughput – while improving the life of city residents with reduced pollution (less truck idling) and fewer traffic jams (smart lighting that automatically adapts to bridge closures).

I’ve seen analytics used to figure out why cheese was curdling in Wisconsin; count the number of bubbles in Champagne; keep track of excessive fouls in Swiss soccer, track bear sightings in Canada; avoid flooding in Argentina; detect chewing-gum-blocked metro machines in Brussels; uncover networks of tax fraud in Australia; stop trains from being stranded in the middle of the Tuscan countryside; find air travelers exposed to radioactive substances; help abused pets find new homes; find the best people to respond to hurricanes and other disasters; and much, much more.

The reality is that there’s a lot of inefficiency in the world. Most of the time it’s invisible, or we take it for granted. But analytics can help us shine a light on what’s going on, expose the problems, and show us what we can do better – in almost every area of human endeavor.

Data is a powerful weapon. Analytics isn’t just an opportunity to reduce costs and increase profits – it’s an opportunity to make the world a better place.

So to paraphrase a famous world leader, next time you embark on a new project:

“Ask not what you can do with your data, ask what your data can do for the world.”

What are your favorite “magical” examples, where analytics helped create win/win/win situations?

Download our free eBook for more insight on How the Port of Hamburg Doubled Capacity with Digitization.

This article originally appeared on Digital Business & Business Analytics.

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Timo Elliott

About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in publications such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics.