DataOps Is The Next Big Thing

Ella Brand

Data is mainstream. In today’s world, there is an explosion of data sources because of all the advancements in collection: sensors on the Internet of Things (IoT), new apps, and social media. There’s also the realization that data can be a competitive advantage and the perception of the need to democratize it.

Thought leaders of digital transformation and disruption already understand the value of proper business process modeling. Accepting that everyone wants to be data-driven, we should all recognize that data flows are nothing else but business workflows without prose.

That’s why it’s important to not just have dedicated development operations (DevOps) teams in place, as Thomas Di Giacomo wrote in his latest Digitalist blog, but also to hire enthusiastic data operations (DataOps) experts for effective collaboration between product management, data engineering, data science, and business operations. Talents are not that easy to find, and the wording of these jobs descriptions (“operations,” “process engineering”) isn’t that hot. But it needs to become sexier, and I see a chance that this is happening quite soon. Just remember the rise of the data scientist.

Besides the DataOps talents, proper solutions to tackle the challenges of DataOps will spring up like mushrooms within the next couple of years: Data pipelining, data orchestration, data governance, and policy management solutions will be necessary to work with the data wherever it is stored instead of moving it around. Doing it that way is not just pricey, but also a waste of time. Being faster is an obvious advantage: Imagine you can already see the data insights and cause action while your competitors are still figuring out when and where to move the data from one silo to another. Established companies worry: How can we ensure that sensitive data remains safe if it’s available to everyone? DataOps requires many businesses to comply with strict data governance regulations and there are definitely legitimate concerns.

Gartner is defining DataOps as a hub for collecting and distributing data, with a mandate to provide controlled access to systems of record for customer and marketing performance data, while protecting privacy, usage restrictions, and data integrity. DataOps is a way of managing data wherever it’s located, get it cleaned, versioned, transformed, enriched, and delivered. I can see one big trend that is causing the need for DataOps:

Agility. Every business is using this buzzword to express its state-of-the-art flexibly. If carried to its logical conclusion: Agile data processes are by definition never carved out of stone; the processes, the technologies, and even the frameworks should rather be questioned whenever possible and if necessary redesigned or adjusted to establish an environment that is focused on efficiency, quality, interdisciplinary and continuous improvement. We simply can’t afford to exclude data from an agile decision-making process to achieve the velocity of innovation demanded by the digital economy.

We need a new culture, a new approach; one that does for data what DevOps did for infrastructure. The goal should be to improve results by bringing together the data supplier with the data consumer and at the same time getting rid of a static data lifecycle. Let the DataOps journey begin.

Learn more

Want to learn about SAP’s approach of making your DataOps management a lot of easier and what the future Big Data warehousing is about? Then register for the upcoming Webinar on January 24 at 11:00 a.m. ET/17:00 p.m. CET. You’ll hear from Marc Hartz, product manager of SAP Data Hub. See you there!


Ella Brand

About Ella Brand

Ella Brand is the product marketing lead for SAP Data Hub with expertise and a general focus on analytics and Big Data solutions.

CRM In Today’s Ecosystem: What CIOs Need To Know

Riaz Faride

Companies these days usually choose to position themselves as an entity with purpose – a purpose reflecting customer-centricity beyond profit. They develop and commercialize their products accordingly, whether the products are architecturally interdependent or modular. In addition to market share, profitability, and earnings-per-share growth, measuring advocacy as a metric is trending, since it can be an indicator of positive customer experience. It is equally important as measuring satisfaction.

A customer relationship management (CRM) solution is an obvious choice for today’s leaders due to its capabilities of tracking the customer base and their experiences by channels and touch points. Combining CRM with a business intelligence (BI) tool adds significant value since it draws data from multiple sources and provides a business-focused analysis.

Leaping ahead with sophisticated functionality

Today’s CRM solutions are no longer limited to contact management, campaign management, lead management, deals and tasks, email and social media tracking; CRM has leaped beyond its traditional boundaries. Native capabilities or implementation readiness with marketing automation, online reputation management (ORM), and voice of the customer (VoC) solutions are some key options available for consideration by today’s leaders. This flexibility allows businesses to build relationships with unidentified viewers and their influencers, leads, customers, and even advocates of the products!

From a functionality perspective, selecting a CRM solution encompasses many criteria. These include the ability to mine, consolidate, and analyze data for better insights; scalability; high availability; intuitive and process-driven interface; mobile support, spanning the most commonly used device sizes and types; and operating systems. In this age, the need for responsive or adaptive mobile sites is paramount. All these factors are reflected through the architecture, features, and adaptability of a CRM solution. Thus, software lifecycle management and product roadmap should be evaluated during selection of a CRM solution.

Incorporating the latest technologies

CRM is being impacted by contextual customer service through chatbots. Predictive analysis of historical and live data through machine learning has influenced CRM, as well. Ditto virtual reality (VR), which allows customers to interact through software, and the Internet of Things (IoT), which greatly facilitates analysis of customers through real-time data from devices. Inversely, CRM has a meaningful impact on real-time personalization and connected experience.

CRM helps businesses look at their markets through different lenses. This allows them to offer their products that serve the “purpose” of their respective customer base: the task the customers are trying to accomplish or a problem or issue they want to resolve.

Businesses with mature products can leverage CRM and its extensions to define a winning strategy and help them determine success and failure criteria. CRM is also useful during the early phases of a company’s or product’s life, or when the future is unknown and the competitive landscape is changing. The mix of these two use cases is very common and dictates the need for a flexible, user-friendly, scalable CRM solution.

Protecting privacy and complying with regulatory mandates

While CRM in the cloud is gaining popularity exponentially, some decision-makers are still concerned about security and the privacy of customers, leads, and uncategorized users. This is understandable given the importance of compliance with data processing and privacy directives across multiple jurisdictions. In addition, IT leaders need to protect systems and data against vulnerabilities and ensure business continuity. Cloud providers can play an important consultative role during CRM planning and implementation – for example, recommending or providing managed services.

Learn more

For more information about solutions supporting customer engagement and commerce, and fully integrating marketing, commerce, sales, and service, please visit SAP Hybris.


Riaz Faride

About Riaz Faride

Riaz Faride joined SAP in 2017. Prior to this, he worked in the retail industry and had an extensive history in delivering high-value omnichannel projects. Throughout his career, Riaz has been exposed to all avenues of e-commerce, making him a subject matter expert. As a thought leader in his field, Riaz is a mentor for a number of professionals in e-commerce, omnichannel, and project management. He values ongoing learning and growth in both technical and non-technical fields.

Seven Questions To Ask Before Hiring A Managed Service Provider

Daniel Newman

There’s a lot to keep track of in today’s IT departments, and many businesses are hiring managed service providers (MSPs) to help lessen the load. But are they really necessary? And what value do they really bring to the business table? If you’re currently considering partnering with an MSP to support your company’s digital transformation, be sure to ask the following questions before signing on the dotted line.

How does this MSP help drive my business goals?

Face it: Technology is sexy. There’s a huge pressure on all businesses right now to adopt every new fad and fashion that comes along in the name of tech advancement. But at the end of the day, every investment you make in technology needs to support your specific business goals and vision to be worth the cost associated with it. Before choosing an MSP, be sure to outline your short- and long-term business objectives to ensure that the chosen MSP will help you grow both.

Is the ROI substantial?

Some companies see the immediate cost savings associated with MSPs and assume an improved bottom line is enough. I’d argue that’s only part of the equation. Lots of MSPs can save you money in the short term. But if they can’t grow with you—innovate alongside you—and help you grow in your own way, they may not be worth the investment. Be sure to research how the MSP will help you innovate and advance your business processes—creating more efficient workflows, etc.—before making a commitment.

How will the MSP help optimize, rather than just clean up our mess?

Do you have a tech mess on your hands right now? Is that what’s driving you to seek out an MSP? If so, you aren’t alone. The sheer overwhelming and exhausting task of working through the digital transformation is enough to make any business cry “uncle!” and reach out for help. Still, taking a mess off your IT team’s hands is not enough. Your MSP should be helping you make more—do more—achieve more—than you’re able to do on your own. That’s the benefit of technology—cloud—managed as a service (aaS).

How compatible is it in the long term?

The MSP you choose needs to be compatible now, but also in the long term. It needs to offer bigger and better services than what you currently require because someday you’ll be bigger and better yourself. Choosing a small company isn’t bad. But they need to have a big vision—and one you can count on into the future.

How innovative is the MSP team?

You know how fast things are moving in the digital landscape. As important as it is for our own companies to embrace continuous learning of new trends and innovations in the marketplace, it’s equally or more important for our MSPs. When we choose an MSP, we’re relying on them to take the reins on a chosen tech service. We’re counting on them to be up on changes, improvements, enhancements, and service options available in the greater tech community. We need them to be in-the-know—not just content with the services they currently offer. And, we need them to keep us informed of those new services and innovations as soon as they become available so we can start taking advantage of them if they’re a good fit.

Who is supporting me? 

There is nothing worse than partnering with an MSP, only to find that they have no help desk, their email support is a black hole, and their chatbot is always frozen. Before you partner with an MSP, be sure to understand exactly who will be supporting you. Will it be a team of trained engineers available 24/7? A team of chatbots? A call center with limited hours? How long does it take to respond or fix an issue? If possible, get references and ask them how the support has been. Don’t take the MSP’s word for it. Your company is too valuable not to do your due diligence.

What kind of SLA is available?

Last but not least, make sure you’re 100% on board with your MSP’s service-level agreement (SLA). Is it flexible? Does it hold the MSP accountable for support—in a timely manner? Does it allow you to grow, or keep you locked into a certain level of support? Or will you need a new MSP once you expand? It’s important to fully understand the finest details of the SLA before choosing your MSP partner.

Tech trends don’t become trends unless they hold at least some inherent value. MSPs can be incredibly valuable to your business if utilized well. But as with any trend, it’s possible to fall for the lure of the bandwagon, rather than following your true business goals. The questions above will help guide you in making the right—smart—MSP decision.

Additional Articles on This Topic:

This article originally appeared on Future of Work.


Daniel Newman

About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

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.



The Differences Between Machine Learning And Predictive Analytics

Shaily Kumar

Many people are confused about the specifics of machine learning and predictive analytics. Although they are both centered on efficient data processing, there are many differences.

Machine learning

Machine learning is a method of computational learning underlying most artificial intelligence (AI) applications. In ML, systems or algorithms improve themselves through data experience without relying on explicit programming. ML algorithms are wide-ranging tools capable of carrying out predictions while simultaneously learning from over trillions of observations.

Machine learning is considered a modern-day extension of predictive analytics. Efficient pattern recognition and self-learning are the backbones of ML models, which automatically evolve based on changing patterns in order to enable appropriate actions.

Many companies today depend on machine learning algorithms to better understand their clients and potential revenue opportunities. Hundreds of existing and newly developed machine learning algorithms are applied to derive high-end predictions that guide real-time decisions with less reliance on human intervention.

Business application of machine learning: employee satisfaction

One common, uncomplicated, yet successful business application of machine learning is measuring real-time employee satisfaction.

Machine learning applications can be highly complex, but one that’s both simple and very useful for business is a machine learning algorithm that compares employee satisfaction ratings to salaries. Instead of plotting a predictive satisfaction curve against salary figures for various employees, as predictive analytics would suggest, the algorithm assimilates huge amounts of random training data upon entry, and the prediction results are affected by any added training data to produce real-time accuracy and more helpful predictions.

This machine learning algorithm employs self-learning and automated recalibration in response to pattern changes in the training data, making machine learning more reliable for real-time predictions than other AI concepts. Repeatedly increasing or updating the bulk of training data guarantees better predictions.

Machine learning can also be implemented in image classification and facial recognition with deep learning and neural network techniques.

Predictive analytics

Predictive analytics can be defined as the procedure of condensing huge volumes of data into information that humans can understand and use. Basic descriptive analytic techniques include averages and counts. Descriptive analytics based on obtaining information from past events has evolved into predictive analytics, which attempts to predict the future based on historical data.

This concept applies complex techniques of classical statistics, like regression and decision trees, to provide credible answers to queries such as: ‘’How exactly will my sales be influenced by a 10% increase in advertising expenditure?’’ This leads to simulations and “what-if” analyses for users to learn more.

All predictive analytics applications involve three fundamental components:

  • Data: The effectiveness of every predictive model strongly depends on the quality of the historical data it processes.
  • Statistical modeling: Includes the various statistical techniques ranging from basic to complex functions used for the derivation of meaning, insight, and inference. Regression is the most commonly used statistical technique.
  • Assumptions: The conclusions drawn from collected and analyzed data usually assume the future will follow a pattern related to the past.

Data analysis is crucial for any business en route to success, and predictive analytics can be applied in numerous ways to enhance business productivity. These include things like marketing campaign optimization, risk assessment, market analysis, and fraud detection.

Business application of predictive analytics: marketing campaign optimization

In the past, valuable marketing campaign resources were wasted by businesses using instincts alone to try to capture market niches. Today, many predictive analytic strategies help businesses identify, engage, and secure suitable markets for their services and products, driving greater efficiency into marketing campaigns.

A clear application is using visitors’ search history and usage patterns on e-commerce websites to make product recommendations. Sites like Amazon increase their chance of sales by recommending products based on specific consumer interests. Predictive analytics now plays a vital role in the marketing operations of real estate, insurance, retail, and almost every other sector.

How machine learning and predictive analytics are related

While businesses must understand the differences between machine learning and predictive analytics, it’s just as important to know how they are related. Basically, machine learning is a predictive analytics branch. Despite having similar aims and processes, there are two main differences between them:

  • Machine learning works out predictions and recalibrates models in real-time automatically after design. Meanwhile, predictive analytics works strictly on “cause” data and must be refreshed with “change” data.
  • Unlike machine learning, predictive analytics still relies on human experts to work out and test the associations between cause and outcome.

Explore machine learning applications and AI software with SAP Leonardo.


Shaily Kumar

About Shaily Kumar

Shailendra has been on a quest to help organisations make money out of data and has generated an incremental value of over one billion dollars through analytics and cognitive processes. With a global experience of more than two decades, Shailendra has worked with a myriad of Corporations, Consulting Services and Software Companies in various industries like Retail, Telecommunications, Financial Services and Travel - to help them realise incremental value hidden in zettabytes of data. He has published multiple articles in international journals about Analytics and Cognitive Solutions; and recently published “Making Money out of Data” which showcases five business stories from various industries on how successful companies make millions of dollars in incremental value using analytics. Prior to joining SAP, Shailendra was Partner / Analytics & Cognitive Leader, Asia at IBM where he drove the cognitive business across Asia. Before joining IBM, he was the Managing Director and Analytics Lead at Accenture delivering value to its clients across Australia and New Zealand. Coming from the industry, Shailendra held key Executive positions driving analytics at Woolworths and Coles in the past. Please feel to connect on: Linkedin: Twitter: