How Data Will Save Smaller Retailers From The ‘Amazon Apocalypse’

Mario Farag

Recent news of Amazon’s plans to build a second headquarters has gained mass attention for a variety of reasons. In one respect, it could bring as many as 50,000 jobs to the chosen state. But for smaller retailers worldwide, it’s signaling the growing strength of an already formidable competitor. In fact, Forrester says that 83% of U.S. adults purchased from Amazon in 2016, and an estimated one-third of online retail spending is made through the primarily online player.

It’s safe to say that Amazon has e-commerce figured out to a (data) science. In an interview with, Amazon CEO Jeff Bezos credited some of its success to the never-ending effort to give customers what they want before they know they want it. By investing in data-driven technology such as Big Data, the Internet of Things, artificial intelligence, and machine learning, the retailer’s customer experience is improved continuously through algorithm-enabled learning.

Despite all of this success, it doesn’t mean that smaller players are giving up. According to the Oxford Economics report, “The Digital Transformation Executive Study,” sponsored by SAP, 82% of small and midsize retailers are focusing their technology investments primarily on Big Data and analytics to gain the same kind of meaningful insights that Amazon uses.

The growing role of data primes small and midsize retailers for a competitive future

An overabundance of data may be impacting every industry, but retailers are in the eye of this storm. Consumers of all generations expect an entirely new shopping experience, one of limitless choices, fingertip access to information, convenient and seamless transactions, and transparent pricing. And if these new norms are not delivered, they will find another retailer.

The more consumers search, shop, and socialize online, the more data’s role in retail decision-making will mature. To stay competitive, retailers need to invest in strategic enablers such as:

  • Digital IQ capabilities and skills across all aspects of the business
  • Technology infrastructures that handle mind-boggling volumes of data and process it as quickly as it is captured
  • Simplified and consolidated IT landscapes to free up maintenance costs and concentrate more on innovation
  • Integrated data and business process platforms for faster adoption and scale

A common belief among small and midsize retailers is that acquiring these capabilities calls for a substantial investment in multiple technologies. However, this myth couldn’t be further from the truth.

Data analytics in the cloud: A retailer’s best defense

Through an affordable cloud subscription, retailers can access a range of analytics that turns data into a scalable ecosystem of shared intelligence and insight – all without hiring specialized talent or engaging in in-depth training to maintain it. In the meeting room, at the office, on the sales floor, or in front of a customer, retailers can discover, analyze, plan, predict, and collaborate with one integrated experience designed expressly for their business.

Every decision maker can access the data and embedded analytics they need to directly guide business processes to quickly take advantage of emerging opportunities and mitigating risks head-on. They can measure physical store and online sales, shopping behavior, consumer loyalty, and transaction trends in one view, which can be more meaningful than analyzing these metrics separately. And they can do so using the mobile device of their choice to maintain that insight wherever they are.

So as much as industry analysts and market watchers like to predict the doom and gloom of an “Amazon apocalypse,” smaller retailers that embrace data analytics in the cloud will know better. They will be the ones that do more than just survive this new competitive era – they will grow, expand, and maybe (just maybe) give Amazon a run for its money.

Read the Oxford Economics report “The Transformation Imperative for Small and Midsize Retailers,” sponsored by SAP, to find out how your businesses can benefit from implementing technologies such as data analytics.

This article originally appeared on Growth Matters Network.


Mario Farag

About Mario Farag

Mario Farag is senior director of product marketing with SAP. With 15+ years of experience in the software industry, Mario has served in various functions from business operations to product management and go to market. Currently Mario is responsible for SAP’s analytics mobile strategy and HR line of business.

How IoT Is Improving Communication In The Transportation Industry

Konstanze Werle

Fleets aren’t yet comprised of autonomous vehicles, but they are headed in that direction. In the meantime, technology has advanced greatly in the transportation industry, enabling us to communicate useful information with fleets.

Digital technologies can create major advances in fleet management. What can digital technologies tell you about your company’s fleet?

Gaining information from digital technologies

Digital technologies like sensors, machine learning, and the Internet of Things (IoT) let know exactly where every truck in your fleet is and when it will arrive. You can track freight and remain updated on its condition throughout the journey, learn when trucks will require maintenance, and keep appraised of drivers’ physical condition and performance level.

Real-time predictive and geo-spatial analysis and machine learning algorithms help you understand the full supply chain better than ever before. This type of analysis can also predict the effects of transportation delays on different parts of the supply chain. This helps your company manage problems more effectively.

Applying information

Insight about your fleet provides many additional important benefits to business operations. For example, it helps support on-time delivery and offers alternate options during disruptions. It helps create safer conditions and prevents accidents, cargo losses, and other problems. These benefits translate into cost savings, improved customer satisfaction, and smoother business operations. Let’s look at how insights on fleet connectivity can be used within different business applications.

If you know that your shipment is running late, you can pass that information on to your customer so they can plan accordingly. The customer will have concrete information to help them come up with an educated plan of action. The customer can also receive an updated ETA to help the planning process.

For certain industries fleet insights can be particularly critical. In the medical field, for example, trucks carry biologic and biopharmaceutical medicine that must be kept at specific temperatures during a journey. An IDC white paper, The IoT Imperative for Consumer Industries, explains that time and temperature sensors on trucks can send temperature information throughout the trip to ensure that medicine is safe. Sensors can also confirm that drugs have not been contaminated and help prevent losses by tracking the entire journey.

In another example, trucks can send the carrier information when tire wear needs to be checked and other maintenance needs to be performed. Sensors on the truck and driver can also provide data on acceleration speeds, the driver’s heart rate, and other information. The carrier can use this information to schedule maintenance and gain insight on the truck and driver performance.

From, Craig Guillot explains how a more connected supply chain helps businesses work better. Gaining information from trucks, cargo, and drivers provides a major piece that was previously missing. This link helps to better connect information between every part of the supply chain. Guillot says, “Such connectivity will enable trucking companies to enhance existing infrastructure to streamline operations, drive efficiencies and better schedule maintenance.”

In an article for SAP, Robin Meyerhoff provides the case study of Japanese company NTT, which worked to create safer driving conditions for its buses through digital technology. With the combination of sensor-embedded vests and IoT technology, the company was able to gain information about drivers’ heart rate, nervous system responses, and other markers during travel. Through a cloud platform, the company can look at driver information together with vehicle condition, weather conditions, and traffic to improve safety.


Gaining fleet insights from digital technologies can help create improved safety, better performance, and superior communication and service to customers. Also, fleet information and analysis can minimize the impact of transportation delays on your company’s and your customer’s bottom line. Information during the shipping process provides much more control over the supply chain and improves customer service.

Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value: Accelerating Digital Transformation in Transportation.


Konstanze Werle

About Konstanze Werle

Konstanze Werle is a Director of Industries Marketing at SAP. She is a content marketing specialist with a particular focus on the travel and transportation, engineering and construction and real estate industries worldwide. Her goal is to help companies in these industries to simplify their business by sharing latest trends and innovation in their industry.

How Is IoT Driving Growth In Equipment-as-a-Service Options?

Dietmar Bohn

The Internet of Things (IoT) is poised to deliver significant growth to many industries over the next few years. Within three years, it’s expected that companies selling IoT solutions will see revenues of over $450 billion. By 2025, it’s expected that there will be 75.4 billion connected devices worldwide. This provides a strong market for growth in many industries. The manufacturing industry is no different, with opportunities to improve uptime for customers and reduce high-dollar repairs.

At the same time, digitalization and disruption are providing the opportunity for companies with revolutionary new business models to enter the market. One new business model that shows great promise is integrating IoT technology and equipment with aspects of software-as-a-service models. But how will this model work in real life, what impact will it have on the companies that use it, and what benefits will it offer across a wide range of industries? Here’s a quick look.

How is the IoT driving growth in equipment-as-a-service options?

With the advent of cloud computing, software-as-a-service became popular. Essentially, it provided users with software access for a subscription fee, with the software-providing company handling maintenance, upgrades, and security issues. This concept has grown into a wide range of IT and other areas. As an example, from another industry, Netflix provides video services as a service through a monthly subscription fee.

Now the as-a-service model is being applied to a wide range of other industries. Equipment for many industries has often used the service contract or lease model. However, these models have had their own problems. Clients often don’t catch early warning signs that the equipment is having issues. The maintenance schedule may not be appropriate to the client’s site conditions. The equipment may be more than the end user needs. For whatever reason, service contracts can be expensive on both sides.

Equipment-as-a-service that implements IoT technology benefits both sides. Let’s take a look at how it might work in a business. ABC Manufacturing is an electronics manufacturing firm that uses automated MIG welders (metal inert gas welders) to produce part of its electronics components. It has service contracts for these welders, but it is not happy with the downtime and unexpected machine failures, which cost the company money. They’re also not quite sure that the equipment is right for their needs, with the limited-axis welders making somewhat sloppy welds when they reach particular angles.

XYZ  Equipment provides the welding machines but is not happy with the number of failures that could be prevented. These failures cost a lot of time and parts to fix. The unpredictable nature of the failures means sometimes they’re paying repair technicians to sit around while paying overtime when a machine breaks down at odd hours. At the same time, they’re also losing profitability from refunds to ABC Manufacturing for downtime on their lines. They know the customer isn’t quite sure about the machinery, but they’re not quite sure what they want to be changed.

After attending an equipment conference, XYZ’s CTO comes back to the office very excited about new IoT technology and business models. He convinces XYZ’s CEO to try an experiment with ABC’s service contract. XYZ’s CTO sets up an appointment with ABC’s production director and CEO to discuss options.

At the meeting, they talk about the issues with the welders. ABC doesn’t want to invest in any significant money in machinery it isn’t sure will work for their issues, so XYZ offers to set them up with a few 7-axis welders on an equipment-as-a-service option. ABC will pay a monthly fee for the use of the machinery, based on the outcome of the machinery. If they’re not happy with the equipment, ABC can end the subscription at the end of that subscription period without any penalty. XYZ will install sensors that use IoT technology to allow them to remotely monitor the equipment. This allows XYZ to determine when preventative maintenance is needed. The advanced notice lets XYZ schedule maintenance when it makes sense for both companies. XYZ makes fewer repairs and saves money. ABC avoids risk on the equipment. Everyone is happy.

Equipment-as-a-service provides great options for both equipment manufacturers and businesses. By integrating IoT technology with equipment contracts, many companies are gaining better uptime without the heavy investment. Equipment companies are also profiting from the lower failure rate as equipment is being serviced before problems get out of control. IoT technology is expected to add between $10 and $15 trillion to the worldwide GDP by 2030. Where does your company fall with these new possibilities?

Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value: Accelerating Digital Transformation in Industrial Machinery and Components. Explore how to bring Industry 4.0 insights into your business today: Industry 4.0: What’s Next?


Dietmar Bohn

About Dietmar Bohn

Dietmar Bohn is the Vice President of Industry Cloud at SAP. He brings more than 15 years of CRM experience from both outside and inside SAP and more than 25 years of industry experience. Bohn has held different executive roles spanning CRM strategy projects, CRM implementation projects, CRM development and CRM product management. He holds degrees in Electrical Engineering and in Telecommunications.

Hack the CIO

By Thomas Saueressig, Timo Elliott, Sam Yen, and Bennett Voyles

For nerds, the weeks right before finals are a Cinderella moment. Suddenly they’re stars. Pocket protectors are fashionable; people find their jokes a whole lot funnier; Dungeons & Dragons sounds cool.

Many CIOs are enjoying this kind of moment now, as companies everywhere face the business equivalent of a final exam for a vital class they have managed to mostly avoid so far: digital transformation.

But as always, there is a limit to nerdy magic. No matter how helpful CIOs try to be, their classmates still won’t pass if they don’t learn the material. With IT increasingly central to every business—from the customer experience to the offering to the business model itself—we all need to start thinking like CIOs.

Pass the digital transformation exam, and you probably have a bright future ahead. A recent SAP-Oxford Economics study of 3,100 organizations in a variety of industries across 17 countries found that the companies that have taken the lead in digital transformation earn higher profits and revenues and have more competitive differentiation than their peers. They also expect 23% more revenue growth from their digital initiatives over the next two years—an estimate 2.5 to 4 times larger than the average company’s.

But the market is grading on a steep curve: this same SAP-Oxford study found that only 3% have completed some degree of digital transformation across their organization. Other surveys also suggest that most companies won’t be graduating anytime soon: in one recent survey of 450 heads of digital transformation for enterprises in the United States, United Kingdom, France, and Germany by technology company Couchbase, 90% agreed that most digital projects fail to meet expectations and deliver only incremental improvements. Worse: over half (54%) believe that organizations that don’t succeed with their transformation project will fail or be absorbed by a savvier competitor within four years.

Companies that are making the grade understand that unlike earlier technical advances, digital transformation doesn’t just support the business, it’s the future of the business. That’s why 60% of digital leading companies have entrusted the leadership of their transformation to their CIO, and that’s why experts say businesspeople must do more than have a vague understanding of the technology. They must also master a way of thinking and looking at business challenges that is unfamiliar to most people outside the IT department.

In other words, if you don’t think like a CIO yet, now is a very good time to learn.

However, given that you probably don’t have a spare 15 years to learn what your CIO knows, we asked the experts what makes CIO thinking distinctive. Here are the top eight mind hacks.

1. Think in Systems

A lot of businesspeople are used to seeing their organization as a series of loosely joined silos. But in the world of digital business, everything is part of a larger system.

CIOs have known for a long time that smart processes win. Whether they were installing enterprise resource planning systems or working with the business to imagine the customer’s journey, they always had to think in holistic ways that crossed traditional departmental, functional, and operational boundaries.

Unlike other business leaders, CIOs spend their careers looking across systems. Why did our supply chain go down? How can we support this new business initiative beyond a single department or function? Now supported by end-to-end process methodologies such as design thinking, good CIOs have developed a way of looking at the company that can lead to radical simplifications that can reduce cost and improve performance at the same time.

They are also used to thinking beyond temporal boundaries. “This idea that the power of technology doubles every two years means that as you’re planning ahead you can’t think in terms of a linear process, you have to think in terms of huge jumps,” says Jay Ferro, CIO of TransPerfect, a New York–based global translation firm.

No wonder the SAP-Oxford transformation study found that one of the values transformational leaders shared was a tendency to look beyond silos and view the digital transformation as a company-wide initiative.

This will come in handy because in digital transformation, not only do business processes evolve but the company’s entire value proposition changes, says Jeanne Ross, principal research scientist at the Center for Information Systems Research at the Massachusetts Institute of Technology (MIT). “It either already has or it’s going to, because digital technologies make things possible that weren’t possible before,” she explains.

2. Work in Diverse Teams

When it comes to large projects, CIOs have always needed input from a diverse collection of businesspeople to be successful. The best have developed ways to convince and cajole reluctant participants to come to the table. They seek out technology enthusiasts in the business and those who are respected by their peers to help build passion and commitment among the halfhearted.

Digital transformation amps up the urgency for building diverse teams even further. “A small, focused group simply won’t have the same breadth of perspective as a team that includes a salesperson and a service person and a development person, as well as an IT person,” says Ross.

At Lenovo, the global technology giant, many of these cross-functional teams become so used to working together that it’s hard to tell where each member originally belonged: “You can’t tell who is business or IT; you can’t tell who is product, IT, or design,” says the company’s CIO, Arthur Hu.

One interesting corollary of this trend toward broader teamwork is that talent is a priority among digital leaders: they spend more on training their employees and partners than ordinary companies, as well as on hiring the people they need, according to the SAP-Oxford Economics survey. They’re also already being rewarded for their faith in their teams: 71% of leaders say that their successful digital transformation has made it easier for them to attract and retain talent, and 64% say that their employees are now more engaged than they were before the transformation.

3. Become a Consultant

Good CIOs have long needed to be internal consultants to the business. Ever since technology moved out of the glasshouse and onto employees’ desks, CIOs have not only needed a deep understanding of the goals of a given project but also to make sure that the project didn’t stray from those goals, even after the businesspeople who had ordered the project went back to their day jobs. “Businesspeople didn’t really need to get into the details of what IT was really doing,” recalls Ferro. “They just had a set of demands and said, ‘Hey, IT, go do that.’”

Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants.

But that was then. Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants. “If you’re building a house, you don’t just disappear for six months and come back and go, ‘Oh, it looks pretty good,’” says Ferro. “You’re on that work site constantly and all of a sudden you’re looking at something, going, ‘Well, that looked really good on the blueprint, not sure it makes sense in reality. Let’s move that over six feet.’ Or, ‘I don’t know if I like that anymore.’ It’s really not much different in application development or for IT or technical projects, where on paper it looked really good and three weeks in, in that second sprint, you’re going, ‘Oh, now that I look at it, that’s really stupid.’”

4. Learn Horizontal Leadership

CIOs have always needed the ability to educate and influence other leaders that they don’t directly control. For major IT projects to be successful, they need other leaders to contribute budget, time, and resources from multiple areas of the business.

It’s a kind of horizontal leadership that will become critical for businesspeople to acquire in digital transformation. “The leadership role becomes one much more of coaching others across the organization—encouraging people to be creative, making sure everybody knows how to use data well,” Ross says.

In this team-based environment, having all the answers becomes less important. “It used to be that the best business executives and leaders had the best answers. Today that is no longer the case,” observes Gary Cokins, a technology consultant who focuses on analytics-based performance management. “Increasingly, it’s the executives and leaders who ask the best questions. There is too much volatility and uncertainty for them to rely on their intuition or past experiences.”

Many experts expect this trend to continue as the confluence of automation and data keeps chipping away at the organizational pyramid. “Hierarchical, command-and-control leadership will become obsolete,” says Edward Hess, professor of business administration and Batten executive-in-residence at the Darden School of Business at the University of Virginia. “Flatter, distributive leadership via teams will become the dominant structure.”

5. Understand Process Design

When business processes were simpler, IT could analyze the process and improve it without input from the business. But today many processes are triggered on the fly by the customer, making a seamless customer experience more difficult to build without the benefit of a larger, multifunctional team. In a highly digitalized organization like Amazon, which releases thousands of new software programs each year, IT can no longer do it all.

While businesspeople aren’t expected to start coding, their involvement in process design is crucial. One of the techniques that many organizations have adopted to help IT and businesspeople visualize business processes together is design thinking (for more on design thinking techniques, see “A Cult of Creation“).

Customers aren’t the only ones who benefit from better processes. Among the 100 companies the SAP-Oxford Economics researchers have identified as digital leaders, two-thirds say that they are making their employees’ lives easier by eliminating process roadblocks that interfere with their ability to do their jobs. Ninety percent of leaders surveyed expect to see value from these projects in the next two years alone.

6. Learn to Keep Learning

The ability to learn and keep learning has been a part of IT from the start. Since the first mainframes in the 1950s, technologists have understood that they need to keep reinventing themselves and their skills to adapt to the changes around them.

Now that’s starting to become part of other job descriptions too. Many companies are investing in teaching their employees new digital skills. One South American auto products company, for example, has created a custom-education institute that trained 20,000 employees and partner-employees in 2016. In addition to training current staff, many leading digital companies are also hiring new employees and creating new roles, such as a chief robotics officer, to support their digital transformation efforts.

Nicolas van Zeebroeck, professor of information systems and digital business innovation at the Solvay Brussels School of Economics and Management at the Free University of Brussels, says that he expects the ability to learn quickly will remain crucial. “If I had to think of one critical skill,” he explains, “I would have to say it’s the ability to learn and keep learning—the ability to challenge the status quo and question what you take for granted.”

7. Fail Smarter

Traditionally, CIOs tended to be good at thinking through tests that would allow the company to experiment with new technology without risking the entire network.

This is another unfamiliar skill that smart managers are trying to pick up. “There’s a lot of trial and error in the best companies right now,” notes MIT’s Ross. But there’s a catch, she adds. “Most companies aren’t designed for trial and error—they’re trying to avoid an error,” she says.

To learn how to do it better, take your lead from IT, where many people have already learned to work in small, innovative teams that use agile development principles, advises Ross.

For example, business managers must learn how to think in terms of a minimum viable product: build a simple version of what you have in mind, test it, and if it works start building. You don’t build the whole thing at once anymore.… It’s really important to build things incrementally,” Ross says.

Flexibility and the ability to capitalize on accidental discoveries during experimentation are more important than having a concrete project plan, says Ross. At Spotify, the music service, and CarMax, the used-car retailer, change is driven not from the center but from small teams that have developed something new. “The thing you have to get comfortable with is not having the formalized plan that we would have traditionally relied on, because as soon as you insist on that, you limit your ability to keep learning,” Ross warns.

8. Understand the True Cost—and Speed—of Data

Gut instincts have never had much to do with being a CIO; now they should have less to do with being an ordinary manager as well, as data becomes more important.

As part of that calculation, businesspeople must have the ability to analyze the value of the data that they seek. “You’ll need to apply a pinch of knowledge salt to your data,” advises Solvay’s van Zeebroeck. “What really matters is the ability not just to tap into data but to see what is behind the data. Is it a fair representation? Is it impartial?”

Increasingly, businesspeople will need to do their analysis in real time, just as CIOs have always had to manage live systems and processes. Moving toward real-time reports and away from paper-based decisions increases accuracy and effectiveness—and leaves less time for long meetings and PowerPoint presentations (let us all rejoice).

Not Every CIO Is Ready

Of course, not all CIOs are ready for these changes. Just as high school has a lot of false positives—genius nerds who turn out to be merely nearsighted—so there are many CIOs who aren’t good role models for transformation.

Success as a CIO these days requires more than delivering near-perfect uptime, says Lenovo’s Hu. You need to be able to understand the business as well. Some CIOs simply don’t have all the business skills that are needed to succeed in the transformation. Others lack the internal clout: a 2016 KPMG study found that only 34% of CIOs report directly to the CEO.

This lack of a strategic perspective is holding back digital transformation at many organizations. They approach digital transformation as a cool, one-off project: we’re going to put this new mobile app in place and we’re done. But that’s not a systematic approach; it’s an island of innovation that doesn’t join up with the other islands of innovation. In the longer term, this kind of development creates more problems than it fixes.

Such organizations are not building in the capacity for change; they’re trying to get away with just doing it once rather than thinking about how they’re going to use digitalization as a means to constantly experiment and become a better company over the long term.

As a result, in some companies, the most interesting tech developments are happening despite IT, not because of it. “There’s an alarming digital divide within many companies. Marketers are developing nimble software to give customers an engaging, personalized experience, while IT departments remain focused on the legacy infrastructure. The front and back ends aren’t working together, resulting in appealing web sites and apps that don’t quite deliver,” writes George Colony, founder, chairman, and CEO of Forrester Research, in the MIT Sloan Management Review.

Thanks to cloud computing and easier development tools, many departments are developing on their own, without IT’s support. These days, anybody with a credit card can do it.

Traditionally, IT departments looked askance at these kinds of do-it-yourself shadow IT programs, but that’s changing. Ferro, for one, says that it’s better to look at those teams not as rogue groups but as people who are trying to help. “It’s less about ‘Hey, something’s escaped,’ and more about ‘No, we just actually grew our capacity and grew our ability to innovate,’” he explains.

“I don’t like the term ‘shadow IT,’” agrees Lenovo’s Hu. “I think it’s an artifact of a very traditional CIO team. If you think of it as shadow IT, you’re out of step with reality,” he says.

The reality today is that a company needs both a strong IT department and strong digital capacities outside its IT department. If the relationship is good, the CIO and IT become valuable allies in helping businesspeople add digital capabilities without disrupting or duplicating existing IT infrastructure.

If a company already has strong digital capacities, it should be able to move forward quickly, according to Ross. But many companies are still playing catch-up and aren’t even ready to begin transforming, as the SAP-Oxford Economics survey shows.

For enterprises where business and IT are unable to get their collective act together, Ross predicts that the next few years will be rough. “I think these companies ought to panic,” she says. D!

About the Authors

Thomas Saueressig is Chief Information Officer at SAP.

Timo Elliott is an Innovation Evangelist at SAP.

Sam Yen is Chief Design Officer at SAP and Managing Director of SAP Labs.

Bennett Voyles is a Berlin-based business writer.

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: