How IoT Analytics And Strategy Boosts Manufacturing

Geetika Tripathi

After witnessing three industrial transformations kindled by steam power, electricity, and IT, the world is all set to usher in the fourth transformation, known as Industry 4.0. Industry 4.0 blends computers and automation (i.e. cyber-physical systems) and the Internet of Things (IoT) meshed with data and services to reshape manufacturing and give rise to smart factories.

We are early in the Internet of Things era, where cyber-physical systems interact seamlessly with the Web in unimaginable ways, without human intervention, in real time. Industry 4.0 depends on IoT gadgets and machines that can communicate and help us interact, and it will need a lot of data for input.

Machines talking to each other, discovering and analyzing issues in products in advance, automating assembly lines, and requiring minimum human supervision. What is the outcome of the mammoth amount of data, information, and complex processes spawned by various sensors, systems, machines, and shop floors? The answer lies ahead.

The significance of Big Data, IoT, and advanced analytics

Clive Humby, co-founder of retail data science company Dunnhumby, remarked “data is the new oil.” We are in an age where data has become more precious than anything. In today’s digital economy, analytics is the magic lever that turns crude data into the gasoline of consequential insights. The potential of large sets of data can be realized only when it generates significant, actionable insights.

We create technologies to increase the efficiency of work for greater profitability at a lower cost. Advanced Big Data and IoT analytics can bring a discernible value to the manufacturing table in the following ways.

1. Simplifying complex data for increasing efficiency

Ease of access to important information plays a key role in how an organization functions. The IoT creates tremendous opportunities for organizations by decentralizing decisions. Data analytics helps enterprises process and analyze essential information in useful ways. Measuring pivotal performance indicators, like productivity and quality, reduces time and cost and increases revenue.

For example, a car manufacturing giant can use analyzed data from climate sensors to determine that a plant’s weather is not optimal (it’s too humid or hot) for painting automobiles. This data helps the company shift the work to another plant, saving time and cost spent on equipment service as well as speeding delivery, optimizing cost, and augmenting its bottom line.

2. Rendering valuable insights

Advanced analytics enables manufacturing companies to improve production quality by identifying issues and avoiding product failures. Running efficient algorithms to tame vast, overflowing data can enrich boardroom decisions with fresh insights.

3. Benefiting from analytics

In the future, companies will be required to adapt to new technologies and work towards an integrated system, including real-time decision making and enhancing productivity, customer service, and innovation.

Some of the major tools in the analytics artillery are predictive methodologies, prescriptive analytics, machine learning, forecasting models, neural networks, and so on. Their use has unraveled hidden (and untapped) patterns, correlations, trends, and insights.

Stepping into the future

The age of Big Data and IoT technology has clearly unfolded a new path and way of living, but there are still some concerns. For instance, how can manufacturing industries adhere to this new concept? How will industries shed traditional practices and adopt a full-fledged, modern approach? The fact is, the transition will not be so easy.

  1. The first step is to devise a robust, cross-functional digital strategy. This translates to creating methods and ways to pan out value from volumes of Big Data. Some key points to remember are identifying problems and roadblocks and creating innovative solutions with in-house techniques.
  1. Companies have been using crowdsourcing, machine learning, data integration, and advanced analytics as problem-solving methods. The next step is making use of that evolving data to derive crucial information.
  1. Securing data is one of the most important concerns for any organization. It’s essential to use Big Data analytics techniques to identify security breaches and secure the organization and customer information.

Connect with industry experts, partners, influencers, and business leaders at SAP Leonardo Live, our premier Internet of Things (IoT) conference for breakthrough innovation and technology. Register here and join us from July 11–12, 2017 in Frankfurt, Germany to experience how your company can run a digitized business.

This article originally appeared in CIO Review (India edition).


Geetika Tripathi

About Geetika Tripathi

My association with SAP is eight wonderful years. I have a disposition for the latest technological trends and a fascination for all the digital buzz apart from the world of process orchestration, cloud, and platforms.

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.

Global Findings On IoT For Consumer Products

Don Gordon

The massive impact of Internet of Things (IoT) on consumer products is now beyond question, with more than 75 billion connected devices expected by 2025. That’s a significant change from 2017’s 8.4 billion IoT devices in use, which already represents an increase of 31% from 2016.

From iPhones to smart home devices like Nest and the Amazon Echo, IoT-enabled devices have captured people’s imaginations and wallets. But our brand-new study reveals that the IoT presents massive opportunities to consumer products companies in ways that are often unseen to consumers.

Currently, 60% of surveyed global manufacturers are implementing analytics on IoT data to optimize processes and production. But, as the study shows, this is just a small part of the picture. You can get a copy of our study here. However, these numbers have been mostly conjecture with regards to consumer products IoT.

Earlier this year, SAP conducted an in-depth international survey to create a more accurate overall picture. Our professionals received data from respondents in five countries in varying states of development. The individuals surveyed represent a wide range of consumer products industries and many different professional positions. Research demographics are available in the full study document. Here’s a short look at the results of this survey.

A common set of business challenges point to IoT

The top business challenge faced by these manufacturers were raw material cost fluctuation at 35%. This was followed by high logistics costs and shrinking operational margins, at 32% and 31%, respectively. After the top three, high lead time for products and inventory and the slow pace of innovation followed, at 28% each.

The top actions put in place to deal with these concerns were led by faster reaction to demand and capacity changes, at 32%. After this was improving product lead time and product quality and compliance, at 27% and 24%. Following this was focusing on more product innovation and increasing transport efficiencies, at 23% each. The compelling trend here is that many of these challenges are most hindering consumer products companies are areas where IoT has high potential to drive strong benefits.

Understanding and applying IoT technology

But why are they investing in IoT? The survey found that 41% of respondents have a clear picture of what IoT is and what it can do for their company. That still leaves a large number of respondents who know it is important but don’t have a strong grasp of IoT and the benefits it can provide. For overall reasons, having areas of applicability to the business and IoT’s potential value to their business top the list. But what areas are the companies planning on implementing first?

There are several key areas where implementation of IoT technology has been planned: Quality control came in at a high 61%. This suggests that this is the main driver for many businesses. After this: logistics management, distribution center management, inventory movement control, and transportation management.

The companies are taking several different approaches to implementing IoT into their operations. Key initiatives used include creating processes for managing IoT. Following that is training or acquiring IoT-capable staff, learning from early adopters’ actions, increasing budget, and building an organizational consensus.

Leaders and laggards

Leaders and laggards in this group tend to divide on several aspects. One aspect includes strategic drivers for companies implementing IoT into their supply chain. Leaders focus on improving product lead time, reacting to demand and capacity changes more quickly, and improving product quality and compliance. These aspects take a forward-focused approach. Laggards focus on decreasing out-of-stocks, improving cost-to-serve initiatives, and increasing product innovation. This shows a focus on catching up to market changes.

There was also a difference in implementing initiatives to improve IoT use. Leaders focus on creating IoT management processes, learning from early adopters and allocating more funds for the process. Though laggards also focus on creating IoT management processes, it is third on their list, after establishing partnerships to exploit IoT and building an organizational consensus of IoT technology implementation.

This survey helps to prove the potential of consumer products IoT. The results help us gain a stronger understanding of key issues with IoT implementation. We discovered that improving understanding of IoT capabilities needs to be addressed. Understanding allows companies to take advantage of IoT’s benefits. There were also many differences between countries, company sizes, and positions. More detailed information and many insights can be found in our original study report than can fit in this article. To take advantage of this information, download the full report.

By undertaking this survey, SAP has been able to assemble a large number of statistics that are specific to the consumer products IoT industry. As a leader in that industry, we believe it’s important to have accurate numbers to help consumer products companies move forward.  If you need help developing a comprehensive plan to bring IoT technology into your product line or company, please feel free to contact us today for more information.

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


Don Gordon

About Don Gordon

Don Gordon leads global Consumer Products industry marketing for SAP. Previously he led global Retail industry marketing for IBM. He lives in Philadelphia, considered by many to be the finest city on earth.

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: