Five Powerful Ways Your HR Team Can Leverage Big Data

Charles Ebert

The term “Big Data” seems mysterious to many HR organizations. Of course, if you don’t know what this technology is and how it works, you can’t use it to add value.

Big Data describes the huge amount of data, both structured and unstructured, that people and systems are using. But only the data that affects business greatly on daily basis counts. Even so, this amount of data is usually so large that traditional software can’t analyze it. But analysis is crucial if you want to reveal patterns and trends to improve your business.

How people use Big Data for business

Analyzing Big Data helps businesses get better insights, understand processes within the company, make important business decisions according to patterns found in research and analysis, and plan strategic business moves. When it comes to human resources and HR organizations, Big Data helps make decisions that affect recruiting, creating bigger benefits for the company.

Data can actually make a big difference if used wisely. However, not all HR departments can do so, with many still trying to learn what can they do with this massive amount of data, how they can process and interpret it.

While it might seem challenging, learning how to manage Big Data is crucial for any HR department that wants to develop and advance. By learning the latest Big Data trends, HR organizations can leverage them in the market. They can also learn how to better distribute their resources and find employees with better skill sets. Big Data also has an affect on existing staff; for example, by learning what increases and decreases productivity, work processes can be adjusted.

Here are the most effective ways your HR team can leverage Big Data.

1. Planning

Analyzing various internal surveys can help HR understand what improves retention. If common employee complaints are identified in surveys, HR can eliminate them to prevent employee turnover.

Survey analysis can also help HR:

  • plan new, more effective talent-retention programs
  • learn how to benchmark workers effectively

2. Talent forecasting

Relying solely on technology isn’t always effective: the human element of HR still matters, sometimes solving problems more efficiently than computers. However, using common HR tests (e.g., personality and cognitive ability) can affect your hiring process and increase the quality of your hires.

To do this, analyze the test results of your most successful employees and try to establish patterns that you can compare with those of potential candidates. While this isn’t a foolproof solution, it can help HR choose between two equally qualified candidates and increase the chance of hiring better employees.

3. Worker performance

While it’s impossible to replace people with technology (at least for now), combining technology and the human element can do wonders for your company.

Analyzing Big Data helps HR discover new insights and trends that can be shared with employees and help them in their work. This could increase their performance level, thereby helping your company succeed.

4. Better understand your employees

To help your employees perform better, you need to know exactly what you want to improve and how to it. What are their current skills? What skills can they develop? What can be done to help them succeed at work?

By studying workforce analytics, you can better understand your employees, know their strengths, weaknesses, current skills, etc., in order to develop a strategy to help your staff grow.

5. Find out what motivates your employees

Your workers’ engagement level directly affects your company’s overall results. In order to find new ways to motivate employees and make them more engaged, you need to know exactly what drives them, how you can affect those drivers, and so on. Analyzing data from survey results enables you to understand how to use what motivates them to improve engagement.

Big Data can improve many aspects of a company’s HR organization, including recruiting and hiring outcomes. To tap into its value, create a diverse team of professionals who can leverage Big Data efficiently and use the results for future planning. By entrusting this work to a strong and reliable team, you’ll can ensure that the important data that can affect your company won’t go unnoticed.

Learn more about using technology to improve employee engagement in How Emotionally Aware Computing Can Bring Happiness to Your Organization.


About Charles Ebert

Charles is a career mentor, motivational speaker, and human resources consultant with over 10 years of experience in HR sector. Charles is a lead expert at Professional Resume Solutions. Apart from career mentoring, he loves photography and football. Find him on Linkedin, Twitter, Facebook, and Google+.

How Technology And Data-Driven Insight Can Boost Employee Engagement

Andre Smith

Human resources professionals know that happier employees are more productive. In today’s hyper-competitive global economy, employee productivity can mean the difference between business success and failure. That means employee happiness should be a top priority for any company that aims to succeed.

The difference between a happy, engaged workforce and a dissatisfied one is vast. Surveys indicate that companies with highly engaged employees outperform others by as much as 202%. Still, according to Gallup polling data, only about 32% of employees report that they feel motivated and engaged at work. To fix the problem, we must first understand what causes it. Here are the most common roadblocks to employee engagement, and how technology and data-driven management can help overcome them.

Rigid schedules

In a recent survey, as many as 70% of employees reported that having flexible work hours was very important to them. In today’s modern interconnected world, it’s easier than ever to create flexible work arrangements for employees. The key to making it work is to utilize comprehensive workforce management software to analyze business needs before beginning a flexible work initiative.

Gary Corcoran, of Advance Systems, says, “Gaining a full picture of necessary staffing levels throughout the workweek is the first step to creating a flexible schedule. In reality, the flexibility allowed should extend as far as business needs will allow. That way, employees stay happy and the business won’t find itself short-staffed at busy times.”

Lack of work/life balance

In 2016, American workers failed to use a combined 662 million earned vacation days. A big reason for this is anxiety about requesting vacation time. The results are profoundly damaging for both employees and companies. Once again, a data-driven solution is in order. By keeping careful track of which employees aren’t taking vacation time, human resource managers can intervene to make sure they take a break.

This goes a long way towards changing the office culture surrounding vacations. Depending on the business structure, making vacations mandatory can create a positive change. Companies can even offer automated paycheck deductions into vacation savings accounts using services like 401Play to encourage health employee vacation habits.

Poor internal communication

One of the most common employee complaints is lack of transparency and communication within their organization. Not seeing clear reasons for their work or understanding their place within the bigger picture is dispiriting and counterproductive. To combat this, a variety of technological tools may be employed. First and foremost, the internal communication system should include an all-in-one platform like Slack, which all employees can use to remain connected with one another. At the team level, project-based communication tools like i done this make task management a snap and help employees to see how their work dovetails with that of their co-workers.

Engagement begins at the top

There are many other factors that can contribute to employee engagement than mentioned here. There is, however, an easy, but often overlooked, way for managers to boost employee engagement: They must embody their own engagement policies. For instance, if the business allows flexible scheduling, managers should use it as well, always striving to stay productive and set an example.

The same concept goes for things like vacation time and communications: It’s all about setting a tone. The level of commitment to creating a healthy and motivated workplace at the management level is the true determining factor in the success of any engagement policy. Efforts by managers let employees know that they are valued. That creates the kind of engagement that cannot be bought or quantified.

Learn more about how data analytics in HR can get your business Moving From Gut Instinct to Data Insight.


About Andre Smith

Andre Smith is an Internet, marketing, and e-commerce specialist with several years of experience in the industry. He has watched as the world of online business has grown and adapted to new technologies, and he has made it his mission to help keep businesses informed and up to date.

Are Your Performance Reviews Ahead Of The Curve Or Just A Box-Ticking Affair?

Sonya Clark

How many times have you sat through your performance review and felt like it was a box-ticking exercise? This approach to performance management may have worked 20 or even five years ago, but workplaces have changed.

Globally, only 13% of employees are engaged in their jobs. In fact, actively disengaged workers worldwide continue to outnumber engaged workers at a rate of nearly 2 to 1.

In Germany alone, Gallup estimates that disengagement costs €112 billion to €138 billion per year. Companies with engaged employees outperform those without by up to 202%. The need to dramatically change the way we manage employee engagement and performance couldn’t be more urgent. Truth is, what we’ve known as performance management is not working.

Companies of all sizes are shifting away from annual appraisals to more regular check-ins and frequent real-time feedback. According to Deloitte, “70% of executives are finding that the redesign of performance management is now a high priority.”

Now that we are well into 2018, companies will follow the example of global giants such as SAP and Nestle, which have stripped complexity, such as annual appraisals, ratings, calibration meetings, and competency assessments, focusing instead on regular, quality performance conversations and feedback known as Continuous Performance Management (CPM).

SAP’s head of HR for Australia and New Zealand, Debbie Rigger, believes that CPM comes down to systems, process, and people. Questions she recently explored with Queensland HR leaders include: How do you set the bar at the right level? How do you monitor and assess performance? And how do you do all of this is a simple and transparent way?

CPM: A new approach

At SAP, we needed an updated approach to performance. In order to be agile and keep up with our customers, we needed to head in a different direction, so by early 2017 we rolled out CPM, which resulted in huge gains:

  • 88% of employees are having continuous dialogue
  • 79% are living the new continuous dialogue culture
  • There’s been an 80% increase in engagement in development planning

Let’s face it, everyone wants to know how they are doing at work. By way of comparison, if you play sports, feedback is instant when you are on the field – as a result, development and improvement are much faster. Your coach is there to give you direction, offer support, and help you through the challenges. Ultimately a good coach will also be there to celebrate the wins, and help you to be the best you can be – every step of the way… Not just once a year. Why should the workplace be any different?

Technology provides an auditable trail that facilitates a more rounded conversation about the highs and lows of the past 12 months. It also takes away the reliance (and risk!) of memorizing events – which can be skewed in hindsight. Ultimately, technology creates a level playing field for everybody. It bridges the gap between departments, distance, and generations.

To get more HR-related insights, including SAP SuccessFactors customer stories, please visit our HR Insights Digital Hub.


Sonya Clark

About Sonya Clark

Sonya Clark is a HR Specialist with SAP SuccessFactors. For close to 15 years, she has partnered with HR and Business leaders to align their people strategies to their business strategies, delivering people initiatives that increase employee engagement and productivity. Using a combination of both best practise, and leading edge technologies offering her clients a design approach to intelligent HR management to ensure a return on investment. Based in Brisbane, she is regularly hosting HR thought leadership events, whilst partnering with HR experts to deliver strategic solution advice.

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.