Top 5 Finance Blogs Of February 2017

Jean Loh

The #1 most-read blog in CFO Knowledge for February, even though it was published on the very last day of the month, was on country-by-country reporting legislation. Other hot topics: gender diversity, finance agility, predictive analytics, and cyber-risk management at Deloitte. If you missed any of these great blogs, here’s a roundup.

What CFOs Need To Know Now About Country-By-Country Reporting Legislation

Procurement And Gender Diversity: Building Leadership That Matters

Bring Agility Into The Finance Department To Enable True Business Partnering

Predictive Analytics: The Answer To “What’s Next?”

Cyber-Risk Management Built In From The Start As Deloitte Transforms Its Core Finance System

Look a little further back in time, at last year’s trends that are influencing business in 2017 and beyond. Read More Than Noise: 5 Digital Stories From 2016 That Are Bigger Than You Think.

Follow SAP Finance online: @SAPFinance (Twitter)  | LinkedIn | FacebookYouTube


Jean Loh

About Jean Loh

Jean Loh is the director, Global Audience Marketing at SAP. She is an experienced marketing and communication professional, currently responsible for developing thought leadership content that is unbiased and audience-led while addressing market challenges to illuminate and solve the unmet needs of CFOs, CIOs, and the wider global finance and IT audience.

Your GDPR "Duties Of Proof" And Liability

Neil Patrick

In the typical application of legislation, one is assumed innocent until proven guilty. There are some exceptions; in many countries, health and safety is an example. Companies are assumed guilty until proven innocent if there is a fall and injury. The question is asked: What could they have reasonably done to prevent it?

Burden of proof

While the European Union (EU) General Data Protection Regulation (GDPR) doesn’t have a full reverse burden of proof, Article 82 (Right to compensation and liability) paragraph 3 states, “A controller or processor shall be exempt from liability under paragraph 2 if it proves that it is not in any way responsible for the event giving rise to the damage.

This means liability exists for any damage caused by noncompliant processing, and in severe cases implies that a data subject receives effective compensation. The apportionment of liability will most likely depend on the degree of processing responsibility between data controllers and data processors.

The “damage” outlined in paragraph 2 of Article 82 relates to processing activities of a data controller and data processor and whether this processing is not compliant with the GDPR (as well as fulfilling lawful instruction of a data controller if you are a data processor).

So what does this processing burden of proof look like?

Processing of personal data

Drilling further into the regulation, we learn that processing of personal data is defined as “collection, recording, organization, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction.” Pretty much anything and everything to do with “touching” personal data.

The GDPR also requires that processing is specific to a purpose—the business reason why the personal data is being processed. Companies, in essence, need to attach a defined purpose to their specific processing of personal data, and they need to demonstrate they are processing the data with appropriate responsibility for that purpose only. Without lawful processing acquired (consent, contract, legal obligation, and so on), without a defined purpose (marketing a specific offering for example), processing should not be done.

If processing is taking place, the company must demonstrate by design and by default that it is being done in accordance with GDPR; in other words, prove it.

Further (but not exhaustive) duties when processing personal data include:

  • The amount of data processed is to be “adequate, relevant, and limited to what is necessary for the purposes.”
  • Data subjects “should be made aware of risks, rules, safeguards, and rights in relation to the processing of personal data.”
  • It should be “in a manner that ensures appropriate security and confidentiality of the personal data, including for preventing unauthorized access to or use of personal data and the equipment used for the processing.”
  • The “period for which the personal data are stored is limited to a strict minimum.”

Security of processing personal data: data breach

In addition to procedures around processing of personal data, there is also a burden of reporting breaches to the supervising authority, in the case of failures of security relating to processing of personal data.

The GDPR defines a data breach as “a breach of security leading to the accidental or unlawful destruction, loss, alteration, unauthorized disclosure of, or access to, personal data transmitted, stored, or otherwise processed.” We come back to processing of personal data once again, this time adding breaches of security.

Data controllers have 72 hours (or less if required in that EU member state) to report becoming aware of a data breach. Data processors have a duty to inform a data controller of a data breach.

The extent of risk to data subjects (number of records breached, type of data breached, nature of exposure, for example) will significantly impact fines, corrective measures, and so on that, a supervising authority may consider imposing.

Clearly, the GDPR takes processing of personal data seriously, as well as managing the security of processing.

What to do?

How do we prove we are “not in any way responsible for the event giving rise to the damage”?

This is not trivial.

I am not a lawyer and cannot give legal advice, so this is my own personal thought: Build a digital “paper trail” of how you have implemented the GDPR in your organization and what measures you have in place to manage processing of personal data—and start as soon as possible.

Implementing an access governance tool (for example) is one of the aspects to manage security when processing personal data. But as with any technology and evidence-based regulation, you must show that the technology (as a control) is fit for purpose (design effectiveness) and has been rolled out properly and is being used (operating effectiveness).

The same holds with policies (data encryption, 2-factor authentication, or even your GDPR policy). Creating the best policy in the world is excellent and admirable, but if it isn’t implemented throughout the organization and you don’t have a record of its being accepted by the recipients, you have not complied with GDPR.

Bottom line

Even if you don’t have all the technical measures in place, it will be a really good thing if you have a documented GDPR program in place with both:

  • Executive sponsorship (accountability)
  • Evidence of it being successfully rolled out throughout your organization (which of course requires that it has been rolled out)

This can help you build evidence that you have done all that you can that is reasonably possible to:

  • Use state-of-the-art technologies
  • Have technical and organizational measures in place
  • Have privacy by design and by default

Learn more

  • Review these assets, including a Webinar, to find out more about how you can turn GDPR compliance into a growth opportunity.
  • Read the rest of our GRC Tuesday series blogs on GDPR.

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | Facebook | YouTube

This article originally appear on the SAP Analytics blog and is republished by permission.


Neil Patrick

About Neil Patrick

Dr. Neil Patrick is a Director of SAP Centre of Excellence for GRC & Security covering EMEA. He has over 12 years’ experience in Governance, Risk Management and Compliance (GRC) & Security fields. During this time he has been a managing consultant, run professional services delivery teams in the UK and USA, conducted customer business requirements sessions around the world, and sales and business development initiatives. Neil has presented core GRC and Security thought leadership sessions in strategic customer-facing engagements, conferences and briefing sessions.

How To Simplify Expense, Travel, And Invoice Management

Rebecca Dolan

If your company is facing issues with expense, travel, and invoice management, you’re not alone. A commissioned study conducted by Forrester Consulting on behalf of SAP Concur found that 60% of companies face issues with manual processes and timeliness due to their current tools.

Outdated legacy technology is one of the core causes of inefficient expense, travel, and invoice management. Manual processes are clunky, time-consuming, vulnerable to human error, and lack visibility across company spend. The inaccuracies and slowdowns they cause can also lead to auditing headaches, additional work, and extra costs.

The other major issue facing companies in this area is the lack of alignment between the IT and finance departments. Sixty-one percent of firms say that IT is focused more on the usability and employee experience and less on spend reductions. Conversely, 64% of firms say that finance is focused more on reducing spending and less on usability and employee experience.

What if there was a solution that would increase visibility, save time, and cut costs – all while satisfying the needs of stakeholders in both departments?

Financial teams want to simplify processes and save money

While new invoice management tools can’t lower your bills themselves, they can optimize your processes in ways that will cut costs. Automation will ensure those bills are accounted for and paid on time, saving you from late fees, reducing the time spent managing invoices, and allowing you to focus valuable time on other tasks.

Beyond time savings, you need visibility into what is being paid, to whom, and from what budgets. Inadequate visibility into expenses is a key challenge for better managing the expense, travel, and invoice process. This type of reporting is critical for understanding where and when spend happens with enough time to influence it.

IT managers want to reduce the burden of tech support

Thirty-eight percent of finance and IT decision makers cite increasing automation as a top expense and travel priority in the next year or two. And it makes sense that the IT department has a stake in the game. Automated, cloud-based tools enable the efficiency that companies lack, lessening the burden on the internal IT support team, and allowing them to focus attention on supporting employees in other ways.

For employees, cloud-based tools increase productivity, adoption, and satisfaction

In a modern world, employees expect that the technological advances they enjoy in their personal lives will carry over to their work lives. Instead, they face fragmented, manual expense and payment processes that are inefficient and time-consuming. As a result, both managers and employees in the Forrester survey ranked manual processes as a top frustration when it comes to managing expenses.

By fully automating expense, travel, and invoice systems with cloud-based tools available on mobile devices, you can reduce employee frustration, encourage adoption, and increase compliance. Likewise, better tools to track expenses in one place can help them make responsible spending choices and give managers visibility into any potentially fraudulent spending. All of this translates into money saved.

A unified solution benefits everyone

When we combine needs across the board, the solution emerges: An automated expense, travel, and invoice management process will improve employees’ experiences and reduce spending. This can be accomplished by full alignment of IT and finance, which then allows both departments to accomplish their own goals.

Indeed, we think the results of the Forrester survey demonstrate the effectiveness of this approach. Seventy-five percent of companies with a unified strategy reported high satisfaction with travel and expense tools, compared to just 35% without a unified strategy.

Aligning the goals of the finance and IT teams has a profound effect on the overall travel, expense, and invoice management process. Download the full study now to learn more about how you can simplify your expense, travel, and invoice solutions.

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | FacebookYouTube

This story originally appeared on the SAP Concur newsroom.


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.