Big Data Privacy Risks And The Role Of The GDPR: Part 1

Evelyne Salie

Part 1 of a 2-part series. Read Part 2.

Data privacy concerns anyone using the intra- and internets of our global Big Data community. But many social media and web shop customers, employees, and global organizations aren’t fully aware of the privacy risks their online activity poses. Likewise, many individuals and businesses don’t realize there are actions they can take to guard themselves against the most hazardous risks.

There are two parties prompted to take protective actions by the General Data Protection Regulation (GDPR) —individuals and organizations with global customers coming from the European Union and other countries.

Major privacy threats and their impacts

There are multiple ways that Big Data analytics can invade personal privacy. The inherent risks are:

1. Discrimination: Use predictive analytics for determination on individuals

The use of predictive analytics by the public and private sector can be used by the government and companies to make determinations about our ability to fly, find a job, obtain a clearance, or get a credit card. The use of our associations in predictive analytics to make decisions that have a negative impact on individuals can lead to discrimination.

2. Embarrassment of breaches: Create public awareness by exposing personal information – identity theft

Examples include data breaches at multiple well-known retailers, restaurant chains, online marketplaces, government agencies, universities, online media corporations, and the recent hack that not only put unreleased movies on the web but exposed the personal information of thousands of employees. Also, public awareness about credit card fraud and identity theft is at an all-time high.

3. Abolishment of anonymity: Removing only a few data sets can lead to re-identification

Without rules for anonymized data files, it’s possible to combine data sets. Without first determining if any other data items should be removed prior to combining to protect anonymity, it’s possible that individuals could be re-identified.

4. Government exemptions: Collecting and adding more and more personal information to government databases

As an example, Americans are in more government databases than ever, including that of the FBI, which collects Personally Identifiable Information (PII) including name, any aliases, race, sex, date and place of birth, Social Security number, passport and driver’s license numbers, address, telephone numbers, photographs, fingerprints, financial information like bank accounts, employment and business information, and more. And who guarantees AAA quality of that data?

5. Data brokerage: Selling of unprotected and incorrect data profiles

Numerous companies collect and sell consumer profiles that are not clearly protected under current legal frameworks. The data files used for Big Data analysis can often contain inaccurate data about individuals, use data models that are incorrect as they relate to individuals, or simply be flawed algorithms.

6. Data misinterpretation: Having more data is no substitute for having high-quality data

While one can find countless political opinions on social media, these aren’t reliably representative of voters. A substantial share of tweets and Facebook posts about politics are computer-generated.


The role and importance of information management and governance in data privacy will be a key success factor for all organizations with European Union customers. In my next blog, I’ll break down the fundamentals of the required changes that will go into effect with GDPR.

This article, GRC Tuesdays: Part One – Big Data Privacy Risks and the Role of the GDPR, originally appeared on the SAP BusinessObjects Analytics blog and has been republished with permission.


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Evelyne Salie

About Evelyne Salie

Evelyne is a highly experienced IT-Solution Principal, Business Developer and Project Manager with over 10 years IT- industry experience within the Governance Risk and Compliance and Finance area of expertise. She currently works as a Senior Director in Business Development at SAP Finance and GRC solutions. In her business development role she is working on concepts and realization for new generation of Finance solutions, running in real time, integrating predictive, Big Data, and mobile, which will change how offices of the CFO work, how the business is run, and how information is consumed.

Blockchain In Finance Sales Support: Smart Contracting

Vaag Durgaryan

Blockchain and bitcoin have been in news headlines since the end of last year. However, few people know about their potential implication on finance sales support, and more specifically, on contracts.

Run smarter, not harder

According to, smart contract technology can be compared to a vending machine. Typically, you would visit a lawyer or a notary, pay them, and wait for your document. With smart contracts, you simply drop a cryptocurrency into the vending machine, along with your driver’s license or other documents. Here’s how it works:

  • A contract between two parties is coded into blockchain. The coded contract includes information on terms, conditions, and triggering event.
  • As soon as the event happens, the contract automatically executes according to the coded terms and conditions.

Application in finance sales support

Contracting is one of the main activities in finance sales support. Suppose a seller sells a product to a buyer. This can be done through the blockchain by paying in cryptocurrency. The buyer would get a proof of purchase, which is held in the coded contract in blockchain; the seller would deliver the product by a specified date. If the product doesn’t come on time, the blockchain releases a refund. If product comes before that specific date, the function holds it, releasing both the fee and product respectively when the date arrives, and the terms and conditions are met.

The blockchain could not only provide a single ledger as a source of trust, but it also smoothes possible issues in communication and approval workflow. For instance, before making a transaction to sell products, a company currently needs to wait for a department to complete all approvals and meet internal or external conditions. In smart contracting, once the contract is coded, it will wait until all conditions of the contract are met and will execute it automatically.

Even more digital

From the perspective of the buyer, the smart contracting process can be supported by chatbots, which are available 24×7 and can consult on the process, terms, and conditions. Total digitalization and automation of non-complex contracting processes enables finance salespeople on the seller’s side to reinvest in “white-glove” support of very large contracts and deals.


Blockchain and smart contracting are not perfect. In traditional contracting, a court would review and provide a ruling in case of disagreement. In blockchain, the contract performs, no matter what. Additional concerns include security and technical bugs. While such factors may discourage smart contracting in the short term, we may see those problems fixed in the future as technology adoption increases.

Further reading

For more on this topic, read “Smart Contracts with Blockchain: New Foundation for Binding Legal Agreements” by Peter David, SAP regional CFO.

Also, read 3 Reasons CFOs & Finance Professionals Should Attend SAPPHIRE NOW to learn about what’s happening at this year’s SAPPHIRE NOW and ASUG Conference – panels, keynotes, discussions, presentations, and endless ways to connect to people and gain new ideas for streamlining processes. Join SAP’s finance team and partners June 5–7, in Orlando, Florida.

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

Vaag Durgaryan

About Vaag Durgaryan

Vaag Durgaryan is the commercial finance director for SAP in the Middle East and North Africa, which comprises of over 20 countries. Starting in 2017, he oversees a multinational team that provides finance expertise, knowledge, and strategy outlook for finance sales support in the region. Prior to that, Vaag was chief of staff for the CFO for SAP Global Field Finance and co-drove global transformation initiatives with focus on process simplification and people enablement. He holds an Executive MBA degree from ESSEC Business School and Mannheim Business School. Vaag has a passion in digitalization and learning culture.

Black Box Or White Box? Machine Learning For Finance And Risk Processes

Birgit Starmanns

Consider two different approaches to logic and system-driven decisions, commonly known as a white box and a black box approach.

With a white box approach, a term that comes from software testing, the logic and steps are known and can be traced. The logic may be mapped in flowcharts, rules, or code; most importantly, the logic and the steps of a program are very clear.

Enter the black box approach. In this case, the inner workings are not known, for example, to a software tester who sees only the inputs and the outputs. In terms of automation, this approach is also relevant when a process is too complicated to allow rules to be defined. Instead, the internal mechanism is hidden; and the subject here is that an artificial intelligence system makes specific inferences.

By extension, the argument can be made that this is similar to the way that humans learn. We make decisions every day, in general without consulting a list of rules one by one; we have learned through experience. And additional experiences influence the decisions that we make in the future.

Now let’s apply this to business systems, specifically finance systems. First, let’s look at different types of automation. As finance organizations undergo digital transformation, the need to become more efficient through automation is key, to allow finance departments to reduce errors, and to improve their speed of processing and the financial close. Such efficiencies also allow finance teams to transform their own organizations, to focus on more strategic tasks instead of handling a myriad of exceptions on a manual basis.

Types of automation

Let’s first take a look at the different types of automation, and why machine learning is different from other types of logic.

  • Rules engine. In this scenario, business users define specific rules. Sometimes these are set up in configuration; sometimes they need to be coded by an IT department. These rules are then executed on a periodic basis, most often during a period-end close. However, rules engines often become less effective over time, because they are rarely revisited. A company may expand into a new customer base for which different rules are relevant. If the rules are not adjusted – and they rarely are – finance departments must manually deal with more and more exceptions, especially as transaction volumes grow.
  • Robotic process automation. Robotics is essentially automating a manual task in a consistent way, similar to writing a script. Examples of robotic processes could be loading data into a system, where the same fields are populated, often from an uploaded file. It can also be thought of as writing a macro in Excel to execute a certain set of tasks – tasks that never change, such as manipulating data or generating a graph.
  • Machine learning. Machine learning can identify patterns in knowledge-intensive processes, without explicitly defining the patterns by rules or macros. The machine learning engine learns from historical transactions during an initial training period. It then continues to learn as finance teams make decisions based on exceptions; think of this as continuing education. Therefore, as an organization defines new business models, and additional exceptions are generated, the actions taken by finance teams on those exceptions allow the machine learning engine to incorporate these decisions into its learning. Since machine learning is based on an algorithm, it does not actually generate rules, but continues to learn – yes, a bit of a black box approach, and again, similar to the way that humans learn.

Finance applications that leverage machine learning

With the effectiveness of machine learning as part of SAP Leonardo, finance and risk applications are already leveraging machine learning in several scenarios, and the number continues to grow. These include solutions supporting:

  • Cash application. A cloud solution that learns from historical transactions of applying customer payments to invoices for open accounts receivable items. Based on the preferred tolerance level, cash can be applied automatically, leaving finance teams free to deal only with the most complex exceptions.
  • Intelligent goods receipt/invoice receipt reconciliation. A cloud application that learns from historical data and decisions of the finance team in handling exceptions to propose decisions on clearing differences.
  • Business integrity screening. A solution that employs a hybrid set of rules plus predictive analysis. Predictive analysis leverages machine learning to scale across thousands of predictive models to find patterns related to fraud, compliance failures, and other exceptions, thus reducing related financial losses.

Learn more

If you are attending SAPPHIRE NOW and ASUG Annual Conference, visit these sessions for additional information and interactions:

For additional information, you can also visit these sites:

And read this blog: The Evolution of Modern Receivables Management with Machine Learning

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

Birgit Starmanns

About Birgit Starmanns

Birgit Starmanns is a senior director in the Global Center of Excellence for Finance and Risk at SAP. She is focused on the go-to-market for new solutions, and the business benefits they can bring to organizations, such as cloud solutions for finance and applications based on SAP Leonardo technologies such as machine learning. Birgit has over 27 years of experience across solution marketing, solution management, and strategic customer communities. Prior to SAP, she was a principal in management consulting organizations, including Price Waterhouse and several boutique firms. Birgit holds a BA and MBA from the College of William and Mary. She is the author of many articles for the Financials Expert, the coauthor of the SAP Press book Accelerated Financial Closing with SAP, and the SAP Labs guidebook Product Costing Scenarios Made Easy. In addition, she is a regular presenter at various SAP events.

The Human Angle

By Jenny Dearborn, David Judge, Tom Raftery, and Neal Ungerleider

In a future teeming with robots and artificial intelligence, humans seem to be on the verge of being crowded out. But in reality the opposite is true.

To be successful, organizations need to become more human than ever.

Organizations that focus only on automation will automate away their competitive edge. The most successful will focus instead on skills that set them apart and that can’t be duplicated by AI or machine learning. Those skills can be summed up in one word: humanness.

You can see it in the numbers. According to David J. Deming of the Harvard Kennedy School, demand for jobs that require social skills has risen nearly 12 percentage points since 1980, while less-social jobs, such as computer coding, have declined by a little over 3 percentage points.

AI is in its infancy, which means that it cannot yet come close to duplicating our most human skills. Stefan van Duin and Naser Bakhshi, consultants at professional services company Deloitte, break down artificial intelligence into two types: narrow and general. Narrow AI is good at specific tasks, such as playing chess or identifying facial expressions. General AI, which can learn and solve complex, multifaceted problems the way a human being does, exists today only in the minds of futurists.

The only thing narrow artificial intelligence can do is automate. It can’t empathize. It can’t collaborate. It can’t innovate. Those abilities, if they ever come, are still a long way off. In the meantime, AI’s biggest value is in augmentation. When human beings work with AI tools, the process results in a sort of augmented intelligence. This augmented intelligence outperforms the work of either human beings or AI software tools on their own.

AI-powered tools will be the partners that free employees and management to tackle higher-level challenges.

Those challenges will, by default, be more human and social in nature because many rote, repetitive tasks will be automated away. Companies will find that developing fundamental human skills, such as critical thinking and problem solving, within the organization will take on a new importance. These skills can’t be automated and they won’t become process steps for algorithms anytime soon.

In a world where technology change is constant and unpredictable, those organizations that make the fullest use of uniquely human skills will win. These skills will be used in collaboration with both other humans and AI-fueled software and hardware tools. The degree of humanness an organization possesses will become a competitive advantage.

This means that today’s companies must think about hiring, training, and leading differently. Most of today’s corporate training programs focus on imparting specific knowledge that will likely become obsolete over time.

Instead of hiring for portfolios of specific subject knowledge, organizations should instead hire—and train—for more foundational skills, whose value can’t erode away as easily.

Recently, educational consulting firm Hanover Research looked at high-growth occupations identified by the U.S. Bureau of Labor Statistics and determined the core skills required in each of them based on a database that it had developed. The most valuable skills were active listening, speaking, and critical thinking—giving lie to the dismissive term soft skills. They’re not soft; they’re human.

This doesn’t mean that STEM skills won’t be important in the future. But organizations will find that their most valuable employees are those with both math and social skills.

That’s because technical skills will become more perishable as AI shifts the pace of technology change from linear to exponential. Employees will require constant retraining over time. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is already outdated by the time students graduate, according to The Future of Jobs, a report from the World Economic Forum (WEF).

The WEF’s report further notes that “65% of children entering primary school today will ultimately end up working in jobs that don’t yet exist.” By contrast, human skills such as interpersonal communication and project management will remain consistent over the years.

For example, organizations already report that they are having difficulty finding people equipped for the Big Data era’s hot job: data scientist. That’s because data scientists need a combination of hard and soft skills. Data scientists can’t just be good programmers and statisticians; they also need to be intuitive and inquisitive and have good communication skills. We don’t expect all these qualities from our engineering graduates, nor from most of our employees.

But we need to start.

From Self-Help to Self-Skills

Even if most schools and employers have yet to see it, employees are starting to understand that their future viability depends on improving their innately human qualities. One of the most popular courses on Coursera, an online learning platform, is called Learning How to Learn. Created by the University of California, San Diego, the course is essentially a master class in human skills: students learn everything from memory techniques to dealing with procrastination and communicating complicated ideas, according to an article in The New York Times.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing.

Although there is a longstanding assumption that social skills are innate, nothing is further from the truth. As the popularity of Learning How to Learn attests, human skills—everything from learning skills to communication skills to empathy—can, and indeed must, be taught.

These human skills are integral for training workers for a workplace where artificial intelligence and automation are part of the daily routine. According to the WEF’s New Vision for Education report, the skills that employees will need in the future fall into three primary categories:

  • Foundational literacies: These core skills needed for the coming age of robotics and AI include understanding the basics of math, science, computing, finance, civics, and culture. While mastery of every topic isn’t required, workers who have a basic comprehension of many different areas will be richly rewarded in the coming economy.
  • Competencies: Developing competencies requires mastering very human skills, such as active listening, critical thinking, problem solving, creativity, communication, and collaboration.
  • Character qualities: Over the next decade, employees will need to master the skills that will help them grasp changing job duties and responsibilities. This means learning the skills that help employees acquire curiosity, initiative, persistence, grit, adaptability, leadership, and social and cultural awareness.

The good news is that learning human skills is not completely divorced from how work is structured today. Yonatan Zunger, a Google engineer with a background working with AI, argues that there is a considerable need for human skills in the workplace already—especially in the tech world. Many employees are simply unaware that when they are working on complicated software or hardware projects, they are using empathy, strategic problem solving, intuition, and interpersonal communication.

The unconscious deployment of human skills takes place even more frequently when employees climb the corporate ladder into management. “This is closely tied to the deeper difference between junior and senior roles: a junior person’s job is to find answers to questions; a senior person’s job is to find the right questions to ask,” says Zunger.

Human skills will be crucial to navigating the AI-infused workplace. There will be no shortage of need for the right questions to ask.

One of the biggest changes narrow AI tools will bring to the workplace is an evolution in how work is performed. AI-based tools will automate repetitive tasks across a wide swath of industries, which means that the day-to-day work for many white-collar workers will become far more focused on tasks requiring problem solving and critical thinking. These tasks will present challenges centered on interpersonal collaboration, clear communication, and autonomous decision-making—all human skills.

Being More Human Is Hard

However, the human skills that are essential for tomorrow’s AI-ified workplace, such as interpersonal communication, project planning, and conflict management, require a different approach from traditional learning. Often, these skills don’t just require people to learn new facts and techniques; they also call for basic changes in the ways individuals behave on—and off—the job.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing. As science gains a better understanding of how the human brain works, many behaviors that affect employees on the job are understood to be universal and natural rather than individual (see “Human Skills 101”).

Human Skills 101

As neuroscience has improved our understanding of the brain, human skills have become increasingly quantifiable—and teachable.

Though the term soft skills has managed to hang on in the popular lexicon, our understanding of these human skills has increased to the point where they aren’t soft at all: they are a clearly definable set of skills that are crucial for organizations in the AI era.

Active listening: Paying close attention when receiving information and drawing out more information than received in normal discourse

Critical thinking: Gathering, analyzing, and evaluating issues and information to come to an unbiased conclusion

Problem solving: Finding solutions to problems and understanding the steps used to solve the problem

Decision-making: Weighing the evidence and options at hand to determine a specific course of action

Monitoring: Paying close attention to an issue, topic, or interaction in order to retain information for the future

Coordination: Working with individuals and other groups to achieve common goals

Social perceptiveness: Inferring what others are thinking by observing them

Time management: Budgeting and allocating time for projects and goals and structuring schedules to minimize conflicts and maximize productivity

Creativity: Generating ideas, concepts, or inferences that can be used to create new things

Curiosity: Desiring to learn and understand new or unfamiliar concepts

Imagination: Conceiving and thinking about new ideas, concepts, or images

Storytelling: Building narratives and concepts out of both new and existing ideas

Experimentation: Trying out new ideas, theories, and activities

Ethics: Practicing rules and standards that guide conduct and guarantee rights and fairness

Empathy: Identifying and understanding the emotional states of others

Collaboration: Working with others, coordinating efforts, and sharing resources to accomplish a common project

Resiliency: Withstanding setbacks, avoiding discouragement, and persisting toward a larger goal

Resistance to change, for example, is now known to result from an involuntary chemical reaction in the brain known as the fight-or-flight response, not from a weakness of character. Scientists and psychologists have developed objective ways of identifying these kinds of behaviors and have come up with universally applicable ways for employees to learn how to deal with them.

Organizations that emphasize such individual behavioral traits as active listening, social perceptiveness, and experimentation will have both an easier transition to a workplace that uses AI tools and more success operating in it.

Framing behavioral training in ways that emphasize its practical application at work and in advancing career goals helps employees feel more comfortable confronting behavioral roadblocks without feeling bad about themselves or stigmatized by others. It also helps organizations see the potential ROI of investing in what has traditionally been dismissed as touchy-feely stuff.

In fact, offering objective means for examining inner behaviors and tools for modifying them is more beneficial than just leaving the job to employees. For example, according to research by psychologist Tasha Eurich, introspection, which is how most of us try to understand our behaviors, can actually be counterproductive.

Human beings are complex creatures. There is generally way too much going on inside our minds to be able to pinpoint the conscious and unconscious behaviors that drive us to act the way we do. We wind up inventing explanations—usually negative—for our behaviors, which can lead to anxiety and depression, according to Eurich’s research.

Structured, objective training can help employees improve their human skills without the negative side effects. At SAP, for example, we offer employees a course on conflict resolution that uses objective research techniques for determining what happens when people get into conflicts. Employees learn about the different conflict styles that researchers have identified and take an assessment to determine their own style of dealing with conflict. Then employees work in teams to discuss their different styles and work together to resolve a specific conflict that one of the group members is currently experiencing.

How Knowing One’s Self Helps the Organization

Courses like this are helpful not just for reducing conflicts between individuals and among teams (and improving organizational productivity); they also contribute to greater self-awareness, which is the basis for enabling people to take fullest advantage of their human skills.

Self-awareness is a powerful tool for improving performance at both the individual and organizational levels. Self-aware people are more confident and creative, make better decisions, build stronger relationships, and communicate more effectively. They are also less likely to lie, cheat, and steal, according to Eurich.

It naturally follows that such people make better employees and are more likely to be promoted. They also make more effective leaders with happier employees, which makes the organization more profitable, according to research by Atuma Okpara and Agwu M. Edwin.

There are two types of self-awareness, writes Eurich. One is having a clear view inside of one’s self: one’s own thoughts, feelings, behaviors, strengths, and weaknesses. The second type is understanding how others view us in terms of these same categories.

Interestingly, while we often assume that those who possess one type of awareness also possess the other, there is no direct correlation between the two. In fact, just 10% to 15% of people have both, according to a survey by Eurich. That means that the vast majority of us must learn one or the other—or both.

Gaining self-awareness is a process that can take many years. But training that gives employees the opportunity to examine their own behaviors against objective standards and gain feedback from expert instructors and peers can help speed up the journey. Just like the conflict management course, there are many ways to do this in a practical context that benefits employees and the organization alike.

For example, SAP also offers courses on building self-confidence, increasing trust with peers, creating connections with others, solving complex problems, and increasing resiliency in the face of difficult situations—all of which increase self-awareness in constructive ways. These human-skills courses are as popular with our employees as the hard-skill courses in new technologies or new programming techniques.

Depending on an organization’s size, budget, and goals, learning programs like these can include small group training, large lectures, online courses, licensing of third-party online content, reimbursement for students to attain certification, and many other models.

Human Skills Are the Constant

Automation and artificial intelligence will change the workplace in unpredictable ways. One thing we can predict, however, is that human skills will be needed more than ever.

The connection between conflict resolution skills, critical thinking courses, and the rise of AI-aided technology might not be immediately obvious. But these new AI tools are leading us down the path to a much more human workplace.

Employees will interact with their computers through voice conversations and image recognition. Machine learning will find unexpected correlations in massive amounts of data but empathy and creativity will be required for data scientists to figure out the right questions to ask. Interpersonal communication will become even more important as teams coordinate between offices, remote workplaces, and AI aides.

While the future might be filled with artificial intelligence, deep learning, and untold amounts of data, uniquely human capabilities will be the ones that matter. Machines can’t write a symphony, design a building, teach a college course, or manage a department. The future belongs to humans working with machines, and for that, you need human skills. D!

About the Authors

Jenny Dearborn is Chief Learning Officer at SAP.

David Judge is Vice President, SAP Leonardo, at SAP.

Tom Raftery is Global Vice President and Internet of Things Evangelist at SAP.

Neal Ungerleider is a Los Angeles-based technology journalist and consultant.

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


HR In The Age Of Digital Transformation

Neha Makkar Patnaik

HR has come a long way from the days of being called Personnel Management. It’s now known as People & Culture, Employee Experience, or simply People, and the changes in the last few years have been especially far-reaching, to say the least; seismic even.

While focused until recently on topics like efficiency and direct access to HR data and services for individual employees, a new and expanded HR transformation is underway, led by employee experience, cloud capabilities including mobile and continuous upgrades, a renewed focus on talent, as well as the availability of new digital technologies like machine learning and artificial intelligence. These capabilities are enabling HR re-imagine new ways of delivering HR services and strategies throughout the organization. For example:

  • Use advanced prediction and optimization technologies to shift focus from time-consuming candidate-screening processes to innovative HR strategies and business models that support growth
  • Help employees with tailored career paths, push personalized learning recommendations, suggest mentors and mentees based on skills and competencies
  • Predict flight risk of employees and prescribe mitigation strategies for at-risk talent
  • Leverage intelligent management of high-volume, rules-based events with predictions and recommendations

Whereas the traditional view of HR transformation was all about doing existing things better, the next generation of HR transformation is focused on doing completely new things.

These new digital aspects of HR transformation do not replace the existing focus on automation and efficiency. They work hand in hand and, in many cases, digital technologies can further augment automation. Digital approaches are becoming increasingly important, and a digital HR strategy must be a key component of HR’s overall strategy and, therefore, the business strategy.

For years, HR had been working behind a wall, finally got a seat at the table, and now it’s imperative for CHROs to be a strategic partner in the organization’s digital journey. This is what McKinsey calls “Leading with the G-3” in An Agenda for the Talent-First CEO, in which the CEO, CFO, and CHRO (i.e., the “G-3”) ensure HR and finance work in tandem, with the CEO being the linchpin and the person who ensures the talent agenda is threaded into business decisions and not a passive response or afterthought.

However, technology and executive alignment aren’t enough to drive a company’s digital transformation. At the heart of every organization are its people – its most expensive and valuable asset. Keeping them engaged and motivated fosters an innovation culture that is essential for success. This Gallup study reveals that a whopping 85% of employees worldwide are performing below their potential due to engagement issues.

HR experiences that are based on consumer-grade digital experiences along with a focus on the employee’s personal and professional well-being will help engage every worker, inspiring them to do their best and helping them turn every organization’s purpose into performance. Because, we believe, purpose drives people and people drive business results.

Embark on your HR transformation journey

Has your HR organization created a roadmap to support the transformation agenda? Start a discussion with your team about the current and desired state of HR processes using the framework with this white paper.

Also, read SAP’s HR transformation story within the broader context of SAP’s own transformation.

Neha Makkar Patnaik

About Neha Makkar Patnaik

Neha Makkar Patnaik is a principal consultant at SAP Labs India. As part of the Digital Transformation Office, Neha is responsible for articulating the value proposition for digitizing the office of the CHRO in alignment with the overall strategic priorities of the organization. She also focuses on thought leadership and value-based selling programs for retail and consumer products industries.