Data, Not Technology, Steals The Spotlight In Digital Strategies

Andy Greig

Digital innovation and investment are growing by leaps and bounds – and so is the data created and consumed by them. While experts acknowledge that analysis of structured and unstructured information, Internet of Things, and all forms of artificial intelligence is taking advantage of the availability of extensive data sets, it’s becoming evident that these capabilities are also adding even more information to already overwhelmed business systems.

Many companies believe that replacing traditional data warehousing tools with in-memory databases, Big Data architectures, and cloud service providers will calm the rising flood of data. This approach may move processing closer to the data to increase performance and speed while decreasing complexity, but it does not allow decision-makers to tap into the full value of this trove of insight.

Although these technologies are certainly valuable and transformative in their own right, organizations still need to maintain a single, standardized source of truth for business models, consolidate data access services, and define operations such as scheduling and monitoring.

Why data warehouses still matter

Explosive data growth is not just an opportunity to know what happened, why it happened, and how it will happen again. It’s also a new revenue stream that adds value to existing products and services.

According to IDC, this year’s revenue growth from information-based products will double that of product and service portfolio for one-third of Fortune 500 companies. As more companies buy and sell raw data and value-added insights that cross industry lines, data monetization is emerging as a primary source of revenue – and there are no signs of slowing down as the world steadily reaches 180 zettabytes of data by 2025, up from less than 10 zettabytes in 2015.

To fully realize the value of this massive volume of data, businesses need to upgrade their data warehousing tools, not render them obsolete. Optimized with in-memory computing, cloud platform, and Big Data solutions, data warehousing solutions help democratize decision-making power for all employees of every level. They can now connect past information with live data to analyze emerging risks and opportunities in the moment, address them just as quickly, and engage in real-time transactions.

This next-generation strategy for data warehousing opens the door to capabilities that enable potentially industry-disruptive business change such as:

  • Innovate at the business’s pace: Expand IT’s focus beyond maintaining operations to finding new ways to grow the business.
  • Create an adaptive, scalable operating model: Allow the IT function to upgrade and enhance capabilities and service levels with a more rationalized enterprise architecture.
  • Reduce deployment time and cost: Coordinate a managed service model enabled by prebuilt tools designed to shrink implementation cycles and accelerate adoption. 
  • Establish comprehensive service-level agreements and managed services across the solution landscape: Access industry and engineering expertise and best practices that help improve the management of the infrastructure; systems, technology, and applications; as well as industry and business processes and extensions.

The enabling force of digital initiatives is not just technology – it’s data too

A few years ago, Peter Sondergaard, senior vice president and global head of Research at Gartner, proclaimed that everyone is a technology company. And while this was true at the time, driving digital transformation through technology has created a tsunami of data that cannot be ignored.

Hoarding a pile of data isn’t valuable in itself. Businesses must evolve from software companies to data companies to unleash tremendous value and protect the ecosystem from misuse and integrity breaches. By incorporating next-generation digital investments, from in-memory computing to the cloud and Big Data solutions, businesses can increase flexibility, heighten agility, and maintain reliability – all on one platform.

Accelerate transformative business growth and innovation by unlocking the full value of your data warehousing capabilities. Explore how SAP HANA Enterprise Cloud with SAP BW/4HANA and SAP HANA can help you deliver the business outcomes your desire with less risk.



Andy Greig

About Andy Greig

Andy Greig is the Global Marketing Plan Lead, SAP HANA Enterprise Cloud, at SAP. His primary focus is to create marketing and social media campaigns to help customers realize the benefits and value of SAP Services.

Machine Learning: Is Citizen Data Science Real?

Richard Mooney

We hear a lot these days about the “citizen data scientist.” Everyone wants to use data science and machine learning to understand their business and automate tasks to improve efficiency. But we have a shortage of people with data science skills, so much so that salaries are high for properly qualified people. To chief data officers, it’s an attractive proposition to take people from within their business who understand data and have a strong mathematical background and convert them to data scientists through self-study and online courses.

We have a new generation of visual composition framework tools that enable a business user to visually compose pipelines of algorithms, using techniques such as R and Python selectively to solve more complex problems. These techniques can get impressive visualizations back to the user and help them understand the business using statistics.

Challenges for citizen data scientists

But there are challenges with this approach. It’s not simply a matter of choosing the best algorithm:

  1. It’s very easy for a nonprofessional to misinterpret the results of a predictive model, making decisions based on poor results. It’s very difficult for a manager to recognize it until it’s too late.
  1. They need to master numerous skillsets to maximize model accuracy:
    • They need to understand feature engineering to extract useful insights from the data by deriving variables.
    • The mechanisms needed vary across data types. Date/time is very different from ordinal and continuous variables.
    • They need to extract how these variables change over time.
    • They also need to master complex techniques to make sure the data can be handled by the chosen algorithm and that missing values are correctly dealt with.
    • They need to understand how to deploy the model into production.
  1. They also need to deploy these models to production to generate the needed ROI. They need to understand how to keep the models current on an ongoing basis and how to make sure they are accurate, not just on training data but also validation, test, and new data.

Automation makes it easier

With automation throughout the predictive lifecycle, it’s possible to avoid or simplify these challenges.

  • You can train people to use automated predictive tooling to get a good model quickly and enforce best practices for model accuracy and robustness.
  • You can give them clear guidance on how models perform and enable them to deploy successfully into a wide variety of environments.
  • In parallel, they can hone their skills using a pipeline editor to experiment with other approaches while enforcing the same standards of model debriefing.

Most importantly, this reduces the risk of making a bad decision through an inadvertent but costly error. And the cost of entry to successfully utilizing and deploying predictive analytics is lowered, making it much easier to scale.

Don’t get me wrong, you still need training to take advantage of this. You need to know how to ask the question and how to maximize the results.

Even easier insights with an analytics cloud solution

Finally, business users can take advantage of advanced analytics for business exploration without needing to use any algorithms directly. This can be deployed to normal business users. The interface is set up to give them a simple way to frame the question. The insights are displayed in ways that help the user understand what they can and can’t infer from the data.

Is citizen data science real? 

So, to answer the original question: Yes, citizen data science is real, but we should think about what is the best way to enable people of different skillsets to successfully use data science in their business. This trend will only multiply as the automation techniques and helper tools advance and continue to lower the entry bar for data science and predictive analytics.

Learn more

To learn more about this subject, see:

Learn how organizations are gaining instant financial insights and using them to make better decisions – both now and in the future. Register now for 2017 Financial Excellence Forum, Oct. 10-11 in New York City.

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


Richard Mooney

About Richard Mooney

Richard is the lead product manager for the Predictive Analytics Product Portfolio including Predictive Analytics, Predictive Analytics Integrator & SAP Cloud Platform predictive services. He has 18 years experience in the software industry starting off in development and transitioning to customer facing roles including Product Management, Sales & Marketing. Richard also spent 2 years working as an innovation expert using techniques like Design Thinking, ROI Analysis and Ideation to drive customer innovation and value.

Simpler IT Begins With Hybrid Data Models

Carl Dubler

We hear a lot about simplicity in IT today. It’s a goal that is galvanizing every organization – large, small, commercial, and not-for-profit. Simpler data models, especially in core enterprise resource planning (ERP), should be the beginning of this quest.

Simpler data models in ERP not only help IT departments achieve streamlined IT landscapes; they transform the entire enterprise by fundamentally changing the way we do business. This happens by combining transactions and analytics into a unified, hybrid data model.

Breakthrough technology for database design

For decades, companies dealt with transactional and analytical information separately because database systems could not cope with both types of data. The result is the complex IT landscape we see now: numerous data silos, operational data stores, data warehouses, data cubes, and myriad tools for extracting, transforming, and loading data to feed them.

Now, using in-memory technology and columnar design, databases can handle both transactional and analytical workloads at the same time. Referred to as “Hybrid Transactional and Analytic Processing (HTAP)” by Gartner or “Translytics” by Forrester, these new databases enable a dramatically simpler IT landscape. No redundant systems are needed, and users can perform analytics directly on the transactional data at the source.

A big opportunity to reduce IT costs

All this has far-reaching implications for the IT department. Numerous systems can be consolidated into a single data platform, and cost savings from reduced data storage can be refocused to more strategic areas.

But what does it mean if you work in finance, marketing, supply chain management, or any other line of business? HTAP databases have important implications enterprise-wide.

Whereas analytics were traditionally performed on stale data, the new hybrid databases enable real-time analytics as they’re needed. And because IT staffers no longer must anticipate requirements in advance, users now have unprecedented flexibility in interrogating data to find insights to inform decision making.

Immediate results for the business

The new flexibility, immediacy, and ease with which you can analyze data is already revolutionizing business processes across every function. Finance, for example, can run a “soft close” of the books any time it wants, just to see what will happen. This means finance managers can identify issues early, before the actual close, and provide financial reports on an ad hoc basis.

Supply chain managers can run their material requirements planning (MRP) reports whenever they like, instead of only daily, and perform simulations to gauge the impact on production levels of any drop in supply of a certain component.

Leveraging superior processing capabilities

So far, so good. But simply deploying an HTAP-style database isn’t the end of the story. You also need to ensure that you’re using software that makes the most of what the database offers.

Many database systems have an in-memory option to accelerate queries. This isn’t enough. Applications – ERP in particular, since it is at the core of your business – must be completely redesigned to take advantage of the powerful analytics and superior processing capabilities that an HTAP database can offer.

The new generation of ERP software that is now emerging enables you to connect your enterprise with people, business networks, the Internet of Things, and Big Data in real time, providing live information and insights. The result: a way of working that is more agile, efficient – and simpler than ever.

To learn more about the value that simpler data models can bring to your organization, try our SAP S/4HANA Value Advisor.


Carl Dubler

About Carl Dubler

Carl Dubler is a senior director of Product Marketing for SAP S/4HANA. With an IT career stretching back to the late 1980s, he has done nearly every role in the business. In his ten years at SAP, he also managed SAP’s first commercially available cloud product and first cloud product on SAP HANA.

Diving Deep Into Digital Experiences

Kai Goerlich


Google Cardboard VR goggles cost US$8
By 2019, immersive solutions
will be adopted in 20% of enterprise businesses
By 2025, the market for immersive hardware and software technology could be $182 billion
In 2017, Lowe’s launched
Holoroom How To VR DIY clinics

From Dipping a Toe to Fully Immersed

The first wave of virtual reality (VR) and augmented reality (AR) is here,

using smartphones, glasses, and goggles to place us in the middle of 360-degree digital environments or overlay digital artifacts on the physical world. Prototypes, pilot projects, and first movers have already emerged:

  • Guiding warehouse pickers, cargo loaders, and truck drivers with AR
  • Overlaying constantly updated blueprints, measurements, and other construction data on building sites in real time with AR
  • Building 3D machine prototypes in VR for virtual testing and maintenance planning
  • Exhibiting new appliances and fixtures in a VR mockup of the customer’s home
  • Teaching medicine with AR tools that overlay diagnostics and instructions on patients’ bodies

A Vast Sea of Possibilities

Immersive technologies leapt forward in spring 2017 with the introduction of three new products:

  • Nvidia’s Project Holodeck, which generates shared photorealistic VR environments
  • A cloud-based platform for industrial AR from Lenovo New Vision AR and Wikitude
  • A workspace and headset from Meta that lets users use their hands to interact with AR artifacts

The Truly Digital Workplace

New immersive experiences won’t simply be new tools for existing tasks. They promise to create entirely new ways of working.

VR avatars that look and sound like their owners will soon be able to meet in realistic virtual meeting spaces without requiring users to leave their desks or even their homes. With enough computing power and a smart-enough AI, we could soon let VR avatars act as our proxies while we’re doing other things—and (theoretically) do it well enough that no one can tell the difference.

We’ll need a way to signal when an avatar is being human driven in real time, when it’s on autopilot, and when it’s owned by a bot.

What Is Immersion?

A completely immersive experience that’s indistinguishable from real life is impossible given the current constraints on power, throughput, and battery life.

To make current digital experiences more convincing, we’ll need interactive sensors in objects and materials, more powerful infrastructure to create realistic images, and smarter interfaces to interpret and interact with data.

When everything around us is intelligent and interactive, every environment could have an AR overlay or VR presence, with use cases ranging from gaming to firefighting.

We could see a backlash touting the superiority of the unmediated physical world—but multisensory immersive experiences that we can navigate in 360-degree space will change what we consider “real.”

Download the executive brief Diving Deep Into Digital Experiences.

Read the full article Swimming in the Immersive Digital Experience.


Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu


Jenny Dearborn: Soft Skills Will Be Essential for Future Careers

Jenny Dearborn

The Japanese culture has always shown a special reverence for its elderly. That’s why, in 1963, the government began a tradition of giving a silver dish, called a sakazuki, to each citizen who reached the age of 100 by Keiro no Hi (Respect for the Elders Day), which is celebrated on the third Monday of each September.

That first year, there were 153 recipients, according to The Japan Times. By 2016, the number had swelled to more than 65,000, and the dishes cost the already cash-strapped government more than US$2 million, Business Insider reports. Despite the country’s continued devotion to its seniors, the article continues, the government felt obliged to downgrade the finish of the dishes to silver plating to save money.

What tends to get lost in discussions about automation taking over jobs and Millennials taking over the workplace is the impact of increased longevity. In the future, people will need to be in the workforce much longer than they are today. Half of the people born in Japan today, for example, are predicted to live to 107, making their ancestors seem fragile, according to Lynda Gratton and Andrew Scott, professors at the London Business School and authors of The 100-Year Life: Living and Working in an Age of Longevity.

The End of the Three-Stage Career

Assuming that advances in healthcare continue, future generations in wealthier societies could be looking at careers lasting 65 or more years, rather than at the roughly 40 years for today’s 70-year-olds, write Gratton and Scott. The three-stage model of employment that dominates the global economy today—education, work, and retirement—will be blown out of the water.

It will be replaced by a new model in which people continually learn new skills and shed old ones. Consider that today’s most in-demand occupations and specialties did not exist 10 years ago, according to The Future of Jobs, a report from the World Economic Forum.

And the pace of change is only going to accelerate. Sixty-five percent of children entering primary school today will ultimately end up working in jobs that don’t yet exist, the report notes.

Our current educational systems are not equipped to cope with this degree of change. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is outdated by the time students graduate, the report continues.

Skills That Transcend the Job Market

Instead of treating post-secondary education as a jumping-off point for a specific career path, we may see a switch to a shorter school career that focuses more on skills that transcend a constantly shifting job market. Today, some of these skills, such as complex problem solving and critical thinking, are taught mostly in the context of broader disciplines, such as math or the humanities.

Other competencies that will become critically important in the future are currently treated as if they come naturally or over time with maturity or experience. We receive little, if any, formal training, for example, in creativity and innovation, empathy, emotional intelligence, cross-cultural awareness, persuasion, active listening, and acceptance of change. (No wonder the self-help marketplace continues to thrive!)

The three-stage model of employment that dominates the global economy today—education, work, and retirement—will be blown out of the water.

These skills, which today are heaped together under the dismissive “soft” rubric, are going to harden up to become indispensable. They will become more important, thanks to artificial intelligence and machine learning, which will usher in an era of infinite information, rendering the concept of an expert in most of today’s job disciplines a quaint relic. As our ability to know more than those around us decreases, our need to be able to collaborate well (with both humans and machines) will help define our success in the future.

Individuals and organizations alike will have to learn how to become more flexible and ready to give up set-in-stone ideas about how businesses and careers are supposed to operate. Given the rapid advances in knowledge and attendant skills that the future will bring, we must be willing to say, repeatedly, that whatever we’ve learned to that point doesn’t apply anymore.

Careers will become more like life itself: a series of unpredictable, fluid experiences rather than a tightly scripted narrative. We need to think about the way forward and be more willing to accept change at the individual and organizational levels.

Rethink Employee Training

One way that organizations can help employees manage this shift is by rethinking training. Today, overworked and overwhelmed employees devote just 1% of their workweek to learning, according to a study by consultancy Bersin by Deloitte. Meanwhile, top business leaders such as Bill Gates and Nike founder Phil Knight spend about five hours a week reading, thinking, and experimenting, according to an article in Inc. magazine.

If organizations are to avoid high turnover costs in a world where the need for new skills is shifting constantly, they must give employees more time for learning and make training courses more relevant to the future needs of organizations and individuals, not just to their current needs.

The amount of learning required will vary by role. That’s why at SAP we’re creating learning personas for specific roles in the company and determining how many hours will be required for each. We’re also dividing up training hours into distinct topics:

  • Law: 10%. This is training required by law, such as training to prevent sexual harassment in the workplace.

  • Company: 20%. Company training includes internal policies and systems.

  • Business: 30%. Employees learn skills required for their current roles in their business units.

  • Future: 40%. This is internal, external, and employee-driven training to close critical skill gaps for jobs of the future.

In the future, we will always need to learn, grow, read, seek out knowledge and truth, and better ourselves with new skills. With the support of employers and educators, we will transform our hardwired fear of change into excitement for change.

We must be able to say to ourselves, “I’m excited to learn something new that I never thought I could do or that never seemed possible before.” D!