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SAP’s Markus Noga, Master of the Machines

Feature Article | May 9, 2017 by Jeanette Rohr

They’re already more intelligent than we are. Who or what? Machines!

Thanks to sophisticated algorithms, they’re capable of learning from Big Data and of making statements and forecasts ‒ usually faster and more reliably than humans ever could. SAP knows that machine learning offers unprecedented opportunities, not least in the realm of enterprise applications.

The global Machine Learning team at SAP is led by Dr. Markus Noga, whom German business daily Handelsblatt included in its recent poll of Germany’s 100 smartest innovators, dubbing him “master of the machines.”

Noga is head of the Machine Learning team, part of the SAP Innovation Center Network

Hamburg-born computer scientist Noga began programming at the age of seven. Growing up as the Internet evolved, he studied for a university degree, worked at a startup, and completed a PhD in document processing at the University of Karlsruhe (now the Karlsruhe Institute of Technology) before going on to join management consultancy firm Booz, Allen & Hamilton to broaden his business expertise. Here, he gained firsthand experience of enterprises and industries in 16 countries, working on large projects involving up to 200 people, and being promoted to the role of principal on the leadership team.

Noga then returned to the technology sector, where SAP’s Corporate Strategy Group presented him with the opportunity of joining Europe’s leading software company and of investing his considerable technical and business experience in innovation. Among his other achievements, he was involved in shaping SAP’s innovation strategy, which led to the establishment of the SAPPHIRE Ventures SAP HANA Fund and later formed the basis for the brand new SAP Innovation Center Network. It was not long before Vishal Sikka and Rouven Bergmann charged him with the crucial task of setting up SAP HANA Enterprise Cloud as a new business area and of managing SAP Development’s portfolio.

Noga sees himself as a man with a talent for tapping into new areas and promoting their growth, while at the same time encouraging everyone involved to develop both professionally and personally and to take on greater responsibility. “For me, that’s the most enriching aspect of my job: With machine learning, we have the means to do the groundwork and then to go on and create something new for SAP.”

The Machine Learning team at SAP has indeed made impressive progress. What began as a research project involving universities led to initial use cases and to appearances at SAPPHIRE, Executive Advisory Board Meetings, and customer events for all industries and lines of business. This is largely thanks to the work of the SAP Innovation Center Network, which, under the leadership of SAP Chief Innovation Officer Juergen Mueller, has been functioning since 2013 as an incubator for new markets and disruptive technologies with a view to opening up fresh fields of business for SAP.

“For me, the SAP Innovation Center Network is unique at SAP in the way that it embodies two particular elements,” explains Noga. “The first is startup spirit: We approach topics from an unconventional angle; we embrace new fields and disruptive technologies; and we create innovative solutions. The second is courage: The courage required to take a chance when you aren’t one hundred percent certain that the risk will pay off.”

The SAP Innovation Center Network decided at an early stage to invest in machine learning. More than a “just” a new technology, machine learning is a new means of allowing enterprise applications to learn from data and derive insights from it in a way that they never could before. This in turn requires humans to work in a different way, to interact with data constantly, and ‒ just as in the field of science ‒ to experiment with it.

Noga describes this as a “highly iterative, creative process in which data ‒ and data alone ‒ is the key to progress. It’s an approach that differs significantly from conventional backlog-driven development, which revolves around programming functions.” As such, machine learning development is less predictable but has greater potential for solving complex tasks.

“Looking ahead, I see machine learning developing, particularly in Germany and Europe, into a form of digital mechanical engineering in which machines perform repetitive tasks that humans find less unsatisfying and which, in the same way as analog, physical mechanical engineering, could potentially become a backbone of industry and of society as a whole.”

Ultimately, machine learning will impact the role of the software developer, too. In fact, you could even say that the traditional developer role will morph into that of a data scientist. “I am very happy to see that SAP has added the data scientist profile to our global job catalog,” says Noga.

Though he is quick to point out that data science can’t open the door to machine learning on its own. Machine learning developers will need to combine data science expertise with conventional cloud backend development skills in order to create functionalities, internationalize them, and make them resilient to cyber attacks and downtimes.

The people with this combination of skills are predominantly young talents and recent graduates, because deep learning and all its facets did not emerge until about 2012 and  only began to feature widely on university study programs in 2015/16. Today’s graduates are equipped with the necessary skillset; the same can only be said for a handful of those who have been in the industry for ten years or more.

“One particular characteristic developers share,” says Noga, “is a life-long willingness to learn. Whether it’s a new programing language, a new operating system, or a paradigm shift like the cloud, developers work in a dynamic technical environment. I urge each and every one of them to find out about machine learning and to gain some initial experience in order to get a feel for what machine learning can do and what it can’t.”

Above all, he is careful to point out that machine learning is not some kind of secret weapon. Artificial intelligence can’t solve every single problem that the task of managing a company involves at the touch of a button. But it does have enormous potential for supporting the decision-making process by automating routine tasks and relieving humans of the burden of tasks they perceive as less satisfying.

For SAP to be successful with machine learning, all of its enterprise applications need to be intelligent, says Noga, adding, “The SAP Innovation Center Network looks forward to teaming up with our co-workers right across SAP to build smart services and incrementally make all our products and solutions intelligent.”

Top image via Shutterstock 

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