Astrophysics Goes Business – Interview With Dr. Rene Fassbender

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Dr. Rene Fassbender, astrophysicist & founder of OmegaLambdaTec, explains how science & business come together and why this might be the ideal match! Learn more:

Describe yourself in 50 words or less.

I am Rene Fassbender, founder and CEO of the Munich based startup OmegaLambdaTec – Data Science Services. Before starting on my new business adventure, I was a research astrophysicist in the field of observational cosmology, studying the formation and evolution of the large-scale structure in the Universe.

A real scientist in the business world is quite unique! Can you briefly explain in layperson terms what was your research topic?

I was hunting for the largest objects in the young Universe, the so-called clusters of galaxies, and studying their formation and evolution at distances up to 10 billion light years away. At these distances we are directly observing the Universe and its constituents as they were only a few billion years after the Big Bang, using multi-wavelength observations from X-rays to the optical and infrared range taken with the most powerful telescopes and space observatories of our time.

Interesting! Tell us now a bit about your company – OmegaLambdaTec.

Working for more than a decade at the forefront of this data-driven research topic with massive amounts of data, with different wavelengths, formats, and methods, detecting the weakest signals feasible and extracting new scientific knowledge from these, I was asking myself the question: Who would be better prepared for the data challenges of the digital future than research astrophysicists with this kind of unique data science background? Since I could not come up with a convincing alternative answer, I consequently went for it myself and founded OmegaLambdaTec in 2015.

At the start, the specific use cases were still diffuse ideas, but I was totally convinced that our large research tool-box of data science methods we use and develop in our daily work as scientists could be easily applied to solve countless other existing and emerging data challenges in the real world. Now at OmegaLambdaTec, we use our data science and analysis expertise to identify and exploit digital data treasures for companies and institutions, and help them to get the maximum added value out of it.

Challenging new data problems, with potentially high business impact, are now popping up everywhere around us, especially in new innovative cross-disciplinary fields such as Smart Factory, Smart Energy, Smart Mobility and Smart City. We also see an increasing demand for new data-driven innovation from well-established industries such as banking, real estate and civil engineering.

It is fascinating that you bring methods developed in astrophysics to the business world. Where do you see the main benefits?

Well, first of all the digital data world becomes bigger, faster, and more complex every day. So, there are not a whole lot of people out there who are properly trained to deal with this data challenge, able to extract the maximum amount of information and value from large and complex data sets. Second, the digital data revolution tears down walls between formerly distinct, non-interacting business disciplines and branches.

What is more and more needed are allrounders with a broad analytic and technical background who can instantly switch and make connections between different topics & branches while still maintaining a global view of the big picture. We believe that trained astrophysicists combine these capabilities in an ideal way. The methods and theoretical tools we bring with us to the business world have the potential to transform many branches through expert analytics and data driven decisions. Actively harvesting all the available data sources is just at the start and the vast majority of potential data yields are still waiting to be transformed to economical advantage and new smart services.

What are OmegaLambdaTec’s next goals?

We have recently organized a successful Smart Data event at our homebase in Garching in order to push this key topic in the Munich area. Currently, we are working on establishing a few premium partnerships with customers in strategically important fields as our business base from which to grow. An important goal for us is to finish some more Smart Data reference projects, and complete the development of a few new key algorithms and methods to make ourselves more visible and known in our focus fields of Smart Energy, Smart Mobility, Smart City and Smart Factory. Everything else will then follow, but the start is of course always the toughest part of any new and ambitious journey!

What are the largest challenges businesses face, when trying to get value out of their data?

Finding the right talent to do it, and reaching a common cross-departmental understanding that data science comes with huge business potential. At the end of the day, the management as well as the different affected departments have to fully back new data driven approaches. The returned data science results may affect the work of some company employees in a significant way, and so not everyone in an organization may be happy and supportive to try out new concepts. So in summary, the commonly found ‘we-have-always-done-it-this-way’-attitude prevailing in many companies is the biggest obstruction to data driven innovation potential.

If you could give only one single advice to business how to get more value out of their data what would that be?

Let excellent external data scientists have a look at the available data to explore and evaluate its potential, and quickly outline ways to exploit these data treasures. Data driven innovations do need a secure and independent environment with a startup-mentality to develop and thrive. A ‘fail-often-and-fail-early’-approach to identify the next big Smart Data Application for a company is much more promising than preparing a single big IT project for a couple of years, only to find out that the new service is already offered by a smaller but faster company. There are no blueprints for the applications that will be possible with data in the future, so experimenting and testing will be key to identifying the next breakthrough innovations.

There is a lot of buzz in the industry about big data and data science. How can one distinguish between genuine expertise and cutting edge methods and “hot air”?

Well, as I said there are no blueprints for the upcoming data challenges and solutions of the digital future, so at this point in time I would always go for experienced researchers from hardcore data driven sciences. The data science field is so broad and profound that a University degree is hardly enough to bring the necessary breadth and depth. Excellent data scientists should have experienced cutting-edge research where they have demonstrated that they can independently develop new data analysis methods for problems that have never been solved before, rather than just being able to apply pre-defined tools to established and known data problems. So, a look at the publication record of a person will definitely help to identify real profound data science expertise, and to distinguish between real scientists and mere ‘data enthusiasts’.

What are the interesting trends you see now in the space of data analytics/big data?

One important paradigm shift that is currently happening is the transition from Big Data to Smart Data. After realizing that big alone does not necessarily provide much added value, the focus is now shifting to actually extracting the underlying business value using smart combined analyses that take into account all relevant available information. And of course, real-time and predictive approaches become more and more important. There are also amazing recent breakthroughs in the field of artificial intelligence, so a few years from now AI will likely allow applications that nobody has yet considered. However, I would always urge companies to harvest their low-hanging data fruits first, instead of trying to start the fruit-picking from the top of the tree. There is still so much data potential lying around everywhere we look, that does not require much effort to reach quick and significant data driven returns. So, the easiest way to adopt data science into an organization is bottom-up, starting with small demo projects that prove concepts and indicate possible returns, then moving towards more ambitious projects and goals.

Did you get venture capital or bootstrapped? Why?

For our current service-oriented business model we are bootstrapping in combination with a startup bank loan. Data Science-as-a-service does not have a specific real product behind it, which implies that it is less visible on the venture capital radar. The main advantage of our approach is that we have full control of which direction OmegaLambdaTec is heading and that we do it at a sustainable speed and growth. If in the future we decide to develop some of our ideas into real data driven products, then VC may become a viable option at some point.

What do you wish you knew before you started your company?

How to sell projects to big organizations and companies, which is a bit of an art with many possible obstacles on the way. Right now we are learning by doing with a steep learning curve on the sales side, but also with unavoidable beginners mistakes. On the other hand, sometimes it is better not to know the details of all the challenges ahead, otherwise one might decide not to go for it in the first place. The important thing is to be confident that there are no real show-stoppers or road-blocks in the path ahead.

What is your advice to scientist wanting to make it in the business world?

Learning to sell your work for money is something scientists never really practice in research, but this is likely something one has to learn on the job. Very important, however, are soft skills and the interaction with people to build trust-based working relationships as the basis for longterm success. Good scientists are high in demand in the current and future business world, so one should also not undersell oneself.


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