Philipp works as a Sales Engineer at Splunk. His background is in data visualization and analytics with experience in automotive, transportation and software industries. Philipp’s focus is to leverage Splunk as a data platform for analytics and visualization. He recently won the Deutsche Bahn Hackathon analyzing a 10GB data set around railway infrastructure in 24h (read more: http://blogs.splunk.com/2015/06/08/splunk-team-wins-db-infrastructure-data-challenge-in-24h-iot-hackathon/). In collaboration with Robotron Philipp is working on data mining approaches for the IoT and industrial data to optimize business processes.

We are proud to have Philipp presenting at Data Natives 2015!

Can you describe your professional journey up to the point of joining Splunk?

First of all thanks for this interview and I’m looking forward to Data Natives 2015! Before Splunk I worked as a freelance software developer and consultant on many interesting projects – mainly in the automotive and transportation industries. I was focused on real-time 3D visualization to make heterogeneous data accessible and meaningful – regardless of whether it’s car data or complex infrastructure planning projects. As well as that I was researching visual text analytics and published two papers. For the creative part I’ve been active in digital arts for years and realized many audiovisual and interactive art projects like http://www.cubeflow.de.

What kind of problems do you aim to solve as a Sales Engineer at Splunk?

With the variety of ways that Splunk software is used by our customers, I deal with many interesting and different use cases covering IT operations, security, application delivery and business analytics. Recently I’m working on more and more projects in the area of industrial data and the Internet of Things.

How has the field of business intelligence evolved over the last few years with the rise of ‘Big Data’?

I think there is a continued shift in BI since the rise of ‘Big Data’ as new data sources and types following the ‘four v’s’ principle (volume, variety, velocity and veracity) come into play and add substantial value to existing data. Technically, it can be challenging to get insights from such a changing data landscape quickly. This is where Splunk makes the difference: as a universal platform for machine data Splunk provides this flexibility due to late binding and a powerful search language to correlate and analyze heterogeneous data at large scale – including both real-time and historical information.

If you could apply the technology being developed at Splunk to any real world problem, which would it be and why?

That’s a funny question because in fact all the technology developed at Splunk is used to tackle real world problems: to prevent and detect cyber threats and fraud, proactively monitor IT infrastructures to find and fix errors quickly, analyze and visualize business processes, machine data and sensor data to get new insights. For Example Splunk software is used at the Police station in Chandler, in the USA: They evaluate data to support officers on patrol and monitor problematic neighborhoods. And the IT departments of financial institutions use the software to secure online payments.

What are the key lessons you’ve learned in your career? Biggest ‘Ah ha!’ moments or mistakes?

Expressing it for a real data native: It’s about 10% inspiration and 90% perspiration. If you want to achieve 100% you need to combine 90% and 10% wisely.

What advice would you give to technically minded youths looking to get their career started?

Keep an open mind and learn about different technologies to connect the dots.

Which companies individuals inspire you, and keep you motivated to achieve great things?

There have been many companies and individuals that inspired me in different parts of my career, but one of my great inspirations is people in history: philosophers, inventors, artists. Right now I enjoy working with brilliant people here at Splunk.