How is your organisation using data and analytics to support the corporate vision and purpose?
Data and analytics are key for informed decision-making; they enable predictive analytics to foresee future trends and events. They are also key to measure the impact of initiatives implemented in the past to then evaluate their efficiency and effectiveness.
Data science is at the core of our organisation and represents significant opportunities for improvement of corporate decision making across industries, business functions and markets.
Some of these opportunities even have a strong disruptive and transformative character, opening up new approaches to old business problems and sometimes even allow us to tackle old and unsolved business problems for the first time.
2020 was a year like no other - how did it impact on your planned activities and what unplanned ones did you have to introduce?
The changes that 2020 brought due to the Covid-19 pandemic had a strong impact on our organisation. We adapted not only operating models but also business strategy and goals for 2020 early in the year. There were many lessons to learn along the way which led to positive outcomes, but it is also clear that Covid posed a significant challenge that needed to be overcome.
The increased use of virtual meetings and online collaboration has changed business in many ways for us. Not being able to meet face-to-face imposed real challenges when it came to important negotiations; on the other hand, reducing travel freed more time for online meetings with clients to speed up business development. We learned that client organisations adapted fast and were very open to virtual meetings which in turn helped us.
Looking forward to 2021, what are your expectations for data and analytics within your organisation?
We expect the data and analytics part of the organisation to continue strong growth in 2021. Recruitment seems unaffected by the pandemic, as far as we can tell at the moment.
Is data for good part of your personal or business agenda for 2021? If so, what form will it take?
Yes, data for good has been a topic that we had on the radar for some time now. Unfortunately, the topic had been deprioritised due to the pandemic. My hope is that we can reignite our ideas and potentially implement some in the second half of 2021.
What has been your path to power?
I have a background in mathematics and theoretical physics. I hold a PhD from Heidelberg University and spent three years as a researcher at the Massachusetts Institute of Technology, where I developed convolutional neural network approaches to analyse large quantities of biological data.
I have been leading data science teams in various capacities with different companies including SAP, where I was part of the global data science consulting team and Boehringer Ingelheim, where I built, developed and led the globally operating central data science capability. At Boehringer Ingelheim, my team successfully drove digital innovation across business functions and globally across markets on six continents.
I am the co-founder and CEO of quantLab, a German AI company that developed ground-breaking AI solutions for pharmaceutical organisations to transform customer engagements, optimise market access and supply chains and significantly improve the yield of production facilities. QuantLab became part of Boston Consulting Group in Q4 2020.
What is the proudest achievement of your career to date?
I have received a lot of positive feedback for building strong and outcome-driven data science teams with positive, open-minded team spirit.
My most recent achievement was founding and developing an AI company from scratch during the Covid-19 pandemic. The company’s AI solutions have created significant interest and a strong customer base within the pharmaceutical and healthcare space in a short time and led to the acquisition by Boston Consulting Group.
Tell us about a career goal or a purpose for your organisation that you are pursuing?
In the healthcare and biopharma space there are a lot of potentially useful data sets that still remain heavily under-utilised. For instance, real-world evidence data and clinical study data can be used much more efficiently to set up new clinical studies in a way they lead to stronger outcomes and require less experimentation on the human body (less patients). They can also speed up the approval processes and improve patient care and treatment in hospitals.
The data has many issues. It is fragmented, often poor-quality, and often relies on large amounts of unstructured text data. To make things worse, understanding highly complex medical and biological terminology is still a significant challenge for automated data processing to date.
I hope that we can tackle and solve some of these problems to advance the field and facilitate improvements for patients in the healthcare space.
How closely aligned to the business are data and analytics both within your own organisation and at an industry level? What helps to bring the two closer together?
Organisations in the pharmaceutical and healthcare space often lag behind when it comes to the quality and capabilities of their data science departments. On the other hand, the industry has a strong R&D focus which helps fact-based and scientific decision making to penetrate into business culture. At the moment, there are still many opportunities for improvements but there is a general consensus on the positive potential of data science in the healthcare and biopharma space.
What is your view on how to develop a data culture in an organisation, building out data literacy and creating a data-first mindset?
Developing a data culture in an organization benefits from a multi-tier approach. It is important to have the leadership, management and senior stakeholders on board to give data science a try, but it is then also key to show impactful use-cases quickly that demonstrate significant value for the entire organisation.
Once measurable value has been demonstrated it is easier to grow data science capabilities. To deliver successful use-cases is it important to have strong cross-functional collaborations between business partners and relevant stakeholders, data science, business analytics, external data providers and IT. Internal consulting capabilities can help play a role to deal with change management aspects of a transformation towards a data-driven decision making culture.