Data Science And Business Intelligence Projects: technology, business knowledge and the need for data science skills in the healthcare industry

By Manuel Bierwirth (Pharma MBA Class of 2020), Manager Healthcare Business Analytics at Merck Group

I started my professional career as a software developer. After a few years of software development, I switched to Project Management, where I soon became an IT-Manager, working with an offshore-based support team. I also had the opportunity to work in other countries and get to know different work cultures, people and projects within the United States and Latin America. For six years now, I have been working on projects related to healthcare business, business intelligence in pharma market data, market growth, brands and products.

From 2018 to 2020 I had the honor to successfully finish the Pharma MBA program at Goethe Business School. Through the Pharma MBA I got to know different business processes, helping me understand my business colleagues better. I’ve also learned to become a more knowledgeable counterpart in strategic and project related meetings while broadening a network of fellow students and friends for life.

Today, I am heading a team of architects, developers and business supply chain experts as project stream lead within a global rollout program of SAP + Business Intelligence (BI) processes for Italy and Switzerland healthcare affiliates within the Merck Group.

Coming from the information technology applications area on the one side and working within the healthcare industry and pharmaceutical processes and needs on the other side, requires a strong skillset in technology, project management as well as the ability and attitude to learn more and adapt to the business processes and fast-changing technology.

Due to my experience over the last few years, I was able to notice that the classic setup of business intelligence in IT software development projects is no longer sufficient. Why? Well, it has become clear to me that data provision and the calculation method alone is not enough anymore. Different areas of pharmaceuticals and life sciences collect and load data into so-called data warehouses via data extraction processes. These data warehouses clean, further enrich and further process data, perhaps mix them with other data, until data can be presented in an appealing form within the various and colorful world of BI reporting. As stated, today, this data provision and calculation method alone is no longer sufficient. Business meanwhile expects some form of data intelligence, automation, process controlling and automatic process improvement. This can be translated using the terms Artificial Intelligence, Business Process Automation, Process Mining and Machine Learning.

On the one hand, traditional business users now encounter the combination of well-known enterprise and market data with automation technologies and algorithms. On the other hand, the IT staff also has to deal with new application technologies in their environment that go beyond the pure provision of data and calculation of key performance indicators. The challenge is then no longer just to acquire the new technologies, but also to convey this new knowledge and the new processes to the project and development teams and users within a framework of suitable change management.

IT continues to be a sought-after domain, an important function in multinational industries. However, the necessary skillsets are changing for the classic software developer, the project manager and also the line manager. This means that these roles need at least a basic understanding of AI, machine learning and automation in the future. A deeper understanding for developers is required if data acquisition and data publishing is combined with machine learning technology. In addition, project managers should develop a strong understanding of change management, e.g., how to take classic user groups to faster and intelligent technologies without losing them along the way, which is typically part of the user adaption process within IT projects.

The data science community is still a relatively young working environment and not necessarily part of the IT organization. They will continue to be an important and most likely separate community. IT applications will work together with the data science community in digital transformation projects; But the classic IT roles will – at some point – have to learn at least a part of the data science community in order to be able to master the current and future challenges of business, IT and digital transformation projects.

Being an MBA student…becoming an alumnus…returning as guest lecturer… Goethe Business School continues to be a partner for me in the recent and upcoming years. For instance, I gave a guest lecture in an MBA module where I shared my expertise and experience as Manager Healthcare Business Analytics at Merck Group. The feedback and response for my topic was so great that I was able to further engage and discuss the importance and necessary change management of digital trust and trust in AI/automation within two courses of the Digital Transformation MBA and Pharma MBA program. The exchange with the two cohorts was fantastic; the groups and I had a lot of fun and I learned a lot from that exchange as well.

I am excited about what lies ahead, because I know that there is more to come! Within the new Data Science in Health certificate program – starting in September 2022 – I am running one expert slot on “Business Intelligence and AI use cases in Healthcare Marketing & Sales” in Module 2. Register for the new certificate program and join me in the fall to learn more about “Data Analytics & AI in the healthcare sector”!

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