Björn Höfer, Data and Analytics Director in the Munich office, shares his insights into and passion for the field of data science.


Meet Björn Höfer, Data and Analytics Director in Munich. 


Can you tell us a little about yourself? 

I grew up in the North of Germany in a small village near Osnabrück. Now I live near Munich with my wife and two kids. I’m deeply passionate about the field of analytics, especially causal AI. My current pet project is exploring ‘True Demand’, a demand forecasting approach that extracts trends from unstructured data and uses them to make demand forecasting more precise.


What first sparked your interest in the world of data and analytics?    

 During my studies in international business, I realised that I liked the statistics classes, which led me to take on additional ones. What piqued my interest was the application of multivariate analysis methods in the field of marketing, my major subject. The possibility of actually solving real-world problems with advanced analytics was completely fascinating to me and it still is.


How has working in data and analytics enriched your career? 

 Before joining Metyis, I managed analytics teams in market research, consulting, and the telecom industry. The advantage of doing analytics in consulting is that you have the outside-in view and the independence to also initialise the necessary structural changes within companies that are often needed to make a true impact with data. Working in the industry inspires you to develop strong domain knowledge and data understanding which allows you to create tailored approaches.


What is your current role at Metyis and how was your journey so far? 

I currently work as a Principal for Big Data & Analytics in the Munich office of Metyis. While I enjoy many facets of my work, I particularly am drawn to using powerful data analyses to resolve complex customer problems and surpass their expectations. Working with inspiring young team members and developing new possibilities to generate value from data with state-of-the-art algorithms brings me joy.

The nature of my work demands a wide range of responsibilities including developing propositions, writing proposals, managing projects or project streams, managing stakeholders, analysing data, presenting results, recruiting talent, and providing growth opportunities for existing team members, all of which I enjoy doing. I have had the opportunity to watch the growth of the Data & Analytics team here in the German office, which was truly satisfying.


How does your work at Metyis reward you at a professional and personal level? 

My work in the Digital Campus for our first Mexican client at Metyis was both challenging and fulfilling for me. Given that we were working on a suite of data and strategy topics considering a new and different cultural context was an insightful, perceptive, and fascinating learning experience for me.

At Metyis, I have the opportunity to bring together the best of both worlds by collaborating with clients in a partnership setup. You work like an internal employee, but you also have the independent mindset of an external consultant who brings in experiences from partnering closely with other clients.

From a slightly technical point of view, in my role, I have the flexibility to break down data silos and address management silos accordingly if needed. In many cases, this combination is crucial to make full use of analytical insights and ensure that data science-based recommendations are executed successfully.


How do you feel your team or function is making an impact on the business world through data and analytics?   

 Making an impact with data is still a challenging endeavour. It can easily fail even if one piece of the puzzle is missing. The three important pieces of that puzzle include innovation, implementation, and translation.

Innovation opens new opportunities to make an impact with data that was not possible before. The innovations that my team is developing are new ways of using data, for example, turning unstructured data like text and pictures into structured data using artificial intelligence. Critical ingredients to innovative solutions can range from aspects and sentiments generated from text data to product attributes extracted from pictures. 

Implementation allows us to grasp the opportunities and put them into practice using expertise by actually setting up an innovative solution. I find it interesting and perceptive to work with an international team that brings all the necessary technical and non-technical data science skills to the table that are needed for an effective implementation.

The translation is the final step to assure that the solution is adopted and can generate impact. In this step, my team uses smart and easy-to-understand visualisations that are tailored to the needs of the business users. By this means we ensure that the analyses are properly understood and that the decision-makers follow the data insights in the right way. 


What role does collaboration play within the field of data and analytics?     

In my opinion, collaboration matters in data and analytics primarily in two ways. Firstly, the collaboration within the data team is very important. Data analytics is a team sport. Different skills ranging from data architecture and data engineering to data visualisation and storytelling are needed to bring significant digital transformation to a company.  Secondly, the data team must collaborate with the business areas. Every insight generated and every machine learning model built needs to be integrated, ideally hardwired, into the business as usual. To make this happen an intense collaboration of the data team with the respective business areas is needed.  This collaboration should be trustful and open because both sides need to listen to and learn from each other. The data team needs to understand the business, and the business team needs to learn and understand how to work with the insights, tools, and recommendations that the data team provides. Metyis, I strongly believe, facilitates this collaboration.