Im Michaela and 35 years old.

I have done my PhD in a research center in computational structural biochemistry.

Then I switched to industry.

I worked in a middle size company with a low maturity level in the Data Science & Analytics context.

This is why I faced a lot of resistance and had to learn to act sensitive and diplomatically as well as pragmatically also due to my scientific education and then prejudices stemming from it.

I worked as a Data Scientist as well as BI Developer.

I worked two times for only half a year in Consulting companys.

My Mentoring Topics

  • tips for the job search in the data environment
  • hints for stumbling blocks in the field of data (keyword: change management)
  • how to harvest the low hanging fruits
  • methods for evaluation of business kpis
  • business intelligence
  • data science
16.March 2023

She is a friendly mentor. She gave me a lot of useful advice relating to skills need for the data industry

11.February 2023

7.February 2023

Michaela helped me to better understand how data analytics projects with machine learning are executed. She walked me through the different phases of the project and pointed out the challenges and decisions that will have to be made. It has motivated me to start learning Python to try out creating a small scale predictive maintenance project. Michaela also advised me multiple tools and websites which can help me with my project.

Data Mesh - Delivering Data-Driven Value at Scale
Zhamak Dehghani

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale. Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance. Get a complete introduction to data mesh principles and its constituents Design a data mesh architecture Guide a data mesh strategy and execution Navigate organizational design to a decentralized data ownership model Move beyond traditional data warehouses and lakes to a distributed data mesh