Post by Michael Cherner

How embracing a data science mindset transformed our approach to GIS digikoo GmbH, a company heavily involved with geo-data in the energy and public sectors. Initially, I found it challenging to bridge my data science background with the company's GIS-centric workflows. However, my team and I quickly discovered that our data science expertise wasn't a roadblock—it was our greatest asset. Here's what we learned on our journey: Adaptability is key: My team of data scientists, many with little prior GIS experience, adapted remarkably fast. This proved that a strong data science foundation can be applied to solve complex problems in new domains. Scalability drives value: We successfully demonstrated how scalable calculations in Python and SQL could address significant GIS challenges, delivering cost-benefit improvements that were previously out of reach. Cloud-native is the future: A year ago, we migrated our central data store from PostGIS to Snowflake. While a challenging transition, this move enabled us to unlock even greater scalability for our algorithms and pipelines. Customization closes the gap: Despite powerful tools like Snowflake, we found that traditional Business Intelligence (BI) tools like PowerBI or Superset couldn't match the speed and specialized visualization features of QGIS. This led us to develop our own custom solution digipad to provide the flexibility our users need. My biggest takeaway: Data science and GIS aren't separate fields—they're a powerful combination. By approaching GIS with a scalable, data-first mindset, we can drive innovation and create more effective, high-impact solutions. What unexpected synergies have you discovered between different tech domains? #EONPlaymaker