A journey into the future of data management
Mahmoud Kenawi - Senior Data and Digitalization Consultant at Cegal
From remote sensing to data-driven innovation - A journey into the future of data management!
With a PhD in geographic analysis of land use for renewable energy in Norway nearing completion, the journey into Data Management has been anything but random. Working with remote sensing and geoprocessing, I discovered early on the power of data, and how the right processes and technology choices can transform raw information into insights with value.
My path into data management started with collecting and analyzing remote sensing data using machine learning and convolutional neural networks (CNNs). This gave me insight into how complex, visual data structures could be interpreted, processed and used in practical planning. Eventually, geographic analysis and data-driven post-processing became a natural extension of the work. When I joined Cegal in November 2024, I took this knowledge with me into new challenges, such as handling different datasets in the cloud, automating workflows and building APIs with Python as a toolbox.
Already during my master's studies, I got a taste of what data management could be. I worked with hourly hydropower production data over a long period of time and learned how multiprocessing and distributed computing could optimize the use of resources.
That's where my interest really kicked in - in the face of real, complex data and the ability to extract tangible value from it.
One of the most demanding datasets I've worked with was high-resolution remote sensing data. The challenge lay not only in the size, but in the requirements for efficient processing. I had to learn how to maximize available resources, combine different tools and use High-Performance Computing (HPC) to run parallel simulations. It was a steep learning curve in building scalable workflows, and in thinking tool-independently and adaptably.
Data management in change: quality, scalability and future technology
Today, the amount of data is growing exponentially, but the challenges are not just about volume. It's just as much about ensuring data quality, availability and scalability. With ever-larger and often unstructured datasets, the need for clean, well-documented and interoperable processes is increasing.
Smart data structures, automation and cloud-based solutions are crucial, especially when it comes to extracting value in real time.
The transition from on-premises computing to the cloud has revolutionized how we work. Previously, most processing was done on a single machine or in local HPC clusters. Now we're working in cloud-based environments that offer greater flexibility, collaboration and scalability, but also require continuous updating of tools and methodologies.
Looking ahead, I believe quantum computing, and especially quantum machine learning, will be among the most transformative technologies. Even though it's still early in its development, great progress is being made on the hardware side. Quantum technology has the potential to lift processing, pattern recognition and optimization to a level that far exceeds what classical systems can handle. It opens up new possibilities - and new ways of thinking about data.
My role in Cegal: variety, value creation and cloud-based solutions
What I find most exciting about working in the Data Management team at Cegal is the variety of tasks and challenges. Every day is different and requires me to be flexible, curious and willing to learn. It's precisely this dynamic that makes the job so rewarding, both technically and personally.
Through my expertise in analytics, automation and cloud-based processing, I can help Cegal's customers extract more value from their data. It's about improving efficiency, delivering faster and more accurate results, and structuring data to make better decisions.
What really sets Cegal's approach apart from many other players is our focus on cloud-based and modern solutions that not only store data, but give our customers flexibility and real-time access. By combining this with automation and smart integrations, we can extract value faster and more securely than traditional, on-premise solutions allow.
Northern lights and data structure
When I work with data, there's one thing I'm particularly passionate about: structure. Organizing data in a way that makes it easy for different users to extract exactly what they need, that's a satisfying challenge. It's not just about order, but about making information accessible and valuable.
When I'm not working on data structure and automation, I'm looking at the sky, literally. Astrophotography and northern lights hunting are among my greatest hobbies. There's something magical about combining technical precision and nature experiences, and perhaps not surprisingly, it's also about light, patterns and data capture - just in a slightly different form.
Northern lights in Oppdal.
