Katrine’s editorial focus spans the full AI and data technology stack — from AI application and agent development to generative AI, LLMs, machine learning, MLOps, data engineering, cloud applications, business intelligence, and modern data platforms.
Katrine is particularly interested in helping businesses understand how AI and data technologies are actually built, integrated, deployed, and scaled. Her work translates complex technical concepts into practical information for technology leaders, business decision-makers, developers, and organizations evaluating AI and data solutions.
Editorial approach
Katrine believes that effective technology coverage should go beyond describing what a tool or platform can do. Her editorial work focuses on how technologies perform in real-world environments, what they require to implement, how they integrate with existing infrastructure, and where their limitations lie.
She oversees content covering AI development services, AI consulting, machine learning solutions, data engineering, analytics, cloud applications, and enterprise technology. Particular attention is given to architecture, scalability, security, integration requirements, implementation complexity, and long-term maintainability.
Under her editorial leadership, the publication aims to make complex AI and data topics accessible without oversimplifying the underlying technology.
Topics covered
Katrine oversees and contributes to content across the publication’s major technology areas, including AI development, AI app development, AI agent development, generative AI, AI chatbots, LLMs, machine learning, deep learning, and enterprise AI.
Her coverage also extends to the data layer behind modern AI systems: data engineering, data science, data analytics, data warehouses, ETL, data integration, data lakes, data architecture, data governance, data quality, and modern data stacks such as Databricks, Azure Data Factory, and dbt.
For business intelligence and analytics, she covers Power BI, dashboard development, data visualization, predictive analytics, and marketing analytics, with a focus on how organizations turn data into measurable business outcomes.
Editorial independence
Katrine maintains a strict separation between editorial and commercial interests. Service providers, software companies, and technology vendors do not influence editorial conclusions, rankings, comparisons, or recommendations.
Content is developed using publicly available documentation, technical research, product information, industry sources, and expert insights. Where appropriate, technical claims are reviewed against multiple sources to distinguish documented capabilities from marketing claims.