Stay ahead with the inics blog
We regularly publish insightful articles on key topics related to data management, business intelligence, and data analytics—keeping you informed and ahead of industry trends.
From Reporting to Decision Support - Why adaptive orientation matters when KPI autopilot fails
Too often, it was treated as if enough data, enough dashboards, and enough automation would eventually make decisions almost self-executing. That was always too simplistic. But once you stop confusing data-driven work with decision automation, the real question becomes much more practical: "What actually helps an organisation make better decisions when the old reading model starts to weaken?"
Data-Driven Decision Making: What It Really Means When the Environment Stops Behaving
Part 1: Why “data-driven” was often mistaken for decision automation, and why the real challenge is deciding whether your current signals still capture enough of reality to guide action.
Data Mesh, Data Meh? Why Many Companies Need to Reassess Their BI- and Data Organization
A few years ago, Data Mesh was, for many companies, above all a compelling target vision. More ownership in the domains, fewer central bottlenecks, more product thinking, more scalability. On strategy slides, it sounded modern, ambitious, and long overdue.
AI Use Case Inventory and Governance - Portfolio, Roles, Obligations
Part 3/3 concludes the series with the question that, in practice, often needs to be answered first:
Traceability by Design: Auditability Is Architecture, Not Documentation
Part 2/3 of the EU AI Act Series In Part 1, we focused on data quality. Part 2 builds on that. Because even with good data, the same question almost always arises in practice:
EU AI Act Art. 10: Data Quality That Withstands Audits
What data engineering must deliver before the high-risk rules take effect (Part 1/3). In many organizations, data quality was long treated as a hygiene topic: important, but rarely decisive. With the introduction of the High-Risk rules, data quality becomes verifiable. It must be measurable, controllable, and evidentially demonstrable in operations.
Market Correction as a Strategic Opportunity
Why Ownership and Stability Are Becoming the New Currencies in BI & Analytics.
The 2025 Cloud Cost Crisis: Why Data, BI and AI Teams Must Act Before 2026
Cloud cost management collapsed in 2025 and almost nobody was prepared. According to Flexera, 84% of organisations now say cloud cost optimization is their top cloud challenge.¹ BCG reports that studies indicate up to 30% of cloud spend is wasted.² TechRadar reports that 94% of IT leaders struggle to optimize cloud costs, with limited visibility and unexpected cost fluctuations remaining persistent challenges.³ These numbers defined our year and they match what we saw across dozens of BI, data engineering, and AI environments. Cloud has become unpredictable and unpredictability is now a financial risk.
When Success Isn’t a Fixed Number - Measuring Success in a Non-Deterministic Data World
In traditional BI systems, success once seemed easy to measure: A dashboard saves time, automates reports, and reduces error rates. But even there, evaluation was never truly straightforward. How do you measure a better decision? Or the value of insights that prevent errors from occurring in the first place? Even in classical BI, it was never just about numbers, it was about decision quality and impact.