Every company running SAP has data quality issues, most of them small enough to live with. But point an AI model at that same data and ask it to decide faster than any person could, and those small inconsistencies stop being harmless. David Nelson looks at why the model gets all the attention while the data underneath it still gets ignored, and what Norsk Hydro's own experience says about actually fixing that.

Recurring SAP data issues often lead to reactive fixes. This post outlines a six-phase path to move from cleanup to long-term SAP data governance, with practical steps to improve data quality over time.

Many organisations define Data Owners and Data Stewards, yet Data Quality still doesn’t improve. This article explores why accountability on paper fails when the business lacks the power to act—and what needs to change for a true Data Quality culture to take hold.

Many companies invest heavily in S/4HANA but see progress stall before real work begins. Poor data is often the cause. It slows reporting, affects daily processes, and creates extra manual work. This article outlines five practical truths about fixing SAP data fast—starting with a focused 28-day pilot and continuing with ongoing, business-driven data quality work.

Is your data ready for AI? Many Nordic organizations are exploring automation, analytics, and S/4HANA, but unreliable SAP data often holds them back. This article explains why trusted, well-governed data is the foundation for successful, compliant, and scalable AI adoption.