Data Quality Before AI: Why Bad Master Data Slows Every Automation
AI does not fix bad data — it launders it into convincing-looking results. Why the bottleneck is rarely the model.
Insights, tutorials, and news from the world of software development.
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AI does not fix bad data — it launders it into convincing-looking results. Why the bottleneck is rarely the model.
Recurring document work is the fastest AI ROI in SMEs — but only with a control point. Why automation does not mean autonomy.
E-invoicing is mandatory in Germany, not a checkbox. The hard parts are format correctness, the DATEV handoff and audit-proof archiving — not a PDF with XML stapled on.
The cloud you choose is a chain of subprocessors you inherit. Evaluate the chain, not the logo — and the region, not the promise.
The grown Excel is the undocumented specification. You don't rebuild everything — you retire the most painful process and keep Excel running until the new path is proven.
In the proposal, speed and risk collide. AI shortens the path from inquiry to a defensible draft — the final commitment stays with the human.
Headless is not automatically better SEO. Built badly it ranks worse than good WordPress. When the complexity pays off — and when not.
The 80-page spec feels safe and is often the most expensive mistake. How a software project really starts — with the riskiest assumption first, not the thickest document.
Replacing the whole old system at once is the most expensive and riskiest path. How to retire Excel, Access and ERP legacy step by step — without endangering operations.
Not patriotism but a risk calculation. Proximity lowers nameable risks — language, time zone, jurisdiction, accountability — not a flag.
The most expensive cost driver is not the build but two code bases forever. How to decide between native, cross-platform and web app — by need, not trend.
The launch is the start of operations, not the finish line. Without monitoring the customer discovers the incident, not you — and the detection time is the real cost.