{"id":40976,"date":"2026-01-19T10:13:05","date_gmt":"2026-01-19T09:13:05","guid":{"rendered":"https:\/\/www.flynex.io\/news\/datenintegritaet\/"},"modified":"2026-02-05T11:49:52","modified_gmt":"2026-02-05T10:49:52","slug":"data-integrity","status":"publish","type":"post","link":"https:\/\/www.flynex.io\/en\/news\/data-integrity\/","title":{"rendered":"Why Data Integrity Determines Whether Infrastructure Digitization Succeeds or Fails"},"content":{"rendered":"
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Every year, companies worldwide waste <\/span>$2.3 trillion on failed digitization projects<\/a>. The numbers are sobering: According to<\/span> <\/a>McKinsey<\/a>, 70% of all digital transformation initiatives fail to meet their objectives. For digital initiatives overall, <\/span>Gartner<\/a> reports a success rate of just 48%.<\/span><\/p>\n<\/div>\n

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The problem rarely lies with the technology. Drones work. AI analytics work. What doesn’t work: the path from data capture to business decision.<\/p>\n<\/div>\n

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Where Data Integrity Breaks in Practice<\/h2>\n<\/div>\n
Energy providers, telecommunications companies and infrastructure operators invest heavily in drone technology. Capture runs smoothly. The data exists. But between the memory card and the maintenance decision, a data graveyard emerges.<\/div>\n
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The core problem: Data integrity is understood as a snapshot, not as an end-to-end process.<\/p>\n<\/div>\n

Industry experts identify four critical breaking points:<\/div>\n
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1. Inconsistent Capture Standards<\/h3>\n<\/div>\n
Different pilots, different flight altitudes, different overlap rates. The result: data that’s neither comparable between assets nor across time periods. Without standardized capture, reliable trend analysis is impossible \u2014 and neither is objective condition assessment.<\/div>\n
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2. Isolated Data Instead of Asset Context<\/h3>\n<\/div>\n
Drones deliver thousands of images. But which image belongs to which tower? To which line? To which asset component?<\/div>\n
Research on integrated infrastructure asset management identifies data silos as one of the biggest obstacles to effective maintenance. Without automatic asset assignment, data lands in generic folders \u2014 unusable for structured analysis.<\/div>\n
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3. Manual Analysis Bottlenecks<\/h3>\n<\/div>\n
The reality of many inspection programs: Two employees manually evaluate tens of thousands of images. While they work, new inspection orders pile up.<\/div>\n
According to Energy Connects<\/a>, inspection backlogs in critical infrastructure grow daily \u2014 and many operators underestimate that it can take years to catch up using traditional methods.<\/div>\n
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4. Media Breaks During Integration<\/h3>\n<\/div>\n
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Findings are manually transferred to Excel lists. Coordinates typed in. Priorities subjectively estimated. Every interface is a risk point.
\nExperts on inspection databases warn: Isolated information pools accessible to only a few stakeholders prevent timely, informed decisions \u2014 even when the data contains critical insights about asset conditions.<\/p>\n<\/div>\n

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What End-to-End Data Integrity Really Means<\/h2>\n<\/div>\n
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Data integrity is more than “correct data.” It encompasses four dimensions:<\/p>\n<\/div>\n

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