By the tip of 2025, at the very least 30% of generative AI initiatives won’t progress past the proof-of-concept stage, in line with a forecast by Gartner. This has been attributed to numerous points, together with poor information high quality, inadequate danger administration, growing bills, or a scarcity of evident enterprise advantages.
On the Knowledge Engineering Summit 2025, Sandesh Gawande, CEO of iceDQ, posed a query: If information groups have been monitoring high quality for over twenty years, why do AI tasks nonetheless preserve failing? With a background in mechanical engineering and a keenness for manufacturing unit metaphors, Gawande unpacked an issue many within the room had seemingly skilled however might not have framed it that manner.
He argued that the core difficulty will not be that organisations fail to measure information high quality. It’s that they measure it too late. By the point dashboards detect poor accuracy, lacking values, or inconsistencies, the harm is already carried out. AI fashions, like factories, depend on dependable enter and secure processes. When both falters, your entire operation will collapse.
The Manufacturing unit Mannequin
The failures will not be theoretical. Gawande pointed to TSB Financial institution’s IT breakdown, the place neglected information testing triggered a cascade of high quality points and fines nearing £50 million and a direct lack of £330 million. Notably, he identified the sobering twist that the mission’s CIO was personally fined for the oversight.
Different sectors haven’t fared higher both. The Titan submersible imploded with out ever being licensed for its working depth. On related strains, a Boeing plane misplaced a cabin door mid-flight.
Gawande burdened that, in each instances, the issue wasn’t a scarcity of monitoring; it was that the monitoring solely started after the methods had already launched.
Gawande’s framing of information pipelines as “information factories” helped underline what’s lacking in present observe. He described three distinct phases: assembling the system, working the processes, and inspecting the output. Most organisations deal with the third, the place conventional information high quality checks happen. Nevertheless, this, he mentioned, is like ready till a automobile rolls off the road to examine whether or not the manufacturing unit was calibrated.
“By the point you’re measuring, it’s too little, too late,” he mentioned. “What you’re measuring is the output generated by your system.”
He believes the issue evaluation takes place after the system has already failed. He likens it to being on a sinking ship—questioning the worth of being alerted to a system failure at that time—suggesting that, by then, disaster administration stays the one possibility.
As an alternative, information reliability begins within the growth section. He raised questions like: Are the pipelines examined earlier than they run? Are builders doing information testing equivalent to unit exams, integration exams, and correct model management? Are instruments embedded into the system that may flag course of failures earlier than they manifest as information points? If these steps are skipped, then even the best-designed dashboards received’t save the day.
The second section is manufacturing monitoring, the place information flows in from numerous sources, distributors, and methods. At this stage, reliability relies on the flexibility to detect malformed information, schema adjustments, and different failures, lots of which go unnoticed.
A pipeline may run twice or under no circumstances, or an empty file is likely to be loaded with out alert. In line with Gawande, these will not be uncommon exceptions—they’re widespread operational pitfalls requiring lively information monitoring, not autopsy studies.
Redefining Reliability for the AI Period
Most organisations declare to be managing information high quality within the third section, when outputs from the information manufacturing unit are inspected. Nevertheless, by then, as Gawande repeatedly emphasised, it’s typically too late. Worse, these late-stage measures are likely to dominate information governance conversations, skewing the main target away from prevention and extra in direction of patchwork fixes.
The distinction between high quality and reliability, as he put it, lies in time. “High quality is an occasion in time. Reliability is the supply of that high quality persistently over time.”
To drive the purpose house, he provided a battlefield analogy: “Would you go to struggle with a high-quality sword that may break after two strikes or a dependable one that doesn’t break?”
He famous that the sword might need appeared nice in exams, but when it shatters mid-battle, it betrays the consumer. The identical holds true for brittle information pipelines.
This shift requires a unique cultural mindset. High quality assurance groups should transfer past UI testing and be taught to validate pipeline logic, enter integrity, and higher monitoring within the preliminary phases of constructing issues.
He highlighted that 80% of information defects could be prevented throughout growth, and one other 15% throughout operations. That leaves lower than 5% to be caught by way of conventional information high quality checks and information observability.
Enterprise customers should take part in defining guidelines, equivalent to “a product can’t be shipped with out an order”, and guarantee these guidelines are embedded into the workflow immediately. Programs have to be designed to cease when such enterprise guidelines are violated.
None of this, he mentioned, occurs by chance. “Unhealthy high quality is an accident. Dependable high quality is by design.”
He referenced the Bhagavad Gita to underline the strategy: deal with the method, and the end result will comply with. Good information reliability requires this triad—individuals, course of, and instruments—to be aligned from the beginning.
A crucial ingredient entails cultural shift from administration encouraging sound engineering practices and coaching enterprise customers, builders, testers, and information pipeline engineers.
Finally, establishing sound Knowledge Reliability Engineering (DRE) observe is crucial for making certain the supply of dependable information, which is high quality information maintained over time.
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