Improve Data Hygiene, Overcome the AI “GIGO” Problem

For most enterprises, AI has moved well past pilots and proofs of concept. Organizations are racing to put models into production to run their supply chains, elevate customer experiences, detect fraud, and drive revFor most enterprises, AI has moved well past pilots and proofs of concept. Organizations are racing to put models into production to run their supply chains, elevate customer experiences, detect fraud, and drive revenue. But as AI adoption accelerates, one truth is becoming abundantly clear: Bad data leads to bad AI outcomes. Tarun Sood, Chief Data and AI Officer at American Century Investments, summed it up during a 2024 RSM executive panel: “If your data is bad, AI is just going to magnify it and show how bad.”

When it comes to AI, what looks manageable in a sandbox can become a liability in the real world if it’s built on bad data—slowing decisions, skewing results, and multiplying retraining cycles. Gartner forecasts that at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025 due to poor data quality, inadequate risk controls, escalating costs, or unclear business value.

At the same time, organizations must keep pace with emerging and stringent rules for AI and data governance. The EU AI Act, which took effect in August 2024 and is being rolled out in phases, allows for fines of up to €35 million or 7% of a company’s worldwide annual revenue for the most serious violations. Emerging U.S. frameworks—like NIST’s AI Risk Management Framework, new federal guidance (OMB M-24-10), and state laws such as Colorado’s AI Act—are also raising the bar on documenting data lineage, data quality, and overall governance.

Watch Rob Lee discuss the importance of getting data in order before deploying AI 

When Dirty Data Derails AI

So, how does data become dirty—or “unhygienic,” in an AI context? It happens in many ways, and in production AI, small flaws can scale fast. Here are the failure modes most commonly seen once models leave the lab—and the problems they create:

• Duplicates and near-duplicates: Inflate workloads and skew counts
• Inaccuracies (outdated, mislabeled, or misentered): Bias training and degrade predictions
• Schema and data drift: Creep in as sources change, breaking features that worked in testing
• Data bloat (e.g., oversized and low-value payloads, redundant logs): Slows compute-intensive training and retraining
• Incompleteness: Leaves critical gaps that propagate through pipelines and models, causing downstream errors
• Mismatched formats across legacy systems and the internet of things (e.g., encodings, units, missing metadata): Demand costly reprocessing
• Unstructured and multimodal AI inputs: Require reliable labels and alignment (e.g., transcripts synced to video frames, consistent image annotations)
• Label noise: Introduces subtle errors when annotations are inconsistent or incorrect, undermining model accuracy
• Time skews and late-arriving data: Distort features and evaluation, especially in streaming and real-time systems
• Compliance gaps: Raise risk as regulators scrutinize not just what data is used, but how it’s collected, documented (including its lineage), retained, and accessed

These issues underscore why effective data hygiene must transition from a one-time cleanup process to continuous care—anchored by auditing, governance, and automated monitoring.

From Cleanup to Continuous Care

Shifting to continuous care starts with a simple inventory: what data exists, where it lives, who owns it, and how often it changes. That list becomes a source of truth and drives clear rules for what to collect, how long to keep it, and when to delete it so old or low-value data doesn’t linger.

The next step is to put rules and roles to work. Define who does what, when, and with which data. Establish basic data contracts so fields and formats are consistent, and enforce policies in software so access, retention, and lineage are applied the same way every time.

Finally, use automation to make the process continuous. Let tools spot anomalies, remove duplicates, and rate data quality in real time; catch changes in the data or its structure before features break; apply rules across systems; and log where data comes from and where it goes.

That’s why clear rules and automation turn data hygiene into an ongoing discipline—not a one-off cleanup. And as requirements for lineage and documentation tighten, strong hygiene supports operational readiness and audit preparedness. However, it does require commitment and focus. 

As Amy McNee, Senior Vice President of Solution Architecture at Informatica, explains in an article for CDO Magazine: “It’s one thing to extract some data and clean it up once for a pilot. It is a whole different ball game when you want to operationalize that into a repeatable process.”

Where Data Hygiene Is Headed

AI deployments are becoming increasingly complex, and the bar for clean, well-managed data continues to rise. That, in turn, is pushing many organizations to prioritize continuous oversight of data hygiene. In a FirstEigen blog post, Chief Technology Officer Angsuman Dutta observes, “Data quality is evolving from a manual, back-office function to a core business priority.” 

In practice, that shift shows up in a few ways:

• Data lineage and provenance are table stakes. Teams need to know where data originates, how it’s transformed, and which models it feeds. Good lineage supports EU AI Act compliance and speeds root-cause analysis when outputs appear incorrect.
  
• Real-time monitoring is replacing batch-only checks. Continuous controls detect drift between training and production data, flag anomalies as they emerge, and score incoming streams for reliability—akin to always-on cybersecurity.
  
•  Multimodal inputs increase complexity. When models combine text, images, video, and audio, inconsistent or improperly labeled inputs can lead to fragmented and misleading results (e.g., transcripts not aligned with video frames).
  
• Synthetic data must be validated with the same rigor as real-world data. Hidden bias or inaccuracies in generated sets can quietly erode model accuracy.

Even with strong cleaning and monitoring, model testing—using separate test data, public benchmarks, and A/B trials—can get skewed if any of that test material ends up in training. That’s why many teams now make data decontamination part of their broader data hygiene process and AI evaluation practices.

The Value of Data Decontamination

Data decontamination removes test material from training sets so results reflect how a model handles new, unseen inputs, not information it has already been exposed to. Unlike general cleaning, which fixes errors and inconsistencies, decontamination safeguards evaluation integrity.

Typical steps for data decontamination include scanning for exact or near-duplicate matches between training and test data, removing or editing any overlapping content, and inserting unique “canary” strings into tests to verify whether any of them have leaked into the training data.

For enterprises, decontamination is part of governance and compliance. Maintaining clear records of what data was used, how it was handled, and how leakage was avoided keeps evaluations defensible and reinforces confidence in the model. Beyond testing, the same principle holds in production systems like retrieval-augmented generation, where reliability depends on data quality.

Keeping RAG Systems Reliable

Retrieval-augmented generation (RAG) grounds AI models in trusted internal knowledge. However, if the knowledge base is redundant, outdated, or misformatted, RAG can surface inaccurate results—and present them as if they were reliable.

Reliability starts at the source. Remove duplicates, keep metadata consistent (titles, owners, dates, access), and set clear rules for what’s allowed into the index. Keep it fresh with regular re-indexing, breaking long documents into manageable chunks, versioning them properly, and enforcing access controls so models don’t retrieve what users shouldn’t see.

Closing the loop is just as important. Requiring citations in responses, flagging results that are wrong or out of date as signals for improvement, and feeding that feedback back into the system helps clean noisy documents upstream. When content is current, structured, and maintained with ongoing care, RAG becomes far more reliable.

Discover how RAG, powered by Pure Storage® FlashBlade®, is transforming financial AI.

Audit-ready Data, Production-ready AI

AI audits are moving quickly from theory to reality. Regulators and frameworks now expect clear proof of how models are trained, deployed, and monitored—backed by documentation of data lineage, governance policies, and quality controls. Treating data hygiene as part of compliance turns audits from a mad scramble into a repeatable process that also helps build trust with customers and investors.

The benefits extend beyond compliance. When hygiene issues are addressed, data is timely, consistent, accurate, and complete—stored in standardized formats, free of duplicates, and validated against trusted sources. Clean data is then ready for production AI, enabling more reliable insights, shorter retraining cycles, and resilience under regulatory scrutiny.

Find out more about data-reduction techniques like data deduplication.

Infrastructure for Clean, Production-ready AI

The costs of dirty data are real. Gartner estimates that poor data quality drains an average of  $12.9 million a year per enterprise—and that burden grows in production as retraining cycles multiply and risks escalate. Meanwhile, in the “CDO Insights 2025” survey, 92% of data leaders expressed worry that AI pilots are advancing despite unresolved data issues, and 67% said they haven’t moved even half of GenAI pilots to production—often due to data quality, completeness, and readiness issues.

The takeaway: Poor data hygiene doesn’t just slow innovation—it stalls it, introduces risk, and erodes the bottom line. To scale production-ready AI, organizations need modern infrastructure. That means platforms that maintain data quality across environments, support governance and compliance, and deliver consistent performance for AI workloads.

Pure Storage helps teams maintain consistent and synchronized data from edge to core, ensuring that real-time decisions aren’t based on stale or fragmented inputs. Pure Storage AI-native capabilities reduce redundancy, apply built-in data quality checks across iterations, and enforce policy-driven governance that’s aligned with evolving regulations such as the EU AI Act. And because speed matters, Pure Storage delivers low latency and high throughput, enabling faster training, shorter retraining cycles, and better accuracy.

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