Messy Data, Messy AI: The Data Quality Challenges That Undermine Your AI Strategy

AI may be advancing fast but without quality data, it won’t take you far. 

While AI is only as smart as the data it learns from, many organizations still overlook this simple truth and face several negative consequences. As organizations rush to deploy AI solutions, poor data undermines trust, delays projects, and inflates costs.  

What many treat as a backend technical detail is actually the strategic foundation of any successful AI program. Without clean, accurate, well‐governed data, even the smartest models stumble. High-quality data doesn’t just improve results, it makes AI trustworthy, scalable, and truly valuable. 

So, how can you ensure high-quality data in AI? We’ll explore this in the article below. Let's start with the basics: Just how important is data quality in the age of AI?  

Why data quality matters in the age of AI 

AI systems don’t create knowledge from thin air; they learn by identifying patterns in their training data. When the underlying data is incomplete, messy, or biased, the outcomes of AI models reflect those flaws, quickly becoming unreliable. In other words, poor data in, poor predictions out. 

This isn’t just a technical issue. It’s a business one. 

According to Forrester, more than a quarter of global data and analytics employees who struggle with poor data quality estimate it costs their companies over $5 million a year. Moreover, 7 percent say the losses go above $25 million. When AI relies on that same bad data, the impact multiplies. 

High-quality data does more than keep models accurate, improving AI in several ways by: 

• Leading to more accurate predictions and recommendations. 
• Enhancing the effectiveness of automation and personalization. 
• Reducing rework, wasted compute, and model drift. 
• Helping AI systems scale across teams instead of getting stuck in pilot mode. 
• Giving business leaders confidence to trust and act on AI insights. 

Juan Sequeda, head of the AI Lab at data.world, talks about accessibility and clarity to make data AI-ready during the Agentic AI Summit: “Find data that already has well-defined semantics, good column names, and clear descriptions. That eliminates ambiguity and lets AI start working effectively from day one.” 

Data quality in AI isn't an afterthought. It’s the foundation that allows AI to move from pilot projects to trusted and scalable, real-world deployments. 

Compliance and ethical considerations 

AI systems are built on data, making compliance and ethics impossible to ignore. When data is inaccurate or inconsistent, the consequences go far beyond technical errors. Organizations can face penalties, lawsuits, and lasting damage to their reputation.  

Let’s talk about the compliance and ethical considerations organizations face while ensuring data quality in AI. 

• Regulatory pressure: Rules such as GDPR, CCPA, and the EU AI Act hold companies accountable for how they handle data. Poor-quality or biased data can quickly become a compliance issue, resulting in fines that can reach millions. ‍
• Ethical responsibility: Laws are only one part of the picture. Companies are also expected to follow principles of fairness, accountability, and transparency. That means ensuring AI doesn’t discriminate, explaining its decisions, and protecting users’ privacy. ‍
• Traceability: To meet both legal and ethical standards, teams need to know where their data comes from, how it’s been processed, and how it feeds into AI models. Lineage tracking gives that visibility and makes it possible to justify decisions when questioned. 

A simple example shows what is at stake. If a recruitment AI is trained on biased historical data, it may screen out qualified candidates. The result is not only unfair but also opens the company to regulatory scrutiny and public criticism. 

Strong data quality practices reduce these risks. They help ensure AI is both compliant with the law and aligned with the values that customers and regulators expect. 

Now, we can take a closer look at some of the key challenges companies face while ensuring data quality in AI. 

Key challenges in ensuring data quality in AI 

Building reliable AI starts with reliable data. But keeping data clean, consistent, and trustworthy isn’t easy. Here are some of the main challenges organizations face: 

• Fragmented and inconsistent data sources
  Data doesn’t just come from a single source. It’s scattered across databases, APIs, connected IoT devices, and live streams. Each comes in a different format and often with duplicates or inconsistencies. Moreover, real-time streaming adds another layer of complexity because of the speed and volume of data being processed.  

Juan explains this challenge in practical terms: “People try to point their LLMs to a table, but the table has different column names and no description. The AI ends up guessing, and the answer isn’t great. When multiple tables need to be joined, it gets even more complicated.” 

• Manual, error-prone labeling processes
  AI models need labeled data to learn. Labeling is often manual, which makes it slow, costly, and error-prone. Even small mistakes can compromise model accuracy. Scaling labeling across millions of records is one of the most challenging tasks in AI projects. ‍
• Lack of data provenance and lineage
  It’s not always clear where data comes from or how it’s been changed. Without that visibility, teams can't explain model outputs or trace errors back to their source. Juan stresses the importance of context and semantics here: “Building a semantic layer or ontology gives the AI the context it needs to generate accurate queries. Without it, even simple questions may yield wrong results.” ‍
• Balancing access with privacy and compliance  
  AI models perform better when they have access to large and detailed data sets. But privacy regulations such as GDPR and HIPAA restrict how personal or sensitive data can be used. Companies need to protect user privacy while still giving AI systems enough data to learn. Synthetic or anonymized data sets can reduce risk, but they often lose detail and cannot fully replace real data. ‍
• Hidden bias in training data  
  Bias is one of the most visible risks in AI. If training data underrepresents a group, the model’s decisions will reflect that gap. Facial recognition has already shown the consequences, with far higher error rates for women and people of color. That creates not only ethical problems but also legal and reputational ones. 

Let’s explore the best practices to overcome data quality challenges in AI.  

Best practices to improve data quality for AI 

Good data quality takes deliberate work. It isn’t solved with one tool or a single project. Below are some practices that can help ensure data quality when building AI systems. 

Data governance frameworks 

Good data governance is important to ensure AI data quality. It defines who owns the data, how it should be used, and what standards apply. Governance offers clear data management rules that help to reduce inconsistencies and compliance risks. In one Statista survey, 93 percent of companies in the resources industry reported they had adopted governance measures for AI. Also, assigning data stewards or owners can help to ensure accountability. 

As Juan mentioned in the Summit, “Governance is not just compliance. It’s about enabling people to do amazing things. Treat governance as a product. Start small, tied to real business value.” 

Modern data quality tools and platforms 

Manual checks don’t scale. Tools that profile data, flag anomalies, and remove duplicates can catch errors before they impact models. Many platforms now use AI to monitor data in real time. For example, Domo applies anomaly detection and deduplication across integrated data streams, helping teams fix issues at the source. 

Cultural shift: Training and awareness 

Data quality isn’t just the responsibility of engineers. Analysts and business teams also shape inputs. It’s important for teams across various departments to be on the same page regarding data collection and labeling practices. Engineers, analysts, and business users should receive regular training to enhance their awareness and make data quality a shared responsibility across the organization. 

Continuous monitoring and evaluation 

Data isn’t static. Formats change, sources evolve, and drift happens. Setting up monitoring is the only way to catch those changes before they break a model. Dashboards that track quality metrics make it easier to identify shifts and fix them quickly. 

Collaboration with external data providers 

Third-party data sets expand model training but also introduce risk. Vetting providers is essential. Contracts should include quality service level agreements (SLAs) that define standards for accuracy, update frequency, and delivery. This ensures external data meets the same bar as internal sources. 

Documenting and testing data quality 

Data testing works like software testing, ensuring data is consistent and reliable. For better transparency, companies should document their data sources, transformations, and quality checks. Moreover, the unit tests for data pipelines catch errors early and reduce the risk of faulty AI outputs. 

Trends shaping data quality in the age of AI 

Data quality practices are evolving as AI becomes central to business operations. Here are the key trends shaping this shift. 

• AI for AI: AI tools are now used to improve data quality itself. Machine learning models can detect anomalies, fix duplicate entries, and flag potential biases. This reduces manual work and helps maintain cleaner data sets. ‍
• Automated data cleaning pipelines: Organizations are increasingly adopting automated data cleaning pipelines to run checks and catch errors early. Continuous integration makes the process faster and more reliable. ‍
• Synthetic data and augmentation: Synthetic data is gaining popularity as a way to expand data sets while protecting privacy. It helps boost diversity, reduce bias, and fill gaps where real data is limited. ‍
• Data observability: Data observability tools provide teams with full visibility into their pipelines. They track freshness, completeness, and reliability, enabling teams to identify issues before they impact AI models. ‍
• Shift-left mindset: Data quality is being embedded earlier in the data lifecycle. Similar to DevSecOps, this “shift-left” approach ensures quality checks start from the point of collection. ‍
• Ethical AI and responsible data: Ethics is becoming part of data quality. Many companies now appoint data ethics officers and create cross-functional teams. Their role is to ensure fairness, accountability, and responsible use of AI data. 

Let’s see how Domo can help teams ensure data quality in AI. 

How Domo supports AI-ready data quality  

Strong AI outcomes start with strong data foundations. So, how can teams actually deliver AI-ready data? They need the right platform to make best practices part of everyday work, and Domo is built to do exactly that.  

Here’s how: 

• Real-time data quality control: Domo gives teams visibility and control over their data so quality isn’t left to chance. Profiling, cleaning, and governance occur in real-time, making it easier to address issues before they are incorporated into models. ‍
• Smarter data preparation with Magic ETL: Domo automatically handles duplicate records, missing values, and inconsistent formats, ensuring data sets are reliable and ready for training. ‍
• AI-ready operations: High-quality inputs become a repeatable process, not a one-off cleanup project. Teams can scale AI initiatives without losing trust in their data.  ‍
• Governance at enterprise scale: Domo provides the oversight, security, and scalability required for organizations embedding AI into critical workflows. 

When data is reliable, AI stops being experimental and starts driving results you can trust. 

Ready to turn your data into a competitive edge for AI? 

If you’re looking to strengthen your data foundations for AI, these resources are a great place to start building a data quality strategy that drives real results: 

• The Best AI Starts with Clean Data: A Step-by-Step Guide: Learn to spot and fix common data quality issues before they disrupt your AI projects; includes easy, actionable steps and real examples. ‍
• AI Readiness Guide: Assess how ready your organization is for AI and learn what you need to build scalable, trustworthy AI systems using clean and organized data. ‍
• AI Ready Data: AAI Summit Replay: Hear from industry leaders, including Juan Sequeda, on what it takes to prepare data for practical AI use.