Generative AI has transformed how we interact with technology, whether through chatbots, content generation, or process automation.

However, behind every response from ChatGPT, every image created in a specific drawing style, or every line of code suggested by GitHub Copilot, there lies a fundamental element: high-quality data.

If the data used to train these models is incomplete, inconsistent, or inaccurate, the results will be equally flawed. 

Why is data quality crucial for AI?

Generative AI relies entirely on the data it consumes. If this data is inaccurate, outdated, or biased, the models will produce incorrect or even harmful results. 

A study by AWS revealed that 93% of Chief Data Officers consider a data strategy essential for extracting value from Generative AI, but 57% have not yet implemented one.

Impacts of poor data quality

The risks of neglecting data management go far beyond simple operational errors. When we talk about Generative AI and data analysis, inaccurate information creates a domino effect with critical consequences for business:

• Incorrect decision-making (based on inaccurate information).

• Financial losses (IBM estimates that companies lose up to 25% of annual revenue due to data errors).

• Compliance and security failures (violations of regulations such as GDPR).

The invisible foundation of Generative AI

The best AI models — such as GPT-4 or the recommendation systems of Amazon and Netflix — only achieve high performance because they are fed by:

• Curated data: rigorous filtering processes to ensure relevance (e.g., petabytes of clean text in LLM training).

• Representative diversity: avoids bias in critical sectors like healthcare (diagnostics) and finance (credit analysis).

• Continuous updates: outdated data generates obsolete or inaccurate responses.

Without this foundation of data quality, even the most advanced algorithms fail, compromising everything from customer service chatbots to enterprise automation systems.

The 6 pillars of data quality

To ensure your data is truly reliable and suitable for powering high-performance AI systems, it is essential to evaluate it through these six fundamental pillars:

Completeness → Are all required fields filled?

Consistency → Does the data avoid contradicting itself across different systems?

Compliance → Does it follow standards and regulations?

Integrity → Are relationships between data preserved?

Accuracy → Does it reflect reality?

Timeliness → Is it outdated or still valid?

These pillars are not just theoretical concepts — they are practical requirements for anyone wishing to extract real value from their data. A 2023 study by MIT shows that organizations that monitor these 6 dimensions reduce rework costs in machine learning projects by 40%.

The impacts of poor data quality on Generative AI

The link between data quality and AI performance is direct and measurable. Generative models, by their complex nature, exponentially amplify any deficiency present in their training data. See how specific problems manifest:

Biased data creates discriminatory systems

• Real-world case: In 2023, a resume screening system at a multinational company favored male candidates in 78% of technical positions

• Cascading effect: The model replicated historical patterns present in previous hiring data

Outdated information compromises accuracy

• Common scenario: Financial models using pre-pandemic data underestimated market risks in 2022

• Consequence: Erroneous forecasts in credit analysis and investments

Unvalidated sources generate critical hallucinations

• Documented incident: An e-commerce chatbot that recommended prescription drugs without a prescription

• Post-failure analysis: 43% of problematic answers came from unmoderated forums used in training

In 2024, a European bank suffered a €2.3 million fine when its virtual assistant provided incorrect regulatory information to customers — a problem traced back to a lack of governance in the training data.

Best practices for ensuring data quality

Data quality is an ongoing journey. And for companies looking to extract the maximum value from Generative AI, adopting solid data management practices is not optional, it is essential. 

Discover the four fundamental pillars that support a reliable and scalable data strategy.

Cleaning and sanitation

Before feeding any AI model, data must undergo a rigorous preparation process. This includes:

• Intelligent duplicate removal, using algorithms that detect not just exact copies, but also similar records (for example, "José Silva" and "Jose Silva").

• Format standardization, such as dates in the DD/MM/YYYY format and monetary values with the same notation.

• Outlier handling, using statistical methods to eliminate distortions.

• Cross-source validation, ensuring data consistency and reliability.

Data governance

Without solid governance, quality data cannot be sustained. Key components include:

• Well-defined access policies, determining who can view, edit, or approve data.

• Clear documentation that explains the source, meanings, and rules of each dataset.

• Comprehensive metadata, allowing tracking of the entire data lineage.

• Version control, to understand how data has evolved over time.

This structure provides transparency, security, and facilitates collaboration between technical and business teams.

Constant updates

Outdated data can be just as damaging as incorrect data. To prevent old information from harming the AI:

• Establish update frequencies tailored to the type of data (e.g., daily for market data, quarterly for demographic data).

• Implement automated processes to identify and update obsolete records.

• Create feedback mechanisms, allowing users to report inconsistencies.

Continuous monitoring

Quality does not maintain itself. Therefore, it is essential to have:

• Real-time alert systems that detect anomalies and drops in data quality.

• Periodic reports that assess data health and guide improvements.

• Agile correction processes, allowing quick adjustments before issues affect the AI.

Organizations adopting this comprehensive framework report up to a 50% reduction in operational errors, a 40% increase in the efficacy of AI models, and a significant drop in rework and manual adjustments. By turning raw data into reliable assets, these companies build the ideal foundation for any Generative AI initiative with real impact.

Data as a strategic product

Data is no longer just a byproduct of operations but has become a strategic asset — essential for innovation, decision-making, and competitive advantage. 

The most advanced organizations have already adopted this view and structure their initiatives around a model where data is treated as a product with a lifecycle, governance, and measurable value.

This new paradigm requires clear data roadmaps integrated with business goals, guiding everything from collection to final data usage to generate value. Instead of isolated and disconnected initiatives, the focus shifts to creating sustainable data platforms with well-defined goals and indicators.

Many companies are forming multidisciplinary teams dedicated to curating, analyzing, and delivering data as internal products, ready for consumption by marketing, finance, development, and, of course, artificial intelligence teams. These teams are responsible for ensuring the ongoing quality, usability, and accessibility of data.

For all of this to work, it is necessary to promote an organizational culture oriented toward data quality, involving everyone from leadership to operational teams. This means encouraging good practices in day-to-day operations, investing in training, and adopting tools that democratize access to information — always with responsibility, security, and governance.

Treating data as a product is therefore an inevitable move for companies wanting to scale the use of Generative AI with confidence, accuracy, and real impact. Those who invest in this journey now will be at the forefront of the next digital revolution.

AWS Data: solutions for reliable data

To transform data into strategic assets and drive artificial intelligence, it is essential to rely on a robust, scalable, and secure infrastructure. AWS (Amazon Web Services) offers a complete ecosystem of solutions aimed at collecting, storing, processing, and analyzing data, helping companies keep their data always ready to generate real value.

With Amazon Redshift, you can build a scalable, high-performance data warehouse, ideal for complex and near-real-time analytics. Meanwhile, AWS Lake Formation allows the rapid and secure creation of data lakes, centralizing structured and unstructured data into a single governed repository — essential for ensuring controlled and reliable access.

For direct and dynamic queries, Amazon Athena offers a serverless solution that allows using SQL to explore data stored in Amazon S3, eliminating the need for additional infrastructure. Complementing this ecosystem, AWS Data Pipeline facilitates the orchestration of data flows between services, automating extraction, transformation, and loading (ETL) processes securely and efficiently.

When integrated, these tools enable organizations to keep data clean, well-organized, monitored, and accessible. Even more: they create a solid foundation for generative AI applications, reducing operational risks, improving decision-making, and accelerating innovation.

By adopting AWS data services, businesses of all sizes gain flexibility, control, and confidence to advance on their digital transformation journey.

Codebit and your data quality

At Codebit, we understand that excellence in AI begins with reliable data. That is why, in partnership with AWS, we offer customized solutions for:

✔ Data integration (ETL, migrations).

✔ Governance and security (compliance with LGPD/GDPR).

✔ AI model optimization (training with curated data).

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