Data preparation for AI: the foundation almost no one looks at

For more than a decade, we have heard that data is one of a company's most valuable assets. However, many organizations grew by accumulating information in silos, disconnected systems, incomplete metadata, and governance models that were never fully consolidated.

For years, that debt could go unnoticed. With the arrival of artificial intelligence, it stopped doing so. Because AI can be as powerful as the data that feeds it. And today, everything that was left pending is starting to take its toll.

That is why the data preparation for AI it stopped being a preliminary technical task and became the condition for a pilot to reach production.

In fact, executives themselves admit it: in a study of companies with revenues exceeding one billion dollars, the 71% acknowledges that their organization is not ready to scale up AI, due to fragmented data, a lack of clear accountability, silos between departments, and legacy systems that do not integrate. 

Gartner estimates that 85% of AI projects fail before reaching production.

The most frequent cause is not the model or the provider. It is the data.

The data preparation for AI, also known as AI data preparation, became a critical factor for any organization looking to scale artificial intelligence. 

Without reliable, governed, and use-case-ready data, even the most advanced models end up generating inconsistent results.

What is data preparation for AI

The data preparation for AI It is the process of collecting, organizing, cleaning, transforming, enriching, and validating information before using it in artificial intelligence models. Its objective is to convert raw data into reliable information that can train models, generate predictions, or automate decisions.

Gartner raises this in its analysis on AI-ready dataPreparation is not limited to improving information quality. It also implies ensuring that data correctly represents the use case, can be easily discovered, maintains context through metadata, and is consumable by machine learning and generative AI models.

AI-ready data must:

• correctly represent the use case;
• include the errors and exceptions that occur during the operation;
• have traceability of its origin;
• be documented using metadata;
• maintain consistent quality over time.

Many organizations believe that having large volumes of information is enough. 

A company can store terabytes and still not be prepared to implement artificial intelligence: data for AI requires a different approach than traditional data management.

Everyone is talking about AI. Few have it running

Artificial intelligence is no longer an experimental trend. Today, it is part of the innovation strategy of thousands of organizations. However, there is a significant difference between testing AI and getting it to generate sustainable results for the business.

The numbers show that gap.

Figure	What does it measure	Source
46%	AI pilots discarded before production in 2025	Multiple industry surveys
95%	Generative AI pilots that fail when trying to scale	MIT, 2025
39%	Companies reporting financial impact	McKinsey, 2025

While 64% of companies claim to use AI to innovate, only 39% say they have achieved concrete financial results, according to McKinsey. This is the gap between adoption and results: widespread use, but meager returns. And it is structural, not cyclical.

Signs we see in organizations:

• siloed data that no one ever fully understands;
• dashboards that give different numbers for the same thing;
• AI projects that stall within a few weeks of launching;
• teams that don't know what information to use or who is responsible for its quality.

They manifest as technological problems, but the obstacle is internal chaos: data 

fragmented, lack of clear owners, silos between departments, and legacy systems that do not integrate.

Before investing in new models, platforms, or infrastructure, it is worth answering a much more basic question:

The hidden cost of degraded data

The problems of data quality for AI they do not stay on the technical plane. There is a number that usually surprises tech leaders: on average, the 26% for business data is degraded, meaning outdated, duplicated, incomplete, or poorly formatted. And poor quality data can add a 25% Overtime to any data or AI project.

Garbage in, garbage out. No matter how powerful your AI model is: if the input data is bad, the output will be too. This is the same conclusion reached by the datos.gob.es analysis on open data and artificial intelligence: Data quality defines the model's outcome.

According to Gartner, poor quality data costs on average USD 12.9 million annually per organization. Experian, for its part, estimates that fragmented data generates a loss of between 15% and 25% in revenue.

The impact is usually reflected in less accurate models, decisions made on incorrect information, more hours spent manually preparing data, and projects that never manage to scale to production.

That is why data preparation underpins the entire artificial intelligence lifecycle, rather than being a pre-project task. If an organization seeks to move toward more reliable models, the first step is not to add another AI tool. It is build an AI-ready data architecture that guarantees quality, traceability, and governance from the origin.

Data preparation vs. data preprocessing for AI: They are not the same

Both concepts are often used as synonyms, but they describe different stages within a data strategy.

The data preparation for AI It is a comprehensive process that encompasses the identification, integration, quality, governance, and availability of the information that the models will use. 

The data preprocessing for AI it corresponds to a technical stage within that process: adapting the data so they can be interpreted by machine learning or artificial intelligence algorithms.

Data preparation for AI	Data preprocessing for AI
Define what data the use case needs	Adapt the data for the model
Integrate different sources	Normalize and transform variables
It includes governance and metadata	Apply statistical techniques
Evaluate quality and traceability	Prepare training data

In simple terms, preparation answers whether we have the correct data to solve the problem, and preprocessing, whether that data is ready for a model to use. Without a solid preparation strategy, any preprocessing technique ends up working on a weak foundation.

The stages of data preparation for AI

Data preparation begins long before training a model. It involves understanding what information exists, what its status is, how it relates, and how reliable it is for the use case you want to solve.

Data Profiling

The data profiling It consists of analyzing the available sources to understand the quality and structure of the information. It allows detecting problems before they reach the model: for example, that two systems register the same customer with different formats, or that certain critical fields have a high percentage of null values.

During this stage, missing data, duplicate records, inconsistencies, incompatible formats, and anomalies are usually identified. The question it answers is simple: How reliable is our data?

Data cleaning for AI

The data cleaning for AI fixes the issues affecting model performance: deduplication, error correction, missing value treatment, anomaly detection, and format standardization. Its importance lies in reducing errors before the information is used for training or inference.

Cleaning does not mean erasing everything that seems different. An AI-ready dataset has to include the errors and exceptions that occur in operation, because that is where the business patterns the model needs to learn are located.

Transformation and normalization

Once the data reaches an adequate level of quality, it must be transformed so that it can be used by different platforms and models. This stage seeks consistency among heterogeneous sources and includes format unification, system integration, value normalization, structure adaptation, and information enrichment.

The goal is to build consistent, interpretable, and ready-to-feed data into a AI data pipeline.

Feature Engineering

The feature engineering It consists of creating, selecting, or transforming variables to improve the predictive capacity of a model. It is one of the stages that generates the most impact on the performance of artificial intelligence.

For example, instead of just using the date of a purchase, it usually pays off more to build variables such as days since the last purchase, purchase frequency, average ticket size, or behavioral seasonality. Those new variables allow the model to identify patterns that would not be evident with the original data, and they usually make the difference between an acceptable model and one capable of generating business value.

Data validation

The data validation Verify that all prepared information complies with the defined quality rules before being used by the models. Its purpose is to prevent operational errors or unexpected changes from degrading the AI's performance once it goes into production.

During this stage, integrity, consistency, completeness, uniqueness, and compliance with business rules can be validated. Automating this validation within the pipelines reduces the risk of training models with incorrect information.

Data preprocessing techniques for AI

Once the data was identified, cleaned, and prepared, a key stage begins: adapting them so they can be interpreted by artificial intelligence models. Preprocessing transforms complex information into structures usable by algorithms.

Data normalization and standardization

Models work better when variables have comparable scales. Normalization and standardization reduce biases caused by differences in magnitude between variables, something that is especially weighty when different sources must be combined to generate predictions.

Categorical variable encoding

Many business data are represented by categories: customers, products, regions, segments, or sales channels. Algorithms cannot interpret these categories directly, so they must be converted into numerical representations using appropriate encoding techniques.

Dimensionality reduction

As the number of variables increases, so does the complexity of the model. Dimensionality reduction helps identify which information adds value and which introduces noise. More variables do not improve a model on their own: what matters is identifying those that provide relevant information.

Why the data is not ready for AI

Many organizations store huge volumes of information. That does not mean they have AI-ready data. Frequently, ecosystems grew through new applications, databases, and systems that were never designed to work in an integrated manner.

As a consequence, recurring problems appear in the same form across different organizations:

According to Gartner, 63% of organizations do not have—or are not sure they have—adequate data management practices for artificial intelligence. And the necessary data is available at the right time and place in only 30% of cases.

This explains why so many projects work well in pilot and then get stuck there, unable to scale. AI data preparation requires a different vision from traditional data management: data must be easily discoverable, understood, protected, and consumed by machine learning and generative AI models.

At this point, it is advisable apply generative AI to your own data with Amazon Bedrock, so that models only access governed and contextualized information.

The paradox is structural, not cyclical

One statistic that sums up the state of the industry: only 5% of large companies have end-to-end AI integrated into their workflows. The remaining 95% are still stuck in pilot or proof-of-concept mode, unable to scale.

The reason is not the technology. A study of companies with over one billion dollars in revenue confirmed that the biggest obstacle to implementing AI is internal chaos: fragmented data, a lack of clear owners, silos between departments, and legacy systems that do not integrate.

This situation was predictable and will not be resolved quickly. Organizations that continue to bet on more technology without first solving the data layer are repeating the same mistake: the problem is not in the model, it is in the data that feeds it.

The trap of betting only on infrastructure

The race for artificial intelligence has led many organizations to concentrate a good portion of their investments in infrastructure, computing capacity, and new tools. But having more technology does not guarantee, by itself, better results.

Investing in computing power does not solve data-related problems. According to multiple industry surveys, approximately 46% of AI pilot projects were scrapped before reaching production in 2025, largely because the organization failed to prepare its data.

Infrastructure enables AI. Data determines how much value we can extract from it.

An organization can access the best available model and still get inconsistent responses if the information used has quality, governance, or traceability issues. That is why the question is no longer how much computing capacity we have, but how prepared our data is to leverage it.

The advantage lies in proprietary data

Models, platforms, and technologies will continue to evolve. An organization's data, its knowledge, its customers, its operations, and its history are what enable it to build relevant use cases and differentiate itself.

A base that gains value

When data is organized, contextualized, and governed, it can be reused across different projects and use cases. Each improvement in quality, traceability, and access expands the possibilities of deriving value from AI.

Invest before scaling

Before adding models, tools, or greater computing capacity, you must ensure that the foundation is ready. A solid strategy of Data governance for AI allows reducing rework, accelerating implementations, and scaling projects with greater predictability and control.

AI technology is no longer the barrier. The barrier is data maturity and internal governance.

Modern architectures for AI-ready data

As organizations seek to scale artificial intelligence projects, traditional architectures are beginning to show their limits. Today's environments need to integrate information from multiple sources, ensure quality, maintain data governance, and facilitate its consumption by different teams and models.

Data Mesh

The model Data Mesh it proposes decentralizing data ownership. Instead of relying on a single centralized team, each business domain manages its own data as a product. This brings greater responsibility for quality, a better understanding of the business context, shorter access times for AI teams, and organizational scalability.

Data Fabric

The Data Fabric connects different sources through an intelligent integration and governance layer. It enables working in a unified way with information from cloud environments, on-premise systems, enterprise applications, IoT devices, and APIs, regardless of where it is stored.

Lakehouse

The approach Lakehouse combines the advantages of Data Lakes and Data Warehouses. 

It allows the construction of a platform ready for advanced analytics, machine learning, generative AI, and high-volume data processing. For many organizations, it represents a natural evolution toward more flexible architectures.

If the goal is build an AI-ready data architecture, these approaches offer a much more prepared foundation for scaling without depending on a single technology.

Tools to prepare data for AI

Data preparation combines processes, architecture, and automation. There is no single tool capable of solving all challenges, so organizations typically build ecosystems where each component fulfills a specific function. Among the most widely used technologies are:

• AWS Glue, to integrate, transform, and catalog data using managed ETL processes.
• Azure Data Factory, to orchestrate integration flows between multiple sources.
• Data pipelines, which automate the extraction, transformation, and loading of information.
• Metadata catalogs that facilitate discovery and traceability.
• Automated quality controls and validation.
• Cloud services for scalable processing and storage.

AWS recommends following the data preparation best practices of AWS Well-Architected Framework, with quality controls, automation, and governance from the architecture design stage. In projects where infrastructure evolves continuously, it is also advisable automate cloud infrastructure with Terraform, to reduce manual errors and facilitate environment repeatability.

Rather than incorporating new tools, the challenge lies in integrating them within a strategy that guarantees quality, available, and properly governed data.

Challenges and best practices for preparing data for AI

Most artificial intelligence projects do not fail due to technological limitations. They fail because they underestimate the complexity of working with fragmented, inconsistent, and ungoverned data. Certain practices reduce that risk:

• define clear owners for each data domain;
• automate validation and monitoring processes;
• keep catalogs and metadata up to date;
• design reusable pipelines;
• incorporate quality controls at the source;
• continuously review the evolution of the data used by the models.

Data preparation ceases to be an isolated stage to become a permanent capability within the organization.

From pilot to production: The 5-step roadmap

The point where most projects stall is the jump from pilot to production. These five steps organize that transition.

Start with the use case, not the data

Define first what decision the AI is going to make and only then identify what data it needs to make it well. Reversing the order is the fastest way to waste budget without seeing results.

2. A single policy, not three areas talking to each other

Legal, security, and ethics must be resolved within the same governance framework, not in parallel conversations. That framework defines who accesses the data, how they are protected, how their quality is measured, and who is accountable for each source. Without that, data ceases to be an asset and becomes a risk.

3. Metadata that works, not just describes

Metadata is no longer a passive catalog. It has to be able to tell you, automatically, whether or not data is useful for a given model, before that model fails in production.

4. Pipelines designed for two different worlds

Training a model and putting it into production are distinct processes with different demands. Designing pipelines with only one of the two in mind is the reason why so many projects are rebuilt from scratch when scaling.

5. Constant monitoring, not a single snapshot

Data quality doesn't maintain itself: it degrades. New sources, operational changes, and new business rules modify available information without warning. Continuous monitoring is what differentiates a project that is still standing after a year from one that collapses at 13 months.

According to Kiteworks, organizations that implement comprehensive governance frameworks see returns of 3 to 5 times in the first year, thanks to reduced fines, lower remediation costs, and greater operational efficiency.

How Nublit helps you prepare your data for AI

Implementing artificial intelligence requires much more than choosing a model. It requires a data strategy capable of sustaining that technology in the long term.

At Nublit, we start with the same thing on every project: getting the house in order. Before thinking about scaling, we help lay the foundations of quality and governance for each organization's own data. We support initiatives for:

• data strategy for AI;
• modern data architecture;
• Data and AI consulting;
• data governance;
• pipeline design;
• cloud modernization on AWS and Azure.

From architectures like Data Mesh, Data Fabric, and Lakehouse to cloud modernization projects, we work with organizations throughout the region by Data governance services LATAM adapted to each business. If your organization needs a cloud consultancy aligned with AWS and Azure best practices, we can define together a roadmap that turns data into a strategic asset.

Frequently asked questions about data preparation for AI

What is data preparation for AI?

AI data preparation is the process of collecting, cleaning, transforming, organizing, and validating information so that it can be used correctly in artificial intelligence models. It includes data quality, governance, integration, metadata, and preprocessing.

What is AI-ready data?

AI-ready data correctly represent the use case that the model will solve. In addition to quality, they incorporate context, traceability, governance, and the ability to be consumed by machine learning algorithms and generative AI.

How much time is spent preparing data in an AI project?

Various studies estimate that between 60% and 80% of the total effort of an artificial intelligence project is dedicated to preparing, cleaning, transforming, and validating data.

Is data cleaning the same as preprocessing?

No.

Cleaning corrects errors such as duplicates, inconsistencies, or missing values.

Preprocessing is a broader process that also includes integration, transformation, normalization, encoding, and feature engineering to adapt the data to the model.

Why is it important to prepare data for AI?

Because data quality directly determines the quality of the results obtained by the models.

What tools are used to prepare data for AI

Organizations typically use ETL/ELT tools, data pipelines, cloud services such as AWS Glue or Azure Data Factory, metadata catalogs, quality control mechanisms, and automation platforms to build ecosystems ready for artificial intelligence.

What types of data can be used in AI?

Models can work with structured (tables), semi-structured (JSON, logs), and unstructured (text, images, audio, and video) data, as well as multimodal combinations depending on the use case.