Companies are investing heavily in artificial intelligence, as they consider it a concrete tool for competitiveness. Use cases are multiplying, benefits are perceived in terms of efficiency and productivity, and adoption is accelerating.
However, to achieve concrete results, it is not enough to invest: it is essential that time, resources, and skills are properly distributed across all phases of implementation. Data preparation, for example, is one of the most relevant steps, yet it is not always addressed with an adequate level of attention.
In this article, we will look at what data preparation is, its role in a GenAI-based project and, above all, how to approach it to build reliable and secure solutions.
Key Points
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In the AI implementation journey, it is essential to dedicate time and resources to data preparation, a decisive step for the final outcome.
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Data preparation is the process that transforms raw and fragmented data into a coherent, reliable, and usable information base for GenAI systems.
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Data preparation is not a one-off activity but a process structured into 6 key phases, from data exploration to Data Wrangling.
Data preparation, a key step in the GenAI era
The race toward GenAI is affecting all sectors: from customer service to internal functions, companies are bringing increasingly concrete and value-oriented use cases into production.
From the very first experiments, it became clear that general-purpose models, as powerful as they are, were not sufficient to meet real business needs. Organizations are not looking for generic outputs, but for systems capable of operating on specific processes, using proprietary data, and delivering reliable outputs.
To meet this need, vertical models trained on proprietary datasets and techniques such as Retrieval-Augmented Generation (RAG) have emerged, enabling the integration of updated and contextualized knowledge bases into models.
This evolution has highlighted a key point, which has always been part of data science: the quality of the output directly depends on the quality of the data on which the system is based. A large part of the value of an AI project lies in the preparation of this data, and this is precisely where companies should focus their attention.
When are data AI-ready? The profile in 4 points
A company that wants to implement Generative AI starts from a use case, not from data. It must then understand what data it needs and what characteristics that data must have to effectively feed the model.
We can therefore define AI-ready data as data that meets some fundamental conditions:
- It is relevant to the use case;
- It is of high quality, in terms of accuracy, completeness, timeliness, and consistency;
- It is accessible, meaning it can be easily retrieved, combined, and used;
- It is governed and compliant, meaning it respects security, privacy, and compliance policies, and does not contain harmful biases that could influence the AI model’s responses.
Only when these conditions are fully met can data be considered AI-ready. Otherwise, the broader topic of data preparation comes into play.
Data preparation for GenAI: the keystone to obtain reliable outputs
If the goal is to build systems that are useful, integrated into processes, and capable of delivering reliable outputs, data preparation is an enabling factor.
What is data preparation
Data preparation is the set of activities required to transform raw, heterogeneous, and often unstructured data into a coherent information base that can be used by AI systems. It is far from a trivial step, considering that companies have enormous volumes of structured and unstructured data, distributed across different sources and difficult to manage in a unified way.
Data preparation means, for example, connecting different sources, eliminating ambiguities and errors, and structuring knowledge so that it can be easily acquired by systems and used in real time. Without this process, even the most advanced architectures are unable to express their full potential.
What are the 5 challenges of data preparation
Data preparation is not a simple or quick process. On the contrary, it is an activity that requires dedicated skills, time, and a structured approach to address recurring challenges.
- Dati fragmented in silos
Information is distributed across systems such as ERP, CRM, and cloud platforms, making it difficult to build a unified data base. - Inadequate data quality
Incomplete, duplicated, or outdated data is very common and directly impacts the reliability of model outputs. - Complex and time-intensive processes
Data preparation is a long and repetitive activity, with manual areas that slow down the entire AI solution development cycle. - High technical complexity
Integrating different sources, managing structured and unstructured data, and ensuring consistency and traceability require technical expertise and appropriate tools. - Rapid data obsolescence
A significant portion of business information quickly loses value, making continuous updates necessary to maintain output quality.
How to prepare data for AI: the 6 key phases
Preparing data for GenAI is a process that develops over multiple phases and evolves over time. The activities are not strictly sequential: they often overlap, repeat, and are refined as use cases change and business needs evolve.
Exploring and understanding data
The first step is to understand what exists within company data, an activity that is far from trivial considering that information is abundant, distributed across different systems, and characterized by highly heterogeneous formats. In this phase, sources are analyzed, anomalies are identified, and relationships and gaps begin to be evaluated.
The support of automated tools is essential to speed up these activities, but it is not sufficient: human contribution remains indispensable, meaning the ability to interpret data, understand its meaning, and connect it to the solution’s objective.
Improving data quality
Data must become reliable as quickly as possible, which is why data quality activities immediately follow the exploration phase. Here, duplicates, errors, missing values, and irrelevant information are addressed, improving data consistency and completeness.
This also applies to unstructured data, which must be made usable by AI systems: documents, emails, and reports can be normalized, divided into coherent units, enriched with metadata, and transformed into formats that can be easily processed by models.
Integrating and enriching sources
A key phase of data preparation consists of connecting information from different sources, with the aim of building a unified and coherent base from which AI can draw to enhance its response capabilities.
Data profiling
In this phase, data quality and consistency are verified, assessing whether the data is truly suitable for the use case. Structure, content, and relationships are analyzed to identify critical issues before they impact the system.
Extracting, transforming, and making data available (ETL)
At this stage, data is collected from different sources, transformed into a consistent format, and made available in a centralized environment. This step enables uniform access to information and allows models to operate on integrated and up-to-date data bases.
Adapting data to the model and the use case
Finally, data is further adapted according to the model and usage methods in a phase known as Data Wrangling. Here, data is reorganized, enriched, and optimized to make it truly effective within the specific AI system.
From strategy to value: our role in AI projects
At Kirey, we support companies in their artificial intelligence adoption journeys, following technological evolution and working to translate it into concrete business value.
Data preparation is just one of the steps in this journey, but it is also one of the most critical, because a large part of a project’s success is determined here. At Kirey, we take ownership of this phase by providing our clients with specialized expertise, advanced tools, and field experience. The goal is one: to build reliable, secure, and value-oriented AI-based applications.
Contact us to find out how we can embark together on a concrete AI adoption journey.
