5. Data preparation

Data preparation – from raw material to networked corporate knowledge

Once the corporate knowledge has been fully collected, the step begins that decisively determines how powerful, reliable, and useful the later AI will actually be: data preparation. In this phase, a large number of individual files, texts, media, system extracts, and experience reports are, for the first time, turned into a coherent, structured, and technically robust knowledge system.

The collected information initially exists in very different qualities. Often, there are several versions of the same content, contradictory statements, outdated statuses, informal working methods, inconsistent terms, or missing links between related topics. Much has grown historically, is not maintained consistently, or is tailored to different target groups and situations.

A particularly important part of data preparation is the standardization and structuring of your company’s language. Technical terms, internal designations, product names, abbreviations, process names, and typical formulations are prepared and linked in such a way that the AI understands and correctly uses the “language” of your organization, your employees, and your customers. Only in this way can queries be correctly interpreted, content clearly assigned, and misunderstandings avoided. Without this linguistic clarification, many questions go unanswered or are answered inaccurately, incorrectly, or incompletely.

If an AI were to work directly on the unorganized raw material, it could find passages of text and content – but it could not reliably decide which information is valid, up-to-date, and technically correct. It becomes even more difficult with complex questions involving multiple areas of knowledge, rules, products, or processes. Without preparation, the AI lacks the necessary clarity to reliably recognize connections, argue without contradictions, or provide complete, robust answers.

Data preparation is therefore the step in which a viable knowledge base is created from unorganized information – a knowledge base that is technically consistent, speaks your company’s language, and creates the prerequisite for AI not only to find, but to understand, evaluate, and provide meaningful support.

From document to knowledge

Another often underestimated aspect is the quality of later answers with large amounts of data. The more information is simply placed in a database and the more data an AI has to consider at the same time, the greater the fuzziness of the hits and outputs. This is a well-known problem of many systems: As the amount of data increases, precision decreases, answers become more general, less accurate, or mix content that has nothing to do with each other technically.

The reason for this is simple: In classic systems, thousands of pages, PDFs, protocols, or manuals are placed side by side. For the AI, these are equivalent text sources. Relevance, validity, responsibility, product reference, or technical context are not clearly represented in them.

Data preparation fundamentally solves this problem by treating information not as documents, but as knowledge. Content is broken down into its technical components: facts, rules, terms, questions, answers, relationships, dependencies, validities, variants, and links. The individual document loses its role as a knowledge container – it is “dissolved.” What remains is the knowledge it contains in a structured, abstract form.

This knowledge is clearly assigned to products, services, processes, functions, categories, or application scenarios. As a result, the total amount of data no longer matters. The AI does not access a large, fuzzy mass of text, but exactly the knowledge modules that are relevant to the respective query.

If desired, the original documents remain available. You can still look up where something is written, specifically find documents, or search for passages of text. However, the crucial point is: The AI does not work with documents – it works with knowledge.

Linking instead of storing

In data preparation, content is not only cleaned up and standardized, but above all linked together. Different sources of knowledge – documents, processes, products, rules, experience values, and system data – are related to each other. This creates a knowledge network in which the AI not only knows individual facts, but also their meaning, validity, and context.

Among other things, it is determined:

  • which documents belong to which processes
  • which products, article numbers, variants, and rules belong together
  • which functions are intended for what – and when they are not useful
  • which exceptions, alternatives, or dependencies exist
  • which information should be considered together in which situations

Only then can the AI later not only find information, but also classify, combine, and evaluate it correctly.

Technical logic instead of chance

In data preparation, it is also defined which links should be applied automatically in which situations. This creates a technical logic according to which the AI works.

Examples:

  • For a price inquiry, the system automatically recognizes the product, the appropriate article number, associated variants, valid discount groups, and relevant conditions.
  • For a query about a function, not only is it explained what it does, but also whether it makes sense in this specific situation, what restrictions apply, or which alternatives would be better suited.
  • For service inquiries, device, error code, known causes, suitable spare parts, and proven solution steps are automatically linked together.
  • For process questions, responsibilities, forms, guidelines, and dependencies are considered simultaneously.

Such answers are only possible if knowledge has been structured, networked, and technically modeled – not if it is merely stored as files.

What you get out of it

Data preparation creates a knowledge base that:

  • is consistent and free of contradictions
  • remains securely and reproducibly searchable
  • can correctly assign content
  • recognizes relationships
  • can also handle questions that are not explicitly documented
  • and delivers technically robust, comprehensible results

This turns a collection of information into a strategic treasure trove of knowledge – the foundation for an AI that truly understands, supports, and works reliably.

In short

Data preparation transforms isolated content into an intelligent knowledge network.
It is the step that enables an AI to work precisely, contextually, and meaningfully with large amounts of data in the first place.

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