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Glossary

Data validation

What is Data validation?

Data validation checks whether data is complete, correctly formatted and plausible before it is processed further. Typical checks cover mandatory fields, the format of email addresses, postcodes or dates, and logical contradictions – such as a delivery date before the order date. This catches errors early instead of letting them carry over into reports, invoices or AI analyses.

How KI.ordo helps

KI.ordo offers a template that checks records for completeness, format and plausibility and outputs a list of errors per record.

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Topics

  • data quality
  • checks
  • plausibility

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Data cleansing

Data cleansing is the detection and correction of incorrect, duplicate or inconsistent entries in data sets. Typical steps include standardising spellings and date formats, removing superfluous spaces and merging duplicates such as 'Smith Ltd' and 'Smith Limited'. Clean data is a prerequisite for reliable analyses, mailings and AI applications.

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Garbage in, garbage out (GIGO)

‘Garbage in, garbage out’ describes the principle that a system can only produce results as good as the data it receives. This applies doubly to AI: incorrect, outdated or one-sided training or knowledge data leads to wrong or biased answers. A chatbot that draws on an outdated price list will quote outdated prices. Looking after data is therefore an ongoing task in every AI project.

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Structured data

Structured data follows a fixed schema with clearly named fields, such as tables, databases or JSON objects containing details like name, price and date. Machines can read, filter and process it reliably – unlike free text or images. On websites, schema.org markup describes products, events or FAQs, for example, so that search engines and AI systems understand the content unambiguously. AI can also extract structured data from unstructured text.

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Data governance

Data governance comprises the rules, roles and processes by which an organisation determines who may collect, use, change and delete which data. It covers responsibility for data quality, access rights, retention periods and compliance with data protection requirements. It matters especially for AI projects, because models and agents are only as reliable as the data they draw on.

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