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Glossary

AI Data Analyst

What is AI Data Analyst?

An AI data analyst is an AI system or assistant that analyses data such as spreadsheets, exports or key figures and explains the results in plain language. It calculates totals and changes, identifies trends and anomalies and suggests next steps. This gives business teams without statistical expertise faster answers. Language models can make calculation errors, however, so figures should be double-checked. Example: monthly sales are pasted in as CSV and commented on.

How KI.ordo helps

KI.ordo's spreadsheet analysis template derives key figures, trends and anomalies from CSV text and reminds users to double-check the AI's calculations.

Learn more →: AI Data Analyst

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  • Data analysis
  • Key figures
  • Assistant

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Related terms

Exploratory data analysis (EDA)

Exploratory data analysis (EDA) is the first, open-ended look at a dataset: you calculate summary figures, examine distributions and charts and look for patterns, outliers and missing values – without a fixed hypothesis yet. It helps assess data quality and find sensible questions for further analysis or modelling. Example: before a sales forecast, EDA reveals that one month was recorded incompletely because of a system migration.

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Anomaly detection

Anomaly detection finds data points, events or patterns that differ markedly from the norm. These might be unusually large transactions, sudden jumps in sensor readings, duplicate records or suspicious network activity. It is used in finance, IT security, manufacturing and quality control. Methods range from simple statistical thresholds to learning models; hits are indications that a person should assess.

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Predictive analytics

Predictive analytics uses historical data, statistics and machine learning to estimate likely future events. It answers questions such as: which customers are about to cancel? How much stock will be needed next month? When is a machine likely to fail? The results are probabilities, not certainties. Their value depends heavily on data quality and on whether the predictions actually feed into decisions and processes.

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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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