Intelligent Data Processing (IDP)

Imagine a phone that has collected ten thousand photos and several hundred short videos over the years. Each file has a timestamp, but none of them say what is in the picture. If you wanted the photo of a dog on a beach, the phone has no written label to match your words against. Now multiply that situation by every company, every customer record, every system log, and every signed contract. The amount of data that people and organizations produce keeps rising, and most of it does not arrive in neat rows and columns.

Photos, videos, logs, and PDF documents are examples of unstructured or semistructured data. Their contents are real and useful, but the information is not labeled in a form that a search tool can directly query. A search tool works best when it can compare a query against clearly labeled fields. A photo has pixels, a video has frames, and a log has lines of events, but none of these arrive with a field that says what the content means.

The source frames the problem as one of organization. The contents of these files need organizing before a search tool can use them effectively. Intelligent Data Processing, abbreviated IDP, is the term the source uses for the industry built around AI-driven organization and extraction of this kind of material.

The three categories differ in how much organization their contents already carry. The source places photos, videos, logs, and PDFs in the unstructured or semistructured group, and it describes structured data as the kind that arrives in neat rows and columns. The table below summarizes how these categories differ. The examples for structured data are a general reading of the phrase neat rows and columns, not a list the source gives.

Follow one photo through the process. The file enters with no fixed structure. An AI step then produces something a search tool can use, either a written description of the image or, for other sources, extracted fields. The result is stored, so that a later search can reach it. The diagram traces that path from raw input to a search result.

The source does not spell out the internal method each service uses. The key idea to hold onto is the input and output of the AI step, not the mechanism behind it. Content with no fixed structure goes in, a description or extracted fields come out, and that output is what makes the file findable by text.

The source describes two related jobs that AI can perform for organizing visual data. Together they turn visual content, which is hard to search directly, into something that behaves like a searchable index. The diagram shows how a text query, a generated description, and the original image relate during a search.

The source groups extraction tasks by difficulty. Simple use cases include automatically extracting information from credit cards, driver's licenses, receipts, tickets, and contact information found in email footers. These documents tend to have recognizable patterns, which makes the extraction task comparatively contained. More complex use cases include extracting data from contracts, reports, and charts. These sources carry more varied layouts and richer meaning, so the extraction task is harder. The table contrasts the two groups.

Organizing data and analyzing it are closely connected. The source notes that AI is very good with data analysis and that it can write programs that generate data visualizations, identify outliers, and make predictions such as revenue forecasts. Each of these tasks takes data that has already been gathered and asks a question of it. The source names these tasks as examples and does not describe the specific methods used, so this article keeps to the tasks themselves.