Digital Themes

Structured Data

What is Structured Data?

Structured Data refers to quantitative data, such as names, credit card numbers, and addresses, that is stored in anchored fields within a relational database (RDBMS). Structured data conform to pre-defined data models, and therefore can easily be sourced by humans or algorithms within that RDBMS structure. 

Structured data exhibits in a longitudinal format, adhering to a tabular configuration with links between the adjacent rows and columns (e.g. SQL databases or Excel files). Structured data relies on data models of how to store, process, and access data. These models allow for data fields to be retrieved separately or collectively with data from other fields. Thus, structured data can be amassed from several different locations within the database.

This differs from unstructured data and semistructured data. Unstructured data does not fit into predetermined models and remains in its native format until it is analyzed. Semistructured data is data organized with tagging structure but that is still unable to fit into a formal relational database.

Structured data also makes web pages easier for search engines to find and understand by utilizing vocabulary (i.e. translating page content into code) that is easily comprehended by a search engine. Search engines "read" that code and use it to feature richer search results. There are many varieties of structured data, but they're all composed of code. This code determines the level of rich results or rich snippets. Websites use structured data to “talk to" search engines, "telling" them information such as cooking time, calorie counts, movie times, book reviews, blog posts, and more.

Structured data alters how a snippet (i.e. search results) displays on the search results page, showing more and specific information to current and potential customers, thus increasing the probability that viewers will click on those results. Structured data is increasingly powering rich results, as increased clicks lead to higher rankings.

What are the major differences between Structured Data and Unstructured Data?

  • Structured data adheres to a specific structure model (i.e. rows and columns), and unstructured data is generally saved in its native form. Structured data is arranged in predefined data fields, making it organized and easy to access within RDBMS, while unstructured data does not conform to any predefined models.

  • Structured data is quantitative, typically including numbers or items that can be calculated. Analyses of structured data include estimating probability (classification), anticipating relationships within variables (regression), and data clustering based on attributes. Unstructured data is qualitative and requires the use of advanced analytics such as data mining. Unstructured data can include social media interactions, customer surveys, or open-ended customer interviews.

  • Structured data is housed in data warehouses through an ETL pipeline. Alternatively, unstructured data is stored in its original format within vast data lakes. While both can be stored in the cloud, structured data takes up much less space.

  • Due to its organized models, searching through structured data requires little effort from humans or algorithms. Unstructured data, without predefined models, requires advanced processing to facilitate any data mining.

  • The typical format for structured data is numbers and text, utilizing predefined data models. Unstructured data comes in diverse shapes, sizes, and formats, such as audio files, videos, images, sensor data, and emails.
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Data Ingestion

Data ingestion enables to extract value from their data with tools that can process various data formats and large volumes of unstructured data to identify issues and opportunities and make informed decisions to deliver accurate data on time, every time.

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