Extract from Jeffrey Wolff’s article “How Organizations Can Handle Complex Data More Efficiently: Key Best Practices To Follow”
What is complex data?
We’re referring to complex data as the data that’s generated by modern cloud-based applications or software-as-a-service (SaaS) programs. Examples of complex data include word processing documents, maps, images, video, audio files, graphs, and databases.
There are two main types of complex data: structured and unstructured.
Structured data is typically quantitative data that has clearly defined internal parameters and relationships. Structured data generally appears in columns and rows of a relational database. It may include dates, names, addresses, credit card numbers, and other data types that fit within defined fields. This type of data is easy to use and access but is generally inflexible.
In contrast, unstructured data is usually qualitative data that users cannot readily process or analyze using conventional tools or approaches.
Unstructured data includes:
- freeform text such as documents and text or chat messages;
- audio and visual content including pictures, video, and audio recordings;
- online content such as social media posts;
- sensor data from internet of things (IoT) devices; and more.
This type of data requires more expertise and specialized tools to manage.
