There are various types of data that can be considered beneficial to different purposes: there are, of course, sometimes big differences between sensor data, behavioral data and personal data. The collection of such information is therefore not only based on different practices, but its anticipated purpose is often completely different.
What is data?
The word date is Latin and literally means “given.” Data is therefore circumstances or their recorded representation. Data differs significantly from facts in that they are (relative) lack of context, i.e. because they are simply trying to refer to external facts as value-free as possible.
The 1st differentiation of data types: qualitative data vs. quantitative data
How and by whom which data is collected is first of all reflected in the basic distinction between quantitative data on the one hand and qualitative data on the other. This differentiation refers to the method of collection: quantitative data generally consist of countable, numeric values, whereas qualitative data from non-numeric, descriptive information exist.
Qualitative data, for example, has less significance with regard to general trends and is generally perceived as less objective than its quantitative counterpart. However, they offer more space for detailed contextualizations and are able to present a deeper understanding of specific situations.
When it comes to collecting such data, it remains to be seen that there is already a differentiation as to which types of data are ultimately collected: Interviews are more likely to result in qualitative data, standardized questionnaires or machine measurements lead to quantitative output.
The 2nd differentiation of data types: structural-technical differences of data
Another distinction between data lies in the immanent structure of the information collected. In this regard, there are three general categories, which primarily contribute to the corresponding storage. The specific types of data are as follows:
Data that is available in a clearly defined and organized form is referred to as “structured”. Such information is already well formatted and follows a consistent scheme or structure that makes it easy to store, retrieve, and analyze.
In contrast to the first category, there is unstructured data that does not have a fixed structure or hierarchy. This data is often not organized in an easily analyzable or easily searchable way; it is, in a sense, “raw”, i.e. it requires detailed processing in order to be able to be used.
Semi-structured data lies in the spectrum between structured and unstructured data. In contrast to fully structured data, which is already available in a completely tabular form, and unstructured data, which does not exist in coherent form, semi-structured data has a certain (prototypical) structure, but this does not necessarily follow a fixed scheme. This type of data is already easier to search and analyze than unstructured data, but it still offers more flexibility to handle than fully structured data.
Conclusion on data types
Data is not just data! It always depends on which process the respective information originates or how it is collected and what purpose it is ultimately intended to be used. In the end, a decision must be made between the structure, which contributes to the facts of storage, and the general structure of the data, which depends primarily on the knowledge that you expect from its respective use. All in all, this is therefore a strategic question that every organization or company must answer with a view to its own objectives: What types of data do you want and must deal with?
