Talking about text analysis and data analysis, as mentioned above, they are often used interchangeably. This article is an attempt to bring out the differences between the two, and this is done with the help of 5 key parameters of difference. Right from the type of information they handle, to their approach towards analytics, there are quite a few differences.
1] What it’s all About
Data analysis can be defined as a suite of functionalities that can be used to look for patterns and relationships in structured data.
Text analysis, on the other hand, can be defined as a suite of functionalities that are designed to convert unstructured textual data into structured data to facilitate data analysis. So, in a way, it is a precursor to data analysis.
2] Type of Data
The type of data is another point of difference between the two. Data analysis works with structured info that is available in large datasets found in spreadsheets, CRM, ERP, and databases.
Text analysis, on the other hand, works with unstructured textual information that is found in word files, emails, social media, files, presentations, videos, images and more. The formats are varied and so are the sources, which makes it all the more complex.
3] Retrieval of Information
Since structured info is easier to retrieve because of its organized nature, data analysis has it easy to fetch information stored previously. Also, fewer formats makes organization easy.
Unstructured information, on the other hand, comes in multiple formats, and from multiple sources, which makes organization, storage and retrieval of information difficult and time consuming. Also, the information is located across diverse systems, which makes it all the more complicated.
4] Preparation of Information
When it comes to the ease of prepping of information, data analysis has the upper hand as it’s structured, hence formatted. This makes the process of information ingestion for building analysis models easier.
However, when unstructured information comes into the picture, one needs to use linguistic techniques – NLP and AI, metatagging, to make it fit for use.
5] Requirement for Taxonomy
Data Analysis does not require the creation of an over-riding taxonomy. Text analysis, on the other hand, deals with unstructured information in multiple formats, which calls for the need of an over-riding taxonomy to facilitate organization into a common framework.
