Data analysis is a process of testing the information against theoretical constructions. These can be as organized as a hypothesis or as general as a theoretical framework. It is also a process of bringing order, structure, and meaning to the mass of collected data what can be called making meaning.
•There are two sides to analysis, one involves following the standards of a dispelling. In humanities, this involves the application of empathy alongside critical thinking. In science, there is an element of puzzle solving.
•Data visualization is a creative process that makes a meaningful story often for publication out of a mass of results.
•Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. (ref)
Benefits of Data Management click here.
Metadata is a set of data that describes and gives information about other data. It can be as simple as the author, date and title. With data it typically involves description of type of data collection methods, variables. the links below have examples
Or is data [information] that provides information about other data.
There are two reasons, Researchers tell us that they do data management
An Introduction to the Basics of Research Data Management (YouTube video by Louise Patterton)
A great deal of energy has been put into defining the difference between qualitative and qualitative data. Different authors have a definition that varies across fields. We would say that for the purposes of helping you work with your data, it does not matter and can be a distraction.
Data is that which you can use to create a research result from or which underlays the result. It's the information that you, as a researcher in a field of inquiry,can legitimately point to in making arguments and coming to conclusions. It, in other words, the stuff you study. Whatever that stuff is so long as it gets you to a result, its data.
For the purposes of data services, Quantitative data is that data which you can apply statistical tests too. It matters immensely if it is ordinal, or not. It's important to check if its normally distributed. In other words, does it meet the assumption that underlay the particular test? However, even if its numbers, if you can't use stats then it not useful to see it as qualitative.
Qualitative data is that data to which you can apply the processes and theories of qualitative inquiry.This means that if you are doing policy work, a website is a data, if you are doing computer science, the download stats of that website is data.
If you feel our definition is too basic then take a look at the video. This was made by the NEDDICK group of mainly data librarians of which wits is a member, We are scattered around the country. So if you need help and you are not at wits remember we can always refer you to a data librarian near you. For a lot more useful information from NeDICC, Click here