Since the invention of computers, people have used the term data to refer to information on the computer, and this information was transmitted or stored. But that's not its only definition; there are also other types. So how can it be defined? They can be texts or numbers written on paper, or they can be bytes and bits within the memory of electronic devices, or they can be facts that are stored within a person's mind. Now, if we talk about data mainly in the field of science, then the answer will be that it is different types of information that are generally formatted in a particular way. All software falls into two main categories, and those are programs and data. Programs are the collection of instructions used to manipulate it.
Types and uses
The growth in the field of technology, specifically in smartphones, has led to text, video and audio being included in the data plus web activity and logging logs. Most of them are unstructured. The term Big Data is used in defining data to describe data that is in the petabyte range or higher.It's also described as 5V: variety, volume, value, truthfulness, and speed. Nowadays, web-based e-commerce has spread enormously, business models based on Big Data have evolved and treat data as an asset in itself. And there are also many benefits of Big Data, such as reduced costs, improved efficiency, improved sales, etc. Its meaning extends beyond data processing in computer applications. Finance, demographics, health, and marketing also have different meanings, ultimately making up different answers for what the data means.
Qualitative data is everything about the quality of something: a description of an object's colors, texture, and feel, a description of experiences, and an interview are all qualitative data.
Quantitative refers to a number. E.g. the number of golf balls, the size, the price, the score in a test, etc. However, there are also other categories that you will probably find:
Categorical ones put the item you are describing into a category: in our example, the "used" condition would be categorical (with categories like "new", "used", "broken", etc.)
The discrete ones are numerical information that have gaps: p. Eg the golf ball count. There can only be whole numbers of golf balls (there are no 0.3 golf balls). Other examples are test scores (where you receive, for example, 7/10) or shoe sizes.
Continuous data is numerical information with a continuous range: p. Ex. The size of the golf balls can be any value (eq 10.53 mm or 10.54 mm but also 10.536 mm), or the size of your foot (unlike the size of your shoe, which is discreet): in continuous data, all values are possible with no gaps in the middle.
How to analyze them?
Analysis in qualitative research
Analysis and research on subjective information works somewhat better than numerical information, since quality information is made up of words, representations, pictures, objects, and sometimes pictures. Getting knowledge of this tangled information is a confusing procedure; therefore, it is generally used for exploratory research and analysis.
Finding patterns in qualitative data
Notably, the analysis process in qualitative research is manual. Here specialists, as a rule, read accessible information and find monotonous or frequently used words.
Analysis in quantitative research
The main stage in research and analysis is to do it for the exam so that the nominal information can be turned into something important. Its preparation includes: Validation, Editing and Coding. For quantitative statistical research, the use of descriptive analysis regularly provides supreme numbers. However, analysis is never adequate to show the justification behind those figures. Still, it's important to think about the best technique to use for research and analysis that fits your review survey and what stories the specialists should tell. Consequently, companies that are prepared to do what it takes in the hyper-competitive world must have a remarkable ability to investigate complex research information, infer remarkable insights, and adapt to changing market needs.
Unstructured and structured data
Data for humans
A simple phrase - "we have 5 white golf balls used with a diameter of 43mm at 50 cents each" - may be easy for a human to understand, but for a computer this is difficult to understand. The sentence above is what we call unstructured data. Unstructured means it doesn't have a fixed underlying structure - the sentence could easily be changed and it's not clear which word refers to what exactly.
Data for computers
Computers are inherently different from humans. It can be exceptionally difficult to get computers to extract information from certain sources. Some tasks that humans find easy are still difficult to automate with computers. For example, interpreting text that is presented as an image is still a challenge for a computer. If you want your computer to process and analyze your data, it must be able to read and process it. This means that it must be structured and in a machine-readable form.
Why its visualization is important to any career
It's hard to think of a professional industry that doesn't benefit from making data more understandable. All fields benefit from its understanding, as do the fields of government, finance, marketing, history, consumer goods, service industries, education, sports, etc. It is increasingly valuable for professionals to be able to use data to make decisions and use images to tell stories of when informs who, what, when, where, and how. While traditional education tends to draw a clear line between creative storytelling and technical analysis, the modern professional world also values those who can cross between the two - their visualization is right in the middle of analysis and visual storytelling.
Ethics in data management
The ethics of this field, builds on the foundation provided by informatics and information ethics but, at the same time, refines the approach supported so far in this field of research, by changing the level of abstraction of ethical research, from be focused on information to be data. It highlights the need for ethical analyzes to focus on the content and nature of computational operations (the interactions between hardware, software, and data) rather than the variety of digital technologies that enable them. And it emphasizes the complexity of the ethical challenges posed by data science. Due to such complexity, ethics must be developed from the beginning as a macroethics, that is, as a general framework that avoids specific and limited approaches and addresses the ethical impact and its implications and its applications within a coherent, holistic and inclusive framework.
IBM (2012), Bringing Big Data to the Enterprise, Unpublished Report, Armonk, NY: IBM Watson Foundation.
Godika, S. (2015), “Big Data: 9 Steps to Extract Insight from Unstructured Data,” www.datamation.com/applications/big-data-9-steps-to-extract-insight-from-unstructured-data.html, retrieved on June 7, 2015
Reissman, C. K. (2008), Narrative Methods for the Human Sciences, Thousand Oaks, CA: Sage Publications, Inc.
Smith, J. (2013), “Six Types of Analyses Every Data Scientist Should Know,” Data Scientist Insights, http://datascientistinsights.com/2013/01/29/six-types-of-analyses-every-data-scientist-should-know/, retrieved on June 5, 2015.