Another important area of data science is visualization. Data visualization can help in finding a better approach, but information visualization is very important in communicating the results to decision makers, other researchers, local businesses, etc.

A difficult area in big data area is presenting the data to make sense of it. There are two parts here.

• The term data visualization is related to the definition of data.
• The term information visualization is related to the definition of information.

Data is "stuff" that is there. Collected data that is unprocessed or minimally processed data is still data.

Information is data that has value depending on the point of view. What is information to one person can be data to another person.

In 1947, a shepherd boy discovered scrolls in a cave near Qumran in what was then Palestine. About 580 different ancient manuscript fragments were discovered in Qumran Cave number 4 in 1954.

Where the Dead Sea scrolls data or information?

To the shepherd boy, the Dead Sea Scrolls were data, whose value was what the materials were worth.

To scholars, the Dead Sea Scrolls were invaluable information about how people lived and thought 2000 years ago.

The same thing can be data or information, depending on your point of view.

Data visualization is visualizing data to find something useful - this does not need to be very user friendly.

Data visualization is often called exploratory data analysis.

Information visualization is presenting something that has been found to be useful - this needs to be very user friendly.

Information visualization is often called explanatory data analysis.

A useful analogy is that of a jury in a court case. One can use data visualization to determine what to present to the jury - it is sufficient for the data researcher to understand the visualization. But information visualization picks out just the essential results and presents them in such a way that any reasonable person can understand the results.