Assignment Overview: Contemporary issues in digital media
In previous reports we have looked at various concepts of visualization in digital media and also evaluated the views that have been presented on visualization where the comparisons and approaches were also looked at. However, at the same time there is a need to look at the issues that are there and how they can be tackled. To ensure that the issues are understood, it is important to understand few things that include knowing your client, know your consumers and prove results beyond direct response (Greg, April 21, 2010). It is very important information visualization systems to focus on the end users capability to understand and benefit from what is being presented in the form of data, more importantly as the decision making entirely depends on the providence and user experience rather than on the design. In this proposal we will look at analytic gaps that are an epitome of obstacles faced by visualizations aiding towards analytic tasks likes decision making. In this report, a proposal will be presented for a framework that helps design and evaluate information visualization systems (Amar Robert and Stasko John).
1.1 Problem
Information visualization systems are available in plenty, however it is important that these systems are understood by users.
1.2 Aim
To present a framework that helps design an information visualization system.
1.3 Outcome
The idea is to get a tool for visualization that is capable to analyses the input and is able to make some decision based on the data it has.
2. Methodology
2.1 Introduction
A good information is what speaks for itself and that is where contemporary visualization system comes into picture. In recent times the focus of design for these systems has been primarily on correspondence of representation of data but they lack the capability of decision making and this is mainly because of lack of affordability, no intent of trying to take decisions. The data checking is generally done at basic levels which do not yield the required results in order to come out with some conclusion. This is so because these are basic queries that extract some data which is enough to suggest some change or a problem in the system. Some checking can also be done on historical data but overall the trend that is seen is that there is very little interest in trying to get information that is capable of making a decision.
2.2 Identifying the Analytic Gaps
Before coming up with a visualization system that is capable of decision making, it is important to identify the gaps that are identified due to analysis.
2.2.1 Representational Primacy
Information visualization is primarily focused on what is being designed and then depending of the designer and the facts that the user knows which helps to bring down the gaps that are there amongst the data and its use irrespective of how well it describes and how well the user understands and hence the need for analytical thinking while designing such a system.
2.2.2 Gaps amongst Representation and Analysis
There is this need for doing much more than just represent the data which is probably the ability to make decisions where the constructive data is got from the a pre-defined stuff. Bertin, J. (1981).
2.3 Example Describing Analytical Gap
With this intention of understanding how the above mentioned gaps and the systems that currently exist fall into this category, here is an example of managerial decision making along with a scenario for better understanding.
For better understanding, let us consider a fictitious company that was growing at a rapid pace but all of a sudden saw a collapse that was probably due to the need for increase sales but were lacking the capability to keep up with the sales. This resulted in an increase in manufacturing but decline in sales as it could not cope with the increasing expensive infrastructure Senge, P.M. (1994). In this scenario, what happened wrong was that the managers had a target that they were confident about achieving and also fore saw growth. They were very confident about achieving their targets and also saw growth in other areas that included the manufacturing units of the company. However, in the process they did not pay any attention to the response that they were getting from the reports from sales completed. There is a possibility that the report would have surely have shown some irregularity which wouldn’t have been unnatural. However, it is not every time possible to get accurate results when the data volume is always increasing.
2.4 Bridging the Analytic Gaps
It is practically not possible to evaluate each system but there can be some categorization of some tasks that are common and help in developing good systems for visualization. There has been some development in the field of developing categories for organizing low-level tasks that help in visualization and at the same time are useful in coming up with presentations that make sure that these tasks are connected to the required procedures. There is no denial that the low-level tasks are important but there cannot be relied upon for bridging the analytic gaps and then at the same time there are few things that are required to be kept in mind while evaluation the tasks or gaps is the capacity for items where taking a decision is a difficult job, more importantly under uncertainty and learning a domain. At the same time it is important that the user is should be proficient enough to communicate the data in a manner that helps in making a decision (Amar Robert and Stasko John).
Some tasks that are mentioned here will to some extent help come up with a method that will help form an approach that include determination of Domain Parameters, Multivariate Explanation and Confirm Hypotheses.
2.5 Design and Evaluation Framework
After looking at the analytic gaps and bridging them, it is time to propose a design and evaluation framework that will help to put into action the knowledge tasks in a given situation. One is required to apply the knowledge tasks while coming up with a design for visualization tool. This is done in order to be able to generate new subtasks for a visualization to support, Identify possible shortcomings in representation or data and discover possible relationships. There is also a possibility to make use of these tasks for an overall system that helps in evaluating the tool Nielsen, J., and Molich, R. (1990), that is realistic.
Let us now look at six main knowledge tasks which are Expose Uncertainty, Concretize Relationships, Formulate Cause and Effect, Determine Domain Parameters, Multivariate Explanation and Confirm Hypotheses (Amar Robert and Stasko John).
Alternatively, knowledge tasks can also be used to check how commercial tools might or might not be meeting the challenges posed by the analytic gap and these knowledge task are same as mentioned above (Amar Robert and Stasko John).
2.6 Conclusion
In this proposal, we were able to focus on the current information visualization systems on representational primacy and have realized that for this system to become more powerful and useful there has to be a parallel focus on analytic primacy. It was also acknowledged that there are some limitations in current systems which are the two analytical gaps which include the gap that is there amid a relationship and making sure that there is belief that in the relationship is correct and at the same time the difference about the actual requirement and the user sees is highlighted so that the correct decision can be taken. In response to these gaps, a proposal was made in the form of several tasks that help to lessen the gap and also explained the steps to implement the same for systematic design where the focus has been more on information visualization.
3. Reference List
[1] Dr Couros Alec,February 2009. Popular Issues in (Digital) Media Literacy. EC&I 831.
[2] Greg, April 21, 2010. 5 Problems Digital Media Needs to Solve.
[3] Bertin, J. (1981). Graphics and Graphic Information Processing, Berlin.
[4] Amar Robert and Stasko John, A Knowledge Task-Based Framework for Design and Evaluation of Information Visualizations. College of Computing/GVU Center Georgia Institute of Technology Atlanta, GA USA 30332-0280.
[5] Senge, P.M. (1994) The Fifth Discipline. Currency, 1994.
[6] Nielsen, J., and Molich, R. (1990) Heuristic Evaluation of User Interfaces. In Proc. CHI 1990, ACM, pp. 249-256.
[7] Gee James Paul and Levine Michael H., March 2009. Welcome to Our Virtual Worlds. Volume 66.
[8] Hunter Mark Lee, Mapping Digital Media: Digital Media and Investigative Reporting. Reference series 2.
