Multidimensional Perceptual Map for Project Prioritization and Selection - 2014 update

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Traditional perceptual maps are created using scatter charts or quadrant diagrams, which are based on two dimensions (X and Y axes). Then data items are plotted on the plane based on their values for the two attributes.
The multidimensional perceptual map does not rely on the definition of any fixed axes. The map is composed of smaller areas (cells), which are characterized by a vector of values that represent multiple attributes (dimensions). The positioning of data items in the map is determined by its calculated measure (usually Euclidean distance) again each cell. An unsupervised clustering technique called Self-Organizing Map (SOM) is used to generate such maps.
The multidimensional perceptual map ca be used in many areas including project portfolio management, project prioritization, marketing research, product evaluation, performance management, portfolio management, etc.

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Multidimensional Perceptual Map for Project Prioritization and Selection - 2014 update

  1. 1. Multidimensional Perceptual Map for Project Prioritization and Selection Jack Zheng Vijay Vaishnavi 2014 Update Originally Presented at AMCIS 2009 Citation: • Zheng, Guangzhi and Vaishnavi, Vijay (2011) "A Multidimensional Perceptual Map Approach to Project Prioritization and Selection," AIS Transactions on Human-Computer Interaction (3) 2, pp. 82-103, https://www.researchgate.net/publication/264935893_A_Multidimensional_Perceptual_Map_Approach_to_Project_Prioriti zation_and_Selection http://www.slideshare.net/jgzheng/multidimensional-perceptual-map
  2. 2. Multidimensional Perceptual Map: First Impression 2
  3. 3. Introduction  Projects are commonly prioritized using a scoring approach  evaluated according to predefined categories, which are then aggregated into one or two priority numbers.  Decision is based on the understanding of multiple aspects (dimensions) of projects, such as project size (in terms of budget, time, or people), risk level, expected return, business goal, strategic impact, etc.  Aggregated scores may only offer a limited view of project importance. This often leads decision makers to ignore the possible differences masked by the aggregation.  This research presents a visual exploration approach based on multidimensional perceptual maps which is generated by self organizing maps.  It incorporates human intuition in the process and maintains the multidimensionality of project data as a decision basis for project prioritization and selection. 3
  4. 4. Common Prioritization Methods  Follow an indexing or scoring approach which evaluates projects in a set of predefined categories with an option of providing simple quadrant diagrams.  The project priority is commonly represented by one aggregated number (score) based on a weighted summation of scores in each criteria.  Some other methods use two numerical indicators instead of one  the additional indicator adds one more dimension of information and enrich the meaning of projects.  projects are readily plotted on a two-dimensional diagram based on two indicators; in doing so, users can easily see project distributions and overall portfolio composition. 4
  5. 5. Problems when dealing with multidimensional projects data  The final decision relies on simple calculated numbers.  Multiple attributes may be used as inputs and contribute to the calculation process, but at the end, these attributes are transformed into one or two indicators for interpretation simplicity. Such simplicity does not always satisfy business need.  These calculated final scores may only offer a limited view of the project importance. An aggregated score tends to homogenize many projects, hiding useful and relevant information that may effectively distinguish them (Wang et al. 2003). That often leads decision makers to ignore the possible differences that get masked by the aggregation, and may result in decisions that are not well justified.  Visualization is a good mechanism to comprehend portfolio composition intuitively. Challenges:  many visualization diagrams are more confirmatory (for reporting purposes) than exploratory, where they are mere static reflections of results after the decision making process has been completed; they are not well integrated into the decision making process itself.  focused on techniques of generating the visualization, less focused on the use of visualization 5
  6. 6. 2D Perceptual Map  Traditional perceptual maps are created using scatter charts or quadrant diagrams, which are based on two dimensions (X and Y axes). Then data items are plotted on the plane based on their values for the two attributes.  These perceptual maps are commonly use for marketing segmentation or project portfolio management. 6 Cooper, R., Edgett, S., and Klwinschmidt, E. "Portfolio Management for New Product Development: Results of an Industry Practices Study," R&D Management (31:4) 2001, pp 361-380. Quadrant or matrix diagrams are fundamentally constructed based on only two dimensions. Trying to fit high dimensional information into these low dimensional models often leaves out the richness of project information, and leads to a narrower understanding of project distribution.
  7. 7. System Design Overview  A system that provides assistance in viewing, understanding and analyzing projects and project portfolios directly based on multiple dimensions of project data in the complete decision process.  An intuitive visual exploration approach based on multidimensional perceptual maps (MdPM)  addresses the weaknesses of traditional scoring/ranking approaches and visualization approaches, while keeping their simplicity and interpretability  reveals the values of underlying attributes and makes them transparent in the process of viewing, understanding, and analyzing projects and portfolios  utilizes proper interactive visualizations to effectively and intuitively handle multidimensional information for the information seeking process.  involving human strength and supporting managerial intuition (Kuo 1998)  Two visual elements 7 Profile Chart A profile chart is a visualization of an object based on the values of its multiple attributes (dimensions) selected to represent the object; such a visualization forms a representative shape pattern that can offer a unique visual impression of the object. Multidimensional Perceptual Map A high level overview visualization that shows the distribution and relative positioning of all objects based on multiple attributes. It is the basis to map analysis targets (products, projects, people, etc.).
  8. 8. 8 Sample Data and Dimensions Six dimensions Scores for each dimension (for one sample project)
  9. 9. Profile Chart  A profile chart is a visualization of an object based on the values of its multiple attributes (dimensions) selected to represent the object; such a visualization forms a representative shape pattern that can offer a unique visual impression of the object.  Profile charts are able to present complete multidimensional “profiles,” avoiding the reduction of multiple dimensions to a single “number,” and providing a strong and memorable impression that is easy for users to remember and compare.  Examples: candle-stick charts (used in stock trading technical analysis), Star and Petal, Parallel Coordinates, radar (or star, spider) diagrams, or can be created using various types of basic charts such as bar charts, line graphs, area graphs. 9
  10. 10. 10 Profile Chart (Details on Demand)
  11. 11. Multidimensional Perceptual Map  Two major features:  The map is composed of smaller areas (cells), which are characterized by a vector of values that represent multiple attributes (dimensions).  The vector of each area may be directly visualized on the map.  The map can be divided into areas at different granularity levels to meet various exploration needs.  The positioning of data items in the map is determined by its calculated measure (usually Euclidean distance) again each cell.  The multidimensional perceptual map does not rely on the definition of any fixed axes.  An unsupervised clustering technique called Self- Organizing Map (SOM) (Kohonen 2001) is used to generate such maps. 11
  12. 12. Multidimensional Perceptual Map Various map layout 12 Each cell is represented by a vector (rather than coordinates) for its properties
  13. 13. MD Perceptual Map Views 13
  14. 14. Cells View 14 An unsupervised clustering technique called Self- Organizing Map (SOM) is used to generate such maps. Each cell is represented by a vector which represents the characteristics of a map cell. Each vector is visualized using the profile chart, which is embedded directly in the cell. A Cell Profile View displays profile charts of all cells. In such a view, the changing trend/pattern of all cells can be directly observed on the map.
  15. 15. Items View 15
  16. 16. Clusters View + Items View 16
  17. 17. Another Four-Cluster Setting 17
  18. 18. 18 All 3 Views
  19. 19. Prioritization Based on Clustering 19
  20. 20. Visual Exploration Process 20
  21. 21. 1. Map Generation  In this sub-process, the goal is to define and create a multidimensional perceptual map for visual exploration. The essential step is to apply SOM algorithm and further customize the results by visual exploration. 21 The first step is to prepare data for map construction and analysis. The most important data are the attributes of projects selected for a particular task. Once the attributes are determined, a data table is prepared based on all values of these attributes for every project. Depending on the value domain of each attribute, weighting and scaling may be applied. In the second step, the map is defined using the same attributes that describe the projects. The most important map setting at this step is the map size, defined as number of cells (number of rows by number of columns). The finest granularity of map regions is determined by the map size. The size of the map is not predetermined or suggested by the tool, but rather to be explored and tested out by users. Based on a certain map size, the map is generated using a computer algorithm such as SOM. The verification step can be done initially or may be conducted later in the “Visual Exploration” process if any abnormality is discovered.
  22. 22. 2. Visual Exploration In this sub-process, the key steps are user interactions with the visualization (visual exploration actions). The process and actions are designed in accordance with the visual information seeking mantra: Overview, Zoom & Filter, Details- on-Demand (Shneiderman, 1996). 22 Users utilize three map views to quickly focus on certain parts of the map and narrow down candidate projects for final comparison. They may focus on specific regions and projects that are of interest (“Zoom”). Users can switch between fine grained Cells Profile View (“Drill Down”) and any coarse grained Region Profile View (“Drill Up”). A set of target project groups can be defined and highlighted on the map (“Filter”) (Figure 9). Once candidate projects are selected, users can go further to compare individual projects head-to-head using the profile chart comparison tool (“Details-on-Demand”) (Figure 10). The profile charts give clear justifications for analyses and decisions. The overview is used to comprehend the whole map and customize it. The system will transform SOM results into the Cells Profile View (Figure 6) and give users a general sense of the map (“Overview”). A major action in this sub-process is setting different granularity levels of map regions (Region Profile View). The system supports the definition of multiple region sets (multiple ways and levels to divide a map). A user defines these regions by directly observing, comparing, and contrasting cell patterns. Each region’s profile is created by the system on the fly and presented to users though profile charts (see Figure 8). If the map is not satisfactory, then users have three options: 1) try a different set of SOM parameters and re-create the map; 2) increase the size of the map (to decrease the granularity level) such that the resulting vector change trends are smoother; or 3) directly change selected cell vectors in the SOM result (bypassing the algorithm).This kind of direct human intervention is an example of applying intuition and sometimes is very effective. Once the map is deemed to be satisfactory, it can be saved and reused later for analysis consistency. Last, selected projects can be plotted on the map (Item Projection View).
  23. 23. Conceptual Model 23 In general, the designed approach is a computer system driven visual information seeking process
  24. 24. Understanding the Approach An exploratory approach, rather than a confirmatory one It helps to quickly understand the big picture, discover potential patterns, narrow down areas of focus, and come up with hypotheses intuitively. The system is complementary to other approaches and systems, not a replacement May need to use together with other kinds of tools 24
  25. 25. Applicable Areas Project portfolio management, project prioritization Marketing research Product evaluation Performance management Portfolio management 25
  26. 26. More Infomration  Zheng, Guangzhi and Vaishnavi, Vijay (2011) "A Multidimensional Perceptual Map Approach to Project Prioritization and Selection," AIS Transactions on Human-Computer Interaction (3) 2, pp. 82-103  https://www.researchgate.net/publication/264935893_ A_Multidimensional_Perceptual_Map_Approach_to_Pr oject_Prioritization_and_Selection  http://aisel.aisnet.org/thci/vol3/iss2/3/  Zheng, Guangzhi, "A Multidimensional and Visual Exploration Approach to Project Portfolio Management." Dissertation, Georgia State University, 2009.  http://scholarworks.gsu.edu/cis_diss/34 26

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