StandOut DataViz Overview
StandOut DataViz is a flexible environment and platform for doing ad hoc data analysis and rapidly building interactive visual query and analysis dashboards.
StandOut DataViz supports the full range of analysis activities:
Prepare: acquire, clean, and transform data.
Plan: hypothesize about data, prepare a strategy for answering questions.
Explore: use charts to execute the analysis plan.
Present: format results as a convincing presentation to others.
Manage: organize windows, take notes.
Assess: validate observations and conclusions.
Re-Orient: review progress against goals, especially when the analysis spans multiple sessions.
Work with StandOut DataViz like this:
Start with a Business Problem
StandOut DataViz is a tool for answering business questions based on data. You start using it with a business question or problem in mind. Your business question may be simple: what are the interesting relationships in this data? This leads to ad hoc analysis, looking at data to find the relationships within it. Or, you may start with a concrete business problem: who are my best customers, what makes a promotion successful, which employees are most productive, etc.
With a problem in mind, you need data that can answer the question.
Get Your Data
StandOut DataViz provides easy connection to a wide variety of data sources:
Text (or CSV formatted text),
Microsoft Excel spreadsheet,
Microsoft SQL Server database,
Microsoft Access database,
Oracle database,
Certain ODBC-compliant databases, such as MySQL.
Once data is accessed from a source, the connection may be remembered and reused in a subsequent session.
Data to be imported from a source may be examined or have its characteristics changed during loading using the Project Workshop. This may also be used to set the string used to display missing values.
Data Is Stored in a Data Pool
Data is stored in memory in the "Data Pool". This is an in-memory repository that is shared by all StandOut DataViz charts and displays. The Data Pool:
Provides a simple relational model for representing data: data fields are collected into tables.
Fields may be incorporated in a table from other tables by "copying" them, and tables may be linked to copy data between them.
Each data field has a storage type of string, integer number, floating-point decimal number, or date/time.
Fields also have a Statistical Value Type of Nominal, Ordinal, or Continuous; this is set in the Project Workshop.
Unlike the traditional relational model, however, all StandOut DataViz Data Pool tables have two additional attributes: selection state and color. Each row of data can have a different selection state and color, and these attributes apply to any field data displayed (and selected) from that row.
If a Data Table row has been graphically "selected" to highlight it, it is marked as "selected". This selection is reflected in all charts that are showing data from the same table, even if they are not showing the same data fields. Rows that are "unselected" are usually displayed in a gray color.
Color can be mapped to table rows based on data in the table (or linked from another table), and the same color is used in all charts displaying the same selected data. Colors can be customized with the Color Workshop.
New data fields useful for analysis can be created interactively with the Expression Builder.
Explore with Charts & Visual Discovery
A complete set of both simple and novel graphical charts are available for representing your data:
Bar Charts, Pie Charts, Histograms and Line Charts show the distribution of single fields.
Scatter Plots and Multiscapes show two-dimensional interactions between fields.
Data Constellations, a graph view, shows relationships and associations.
Maps show how data is geographically located.
Data Sheets display data details as a columnar text grid.
Summary Sheets show aggregated measures for categories in fields.
Counts gives simple statistics on fields.
Time Table shows events and activities over time.
Heat Map shows data aggregations by groups.
Parabox shows multivariate relationships, including a parallel axis display.
Charts provide a standard set of ways of interacting directly with your data; taken together, these provide Visual Discovery:
Coloring: map color to data rows, usually based on category values in a chosen field.
Selection: graphically sweep items in a chart to highlight them. When items are highlighted, data from the same rows in other charts are also highlighted to show the relationship.
Focus: show details of a particular item by pointing at it.
Remove: hide uninteresting data to better focus on the interesting subset.
Viewpoint manipulation and scaling: control your viewpoint by panning, zooming, and rotating. This is especially useful for 3D views, but even for 2D views such as bar charts, you can get an overview of thousands of categories and then zoom into a subset.
Interactive reordering: reorder items (e.g., order of bars in a bar chart) to better show relationships.
Interactive labeling: show details about an item by touching it.
Aggregation level: show the same data at differing levels of detail. Aggregations are automatically computed for you.
Once your data is available, its time to understand it. StandOut DataViz Author helps you decide how to best visualize your data by letting you build a dashboard, selecting charts and laying them out on a tab in the application. The size and arrangement of the charts is completely under your control.
A successful data analysis session involves more than just visualizing data: the analyst must also understand what he or she is seeing, mark interesting states for later documentation or easy recovery, and generally control the application. StandOut DataViz Author supports these activities:
Chart properties may be displayed in modifiable Property Grids for fine-grained control of the display and behavior of the charts.
Color scales may be created and customized for visual reinforcement of the analysis.
Data tables may be linked statically or dynamically to associated data in multiple tables for more expansive analysis.
The current assignment of colors to data may be displayed in a color legend.
Hierarchy relationships between table fields may be defined and used during the analysis to “drill up” or “drill down” through this hierarchy.
For Shared dashboards, a custom integration may be defined where the current set of graphically identified items is retrieved for formatting, reporting, or exporting to another server.
Filters may be used to condition the data to focus on a particular subset.
Share Your Results
The result of data analysis is conclusions, which often must be incorporated as part of a convincing presentation. Results can be published in a variety of ways. The simplest form of sharing is to share images of the StandOut DataViz charts. This can be done using a variety of formats.
Microsoft Word.
Microsoft PowerPoint.
Adobe Portable Document Format (PDF).
Standard web HTML.
As an image to the system clipboard for pasting into other software.
You can provide help to other people using the dashboard you created via Navigation Panes, documentation formatted as HTML pages that can be associated with each tab in a dashboard. The Navigation Pane for a tab can describe how to use that tab and where to go next. Since this is an HTML page, all of the capabilities of HTML may be used in the Navigation Pane.
Or Share your Results on the Web
A key aspect of data analysis using StandOut DataViz is interactivity; this same interactivity can be crucial to presenting results as well as in discovering results. A StandOut DataViz project created in Author may be published to StandOut DataViz Shared, which provides StandOut DataViz charts in a zero footprint web page. This export requires no modifications or customizations to what was created during analysis, and no use of other tools.
A project published to StandOut DataViz Shared and viewed in a web browser is an interactive analytical dashboard. This same presentation may be used on an ongoing basis by updating the data to allow the same interactive presentation of data and analysis for new instances of the same data.
StandOut DataViz projects may also be sent to other users of StandOut DataViz to allow them to examine the same data. A read-only version of StandOut DataViz, the StandOut DataViz Reader, may also be distributed to others for their use in viewing the analysis of another author, or to apply the same analysis to new data.
An interesting subset of data that has been identified in StandOut DataViz Author may be exported formatted as a Microsoft Excel spreadsheet or as a text file.
The selected subset of data in a StandOut DataViz Shared dashboard may be exported as a text file (if such permissions are configured).
What's Next?
Continue learning about the roles of different StandOut DataViz applications.
Learn about the functions available on the different ribbons in StandOut DataViz Author.
Learn about the capabilities of the various chart types and how to apply them to your data.
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