Table of contents
- Story
- Slides
- Slide 1 OMB Data Visualization Tool Requirements Analysis: SAS
- Slide 2 Background
- Slide 3 Gartner BI Magic Quadrant: SAS
- Slide 4 SAS: Home Page
- Slide 5 SAS: Visual Analytics
- Slide 6 SAS: Registration
- Slide 7 SAS: Full Demo
- Slide 8 SAS: Visual Analytics Client
- Slide 9 SAS: Data Dictionary Spreadsheet
- Slide 10 SAS Visual Analytics: An Overview of Powerful Discovery, Analysis and Reporting
- Slide 11 SAS Visual Analytics: Knowledge Base
- Slide 12 Silver Spotfire: Feature Matrix
- Slide 13 SAS Providers for OLE DB: Download
- Slide 14 Silver Spotfire: Version Comparison 1
- Slide 15 Silver Spotfire: Version Comparison 2
- Slide 16 SAS Visual Analytics Data Sets: Spotfire
- Slide 17 SAS Example Data Sets: Spotfire
- Slide 18 Some Conclusions and Recommendations
- Spotfire Dashboard
- Slides SAS Visual Analytics: An Overview
- Slide 1 SAS Visual Analytics: An Overview of Powerful Discovery, Analysis and Reporting
- Slide 2 Welcome to SAS Visual Analytics
- Slide 3 Content
- Slide 4 Overview of The Data
- Slide 5 Getting Started In SAS Visual Analytics - The Essentials
- Slide 6 Bar Charts - The Basics
- Slide 7 Bard Chart - Add a Hierarchy
- Slide 8 Using a Hierarchy
- Slide 9 Exploring a Heirarchy
- Slide 10 Creating a Hierarchy
- Slide 11 Filters
- Slide 12 Filters - Basic
- Slide 13 Filters - Select and Include
- Slide 14 Filters - Advanced
- Slide 15 Calculations
- Slide 16 Calculations - Creating
- Slide 17 Calculations - Editing
- Slide 18 Geographies
- Slide19 Analyzing Geographies 1
- Slide 20 Analyzing Geographies 2
- Slide 21 Refining Geographies
- Slide 22 Crosstab - Getting Started 1
- Slide 23 Crosstab - Getting Started 2
- Slide 24 Crosstab -Adding Depth
- Slide 25 Crosstab - Drill Down with Hierarchies
- Slide 26 Crosstab - Creating Totals
- Slide 27 Forecast - Getting Started
- Slide 28 Forecast Product Sale & Product Cost of Sale
- Slide 29 Forecast Created
- Slide 30 Forecast Filters
- Slide 31 Forecasting Properties
- Slide 32 Exporting and Using the Forecast Data
- Slide 33 Treemaps - Getting Started 1
- Slide 34 Treemaps - Getting Started 2
- Slide 35 Exploring Treemaps
- Slide 36 Treemaps - More Exploration
- Slide 37 Heat Maps - Getting Started 1
- Slide 38 Heat Maps - Getting Started 2
- Slide 39 Heat Maps - Another Perspective
- Slide 40 Heat Maps - Drilling Deeper
- Slide 41 Heat Maps - Identifying Problem Areas 1
- Slide 42 Heat Maps - Identifying Problem Areas 2
- Slide 43 Correlations
- Slide 44 Correlation Matrix
- Slide 45 Correlation Regression
- Slide 46 Correlation with Gross Margin
- Slide 47 Correlations - Investigating
- Slide 48 Satisfied and Profitable Customers...Buy Which Product?
- Slide 49 Gross Margin Ratio - Star Products
- Slide 50 Box Plots
- Slide 51 Box Plots - Using Them
- Slide 52 Box Plots - Refinement
- Slide 53 Bubble Plots - Getting Started 1
- Slide 54 Bubble Plots - Getting Started 2
- Slide 55 Bubble Plots - Getting Started 3
- Slide 56 Bubble Plots - Exploring
- Slide 57 Bubble Plots - Refining
- Slide 58 Bubble Plots - Time Animation
- Slide 59 Reporting in SAS Visual Analytics - Getting Started
- Slide 60 Reporting in SAS Visual Analytics - The Home Page
- Slide 61Reporting in SAS Visual Analytics - Open Sample Report
- Slide 62 Reporting in SAS Visual Analytics - The Essentials
- Slide 63 Reporting in SAS Visual Analytics - Creating a New Section
- Slide 64 Reporting in SAS Visual Analytics - Editing a New Section
- Slide 65 Reporting in SAS Visual Analytics - Adding a Pie Chart
- Slide 66 Reporting in SAS Visual Analytics - Adding a Line Chart 1
- Slide 67 Reporting in SAS Visual Analytics - Adding a Line Chart 2
- Slide 68 Reporting in SAS Visual Analytics - Adding a Treemap
- Slide 69 Reporting in SAS Visual Analytics - Creating Interaction
- Slide 70 Reporting in SAS Visual Analytics - Working with the Report
- Slide 71 Reporting in SAS Visual Analytics - Finalizing the Report
- Slide 72 Reporting in SAS Visual Analytics - View Reports
- Slide 73 Explore!
- Slide 74 Insight Toys Company - Data Dictionary 1
- Slide 75 Insight Toys Company - Data Dictionary 2
- Slide 76 Insight Toys Company - Data Dictionary 3
- Slide 77 Insight Toys Company - Data Dictionary 4
- Slide 78 Insight Toys Company - Data Dictionary 5
- Silver Spotfire Feature Matrix
- Silver Spotfire Version Comparison
- Research Notes
- Story
- Slides
- Slide 1 OMB Data Visualization Tool Requirements Analysis: SAS
- Slide 2 Background
- Slide 3 Gartner BI Magic Quadrant: SAS
- Slide 4 SAS: Home Page
- Slide 5 SAS: Visual Analytics
- Slide 6 SAS: Registration
- Slide 7 SAS: Full Demo
- Slide 8 SAS: Visual Analytics Client
- Slide 9 SAS: Data Dictionary Spreadsheet
- Slide 10 SAS Visual Analytics: An Overview of Powerful Discovery, Analysis and Reporting
- Slide 11 SAS Visual Analytics: Knowledge Base
- Slide 12 Silver Spotfire: Feature Matrix
- Slide 13 SAS Providers for OLE DB: Download
- Slide 14 Silver Spotfire: Version Comparison 1
- Slide 15 Silver Spotfire: Version Comparison 2
- Slide 16 SAS Visual Analytics Data Sets: Spotfire
- Slide 17 SAS Example Data Sets: Spotfire
- Slide 18 Some Conclusions and Recommendations
- Spotfire Dashboard
- Slides SAS Visual Analytics: An Overview
- Slide 1 SAS Visual Analytics: An Overview of Powerful Discovery, Analysis and Reporting
- Slide 2 Welcome to SAS Visual Analytics
- Slide 3 Content
- Slide 4 Overview of The Data
- Slide 5 Getting Started In SAS Visual Analytics - The Essentials
- Slide 6 Bar Charts - The Basics
- Slide 7 Bard Chart - Add a Hierarchy
- Slide 8 Using a Hierarchy
- Slide 9 Exploring a Heirarchy
- Slide 10 Creating a Hierarchy
- Slide 11 Filters
- Slide 12 Filters - Basic
- Slide 13 Filters - Select and Include
- Slide 14 Filters - Advanced
- Slide 15 Calculations
- Slide 16 Calculations - Creating
- Slide 17 Calculations - Editing
- Slide 18 Geographies
- Slide19 Analyzing Geographies 1
- Slide 20 Analyzing Geographies 2
- Slide 21 Refining Geographies
- Slide 22 Crosstab - Getting Started 1
- Slide 23 Crosstab - Getting Started 2
- Slide 24 Crosstab -Adding Depth
- Slide 25 Crosstab - Drill Down with Hierarchies
- Slide 26 Crosstab - Creating Totals
- Slide 27 Forecast - Getting Started
- Slide 28 Forecast Product Sale & Product Cost of Sale
- Slide 29 Forecast Created
- Slide 30 Forecast Filters
- Slide 31 Forecasting Properties
- Slide 32 Exporting and Using the Forecast Data
- Slide 33 Treemaps - Getting Started 1
- Slide 34 Treemaps - Getting Started 2
- Slide 35 Exploring Treemaps
- Slide 36 Treemaps - More Exploration
- Slide 37 Heat Maps - Getting Started 1
- Slide 38 Heat Maps - Getting Started 2
- Slide 39 Heat Maps - Another Perspective
- Slide 40 Heat Maps - Drilling Deeper
- Slide 41 Heat Maps - Identifying Problem Areas 1
- Slide 42 Heat Maps - Identifying Problem Areas 2
- Slide 43 Correlations
- Slide 44 Correlation Matrix
- Slide 45 Correlation Regression
- Slide 46 Correlation with Gross Margin
- Slide 47 Correlations - Investigating
- Slide 48 Satisfied and Profitable Customers...Buy Which Product?
- Slide 49 Gross Margin Ratio - Star Products
- Slide 50 Box Plots
- Slide 51 Box Plots - Using Them
- Slide 52 Box Plots - Refinement
- Slide 53 Bubble Plots - Getting Started 1
- Slide 54 Bubble Plots - Getting Started 2
- Slide 55 Bubble Plots - Getting Started 3
- Slide 56 Bubble Plots - Exploring
- Slide 57 Bubble Plots - Refining
- Slide 58 Bubble Plots - Time Animation
- Slide 59 Reporting in SAS Visual Analytics - Getting Started
- Slide 60 Reporting in SAS Visual Analytics - The Home Page
- Slide 61Reporting in SAS Visual Analytics - Open Sample Report
- Slide 62 Reporting in SAS Visual Analytics - The Essentials
- Slide 63 Reporting in SAS Visual Analytics - Creating a New Section
- Slide 64 Reporting in SAS Visual Analytics - Editing a New Section
- Slide 65 Reporting in SAS Visual Analytics - Adding a Pie Chart
- Slide 66 Reporting in SAS Visual Analytics - Adding a Line Chart 1
- Slide 67 Reporting in SAS Visual Analytics - Adding a Line Chart 2
- Slide 68 Reporting in SAS Visual Analytics - Adding a Treemap
- Slide 69 Reporting in SAS Visual Analytics - Creating Interaction
- Slide 70 Reporting in SAS Visual Analytics - Working with the Report
- Slide 71 Reporting in SAS Visual Analytics - Finalizing the Report
- Slide 72 Reporting in SAS Visual Analytics - View Reports
- Slide 73 Explore!
- Slide 74 Insight Toys Company - Data Dictionary 1
- Slide 75 Insight Toys Company - Data Dictionary 2
- Slide 76 Insight Toys Company - Data Dictionary 3
- Slide 77 Insight Toys Company - Data Dictionary 4
- Slide 78 Insight Toys Company - Data Dictionary 5
- Silver Spotfire Feature Matrix
- Silver Spotfire Version Comparison
- Research Notes
Story
OMB Data Visualization Tool Requirements Analysis: SAS
Background
- DRAFT White Paper for OMB Pittsburgh, July 11, 2013.
- Start With the End in Mind, Avoid Tool and Turf Wars, and Develop Well-designed Spreadsheets That Can be “Dragged and Dropped” Onto a Tool That Creates Statistics and Visualizations in the Public and Private Clouds.
- Focus on Requirements Analysis by First Comparing Magic Quadrant Leaders and Challengers on Common Data Sets.
- Spotfire Was Able to Reproduce Birst, Information Builders, Logi Analytics, Microsoft, QlikView, and Tableau Data Visualizations With Dynamically Linked Visualizations.
- Broaden This Requirements Analysis to Include More Sample Data Sets and Tools.
- SAS Is a Leader in the Gartner Magic Quadrant.
Gartner BI Magic Quadrant: SAS
SAS Analytics Strengths and Cautions Excerpts
- SAS's portfolio includes tools in areas such as BI, performance management, data warehousing and data quality; however, unlike most other BI platform vendors, SAS primarily focuses on advanced analytical techniques, such as data mining and predictive modeling, where references acknowledge it as a leader.
- The solution-oriented analytic application approach to the market is a differentiator, giving the company the advantage of having a wide variety of cross-functional and vertically specific analytic applications out of the box for a wide variety of industries, including financial services, life sciences, retail, communications and manufacturing.
- In 2012, SAS announced Visual Analytics, the new data discovery product that merges dashboard design with diagnostic analytics and the use of predictive models — a possibility not yet available in some of its competitors' tools. Visual Analytics also provides mobile BI capabilities — a gap that, until now, had been resolved through a partnership with MeLLmo Roambi. Moreover, it is the first visible result of a comprehensive initiative to standardize user interfaces and to better integrate the product portfolio — an area where SAS scores lower than most other vendors in the Magic Quadrant survey. For SAS, it's also a key instrument to reach beyond analytics experts to a more mainstream audience, thereby preventing competitors' data discovery tools from doing so on its customer base.
- References continue to report that SAS is very difficult to implement and use — it was the No. 3 vendor in both categories.
- SAS's dominance in predictive analytics and statistics continues to be challenged on many fronts. IBM is still the main challenger with SPSS and other analytic assets, but wide support of open-source R by large competitors, such as Oracle, SAP and other smaller vendors, will be the most serious threat in the long term. R is challenging SAS for the title of standard coding language for analytics, and is increasingly considered a credible alternative by professionals in the market, eroding SAS's dominance in the analytics community. Other vendors, such as Kxen (not included in this Magic Quadrant), Prognoz, Alteryx or Tibco, are additional sources of competition as more customers adopt analytics.
- Despite SAS's success and awareness as a leader in the predictive analytics space, the company is still challenged to make it onto BI platform shortlist evaluations when predictive analytics is not a primary business requirement.
Note: Bolding by the author to highlight key points.
Source: http://www.gartner.com/technology/re...t=130206&st=sb
Some Conclusions and Recommendations
- Semantic Community Was Able to Import SAS Files Into Spotfire and Export CSV Files From SAS Visual Analytics and Import Them Into Spotfire.
- Spotfire Provides Similar Features To SAS Visual Analytics, But the Author Finds Spotfire Much Easier to Use.
- MindTouch and Spotfire Are More Versatile Than SAS Visual Analytics For Mashups.
- Semantic Community Will Continue to Use The Gartner BI Magic Quadrant Leader Tools and Their Sample Data Sets and to Recreate Visualizations and Dashboards.
Slides
Slide 1 OMB Data Visualization Tool Requirements Analysis: SAS
http://semanticommunity.info/
http://breakinggov.com/author/brand-niemann/
http://semanticommunity.info/Data_Science/Free_Data_Visualization_and_Analysis_Tools/SAS
Slide 9 SAS: Data Dictionary Spreadsheet
Slide 10 SAS Visual Analytics: An Overview of Powerful Discovery, Analysis and Reporting
Slide 11 SAS Visual Analytics: Knowledge Base
http://semanticommunity.info/Data_Science/Free_Data_Visualization_and_Analysis_Tools/SAS
Slide 14 Silver Spotfire: Version Comparison 1
Slide 15 Silver Spotfire: Version Comparison 2
Spotfire Dashboard
For Internet Explorer Users and Those Wanting Full Screen Display Use: Web Player Get Spotfire for iPad App
Slides SAS Visual Analytics: An Overview
Slide 78 Insight Toys Company - Data Dictionary 5
Silver Spotfire Feature Matrix
Source: https://silverspotfire.tibco.com/us/get-spotfire/silver-spotfire-feature-matrix
Detailed feature list
Versions | Personal | Personal Plus | Publisher | Analyst |
Trial | One year | One month | One month | One month |
Monthly Subscription | na | na | $99 | $399 |
Annual Subscription | na | $99 | $1000 | $4500 |
Web Sharing | Personal | Personal Plus | Publisher | Analyst |
Apple® iPad® Compatibility | ● | ● | ● | ● |
Public Sharing | ● | ● | ● | ● |
Invitation-Only Web Sharing | ● | ● | ● | |
Public Content: Max Concurrent Web Viewers | 10 | 20 | 50 | 50 |
Private Content: Max Named Web Viewers | 0 | 2 | 5 | 5 |
Offline Data | ∞ | ∞ | ∞ | ∞ |
Online Storage (MB) | 50MB | 100MB | 250MB | 500MB |
Online Storage (rows) | ~10k | ~100k | ~1M | ~5M |
Data Access | Personal | Personal Plus | Publisher | Analyst |
Replace Data Tool | ● | ● | ● | ● |
Load from Microsoft® Excel®, csv, txt | ● | ● | ● | ● |
Load SAS files (Requires installation of SAS Providers for OLE DB) | ● | ● | ● | ● |
Load from Microsoft Access® | ● | ● | ● | ● |
Load from Microsoft SQL Server® databases | ● | ● | ||
Load from Microsoft SQL Server Analysis Services® databases | ● | ● | ||
Load from OLE DB compatible data sources | ● | ● | ||
Load from ODBC compatible data sources | ● | ● | ||
Load from Oracle® databases | ● | ● | ||
Load from Teradata® databases | ● | ● | ||
Application Building Tools | Personal | Personal Plus | Publisher | Analyst |
Create Custom Hierarchy | ● | ● | ● | ● |
Capture Private and Public Bookmarks | ● | ● | ● | ● |
Action Links | ● | ● | ||
Multiple Data Tables in One Application | ● | ● | ||
Data Transformation Tools | ● | ● | ||
Merge Datasets | ● | ● | ||
Insert Calculated Column | ● | ● | ||
Text Area Controls to Switch Axes and Variables | ● | ● | ||
Tag Data | ● | ● | ||
Interactive Visualizations | Personal | Personal Plus | Publisher | Analyst |
Visual Joining of Multiple Data Sources | ● | ● | ● | ● |
Embedded Geographic Shape Files | ● | ● | ● | ● |
Table | ● | ● | ● | ● |
Cross Table | ● | ● | ● | ● |
Bar Chart | ● | ● | ● | ● |
Line Chart | ● | ● | ● | ● |
Bar & Line Chart | ● | ● | ● | ● |
Pie Chart | ● | ● | ● | ● |
Text Area | ● | ● | ● | ● |
Map Chart | ● | ● | ● | ● |
Scatter Plot | ● | ● | ||
3D Scatter Plot | ● | |||
Treemap | ● | ● | ||
Graphical Table | ● | ● | ||
Box Plot | ● | |||
Heat Map | ● | |||
Parallel Coordinates Plot | ● | |||
Summary Table | ● | |||
Analytic Options and Tools | Personal | Personal Plus | Publisher | Analyst |
Advanced Calculations | ● | ● | ● | ● |
Conditional Coloring Rules | ● | ● | ● | ● |
Reference Lines in Plots | ● | ● | ● | ● |
Trellis mode | ● | ● | ● | ● |
Capture Private Bookmarks | ● | ● | ● | ● |
Number Formatting | ● | ● | ● | ● |
Multi-Y Scaling | ● | ● | ● | ● |
Business Aggregations | ● | ● | ● | ● |
Custom Expressions | ● | ● | ||
Analytic Aggregations | ● | |||
Lists | ● | |||
Curve Drawing | ● | |||
Curve Fitting | ● | |||
Line Similarity Tool | ● | |||
K-Means Clustering Tool | ● | |||
Data Relationships Tool | ● | |||
Hierarchical Clustering Tool | ● | |||
Predictive Modeling | ● | |||
Insert Predicted Columns | ● | |||
Miscellaneous | Personal | Personal Plus | Publisher | Analyst |
Allow Data Export by Web User | ● | ● | ● | ● |
● | ● | ● | ● | |
Export Data | ● | ● | ● | ● |
Export to Powerpoint | ● | ● | ||
Export to PDF | ● | ● |
TIBCO Silver™ Spotfire® client is not for use with other TIBCO Spotfire® deployments. The client may only to be used for connecting to a TIBCO Silver™ Spotfire® server (https://silverspotfire.tibco.com)
Regarding licensing and units, TIBCO Silver™ Spotfire® products are delivered on a Term License basis, client use is per Named User, and web sharing limitations are defined using Concurrent Users for public content and Named Users for private content. Named Client User means a Named User who uses the downloadable and locally installable TIBCO Silver™ Spotfire® client software. Named Web User means a Named User who uses a TIBCO Spotfire® file through a web browser. More details on TIBCO product definitions are available at:http://www.tibco.com/software/productdefinitions.jsp
Silver Spotfire Version Comparison
Source: https://silverspotfire.tibco.com/us/silver-spotfire-version-comparison
Silver Spotfire Version Comparison
* Bold text indicates key upgrade criteria.
Type | Personal | Personal Plus | Publisher | Analyst |
Price / Term | Free for one year | $99 /year | $99 /month $1000 /year | $399 /month $4500/year |
Maximum number of Concurrent read-only web users of your public content | 10 | 20 | 50 | 50 |
Maximum number of Named read-only web users to whom you can grant access to your private web content | 0 | 2 | 5 | 5 |
Online Storage for Web Content | 50MB (~10k rows) | 100MB (~100k rows) | 250MB (~1M rows) | 500MB (~5M rows) |
Web Application Sharing | Public only |
|
|
|
Data Access | MS Excel, MS Access | MS Excel, MS Access |
|
|
Productivity | Replace data tool | Replace data tool |
|
|
Application Building | Capture Bookmarks | Capture Bookmarks |
|
|
Visuals | Dashboard visuals (bar, line, pie, map, cross table) | Dashboard visuals (bar, line, pie, map, cross table) |
|
|
Analytics | Filter & Drill | Filter & Drill | Filter & Drill |
|
Collaboration | Capture Private and Public Bookmarks | Capture Private and Public Bookmarks |
|
|
TIBCO Silver™ Spotfire® client is not for use with other TIBCO Spotfire® deployments. The client may only to be used for connecting to a TIBCO Silver™ Spotfire® server (https://silverspotfire.tibco.com)
Regarding licensing and units, TIBCO Silver™ Spotfire® products are delivered on a Term License basis, client use is per Named User, and web sharing limitations are defined using Concurrent Users for public content and Named Users for private content. Named Client User means a Named User who uses the downloadable and locally installable TIBCO Silver™ Spotfire® client software. Named Web User means a Named User who uses a TIBCO Spotfire® file through a web browser. More details on TIBCO product definitions are available at: http://www.tibco.com/software/productdefinitions.jsp
Questions? Contact mds@tibco.com
Research Notes
Source: http://www.gartner.com/technology/reprints.do?id=1-1DYKLUR&ct=130206&st=sb
Gartner BI Magic Quadrant: SAS Strenghts and Cautions Excerpts
SAS's portfolio includes tools in areas such as BI, performance management, data warehousing and data quality; however, unlike most other BI platform vendors, SAS primarily focuses on advanced analytical techniques, such as data mining and predictive modeling, where references acknowledge it as a leader.
The solution-oriented analytic application approach to the market is a differentiator, giving the company the advantage of having a wide variety of cross-functional and vertically specific analytic applications out of the box for a wide variety of industries, including financial services, life sciences, retail, communications and manufacturing.
In 2012, SAS announced Visual Analytics, the new data discovery product that merges dashboard design with diagnostic analytics and the use of predictive models — a possibility not yet available in some of its competitors' tools. Visual Analytics also provides mobile BI capabilities — a gap that, until now, had been resolved through a partnership with MeLLmo Roambi. Moreover, it is the first visible result of a comprehensive initiative to standardize user interfaces and to better integrate the product portfolio — an area where SAS scores lower than most other vendors in the Magic Quadrant survey. For SAS, it's also a key instrument to reach beyond analytics experts to a more mainstream audience, thereby preventing competitors' data discovery tools from doing so on its customer base.
References continue to report that SAS is very difficult to implement and use — it was the No. 3 vendor in both categories.
SAS's dominance in predictive analytics and statistics continues to be challenged on many fronts. IBM is still the main challenger with SPSS and other analytic assets, but wide support of open-source R by large competitors, such as Oracle, SAP and other smaller vendors, will be the most serious threat in the long term. R is challenging SAS for the title of standard coding language for analytics, and is increasingly considered a credible alternative by professionals in the market, eroding SAS's dominance in the analytics community. Other vendors, such as Kxen (not included in this Magic Quadrant), Prognoz, Alteryx or Tibco, are additional sources of competition as more customers adopt analytics.
Despite SAS's success and awareness as a leader in the predictive analytics space, the company is still challenged to make it onto BI platform shortlist evaluations when predictive analytics is not a primary business requirement.
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