Portals eNewsletters Web Seminars dataWarehouse.com DM Review Magazine
DM Review | Information Is Your Business
   Information Is Your Business Advanced Search

Business Intelligence
Corporate Performance Management
Data Integration
Data Quality
Data Warehousing Basics
Master Data Management
View all Portals

Scheduled Events

White Paper Library
Research Papers



DM Review Home
Current Magazine Issue
Magazine Archives
DM Review Extended Edition
Online Columnists
Ask the Experts
Industry News
Search DM Review

Tech Evaluation Center:
Evaluate IT solutions
Buyer's Guide
Industry Events Calendar
Software Demo Lab
Vendor Listings

About Us
Press Releases
Advertising/Media Kit
Magazine Subscriptions
Editorial Calendar
Contact Us
Customer Service

Components of a BI Dashboard: Spatial Data & Visualization

  Article published in DM Review Magazine
March 2005 Issue
  By Michael L. Gonzales

People think visually. We need to see our logic.1 Map makers have always known that a map is not just a tool for showing how to get from here to there. It is a method - a technique for organizing and embedding knowledge in a manner readily understood by all.

However, the typical business intelligence (BI) tools implemented, such as dashboards, are far m ore simplistic. Consequently, many of these tools place the burden of discovery and information insight on the user with little help other than spreadsheets, columnar reports and, of course, pie charts.

This presents an opportunity for leading BI vendors. The simple fact is that humans think visually - spatially. Therefore, the best BI environments provide advanced visualization techniques based on spatial relationships. These two components foster information such that insight can be easily recognized by users:

Spatial Data. Current systems handle the who, what and when, but the where is completely under-exploited.2 Spatial data enhances the who by creating new informational content (binding substantial third-party data from companies such as D&B to your existing data) as well as directly enabling analysis for the where.

Visualization. Interrelationships that might otherwise be difficult to describe or explain are often readily understood when visually presented. The visualization helps the analyst interrogate the data, while also serving as an excellent means of explaining the information to a broader audience, including executives and customers.

This article defines the value of visualization and spatial data as two critical BI components. The content then examines how leading dashboard technology effectively implements these components.

Visualization, Spatial Analysis and Business Intelligence

For centuries maps, graphs, charts and various diagrams have provided visual cues to show context and connections, allowing measurement and evaluation.3 Regardless of how abstract or complex, effective visualization exposes the information insight lying just beneath the obvious data. Put simply, visualization is a means to present highly complex and abstract data without overwhelming the user.

Nevertheless, visualization must be more than simple graphical presentations.

The Where of BI

Just as a date explodes data into a wealth of analytical power, so too does an address. From an address, you can build elaborate space-related dimensions with attributes such as street address, street block and city quadrant as well as household income, education, family size and home value, or numeric characteristics such as drive time between the customer and his/her favorite store.

Space is an information windfall for the analyst and is as critical to the process of interrogating our data as Time. There are even aspects of Space that go beyond the value of Time. For example, where Time has a single, constant perspective, Space can represent information about the people who live at a location as well as information about where that location physically is in relation to the rest of the world.

Many business problems explicitly or implicitly require that geography (space) be taken into account. Consider the following everyday business questions:

  • Where are my customers located?
  • How big is my market area?
  • What is my share of the market area?
  • Which market areas offer the greatest potential for growth?
  • Where should I target my direct mailing?
  • Where should I open new sites?

Common BI tools do a good job at analyzing the basic who, what, when and how questions that characterize things such as customers. Data needed to discover who is buying what product on what day through what channel is captured with virtually every transaction. From these modest data elements, data mining tools can identify cross-sell and up-sell opportunities, determine market-basket relationships, profile customers and evaluate distribution channels.

However, these traditional analysis tools fall short in answering the tougher questions of why people buy and where customers live in relation to their purchases. Although the why of a transaction is rarely captured with it, the where of the transaction, its geographic location, is almost always captured. In fact, multiple locations are often captured. Ship-to, bill-to and customer contacts are all often captured at companies everywhere. But unlike the dimensions of who, what and when (customer, product, time), the where gets little attention.

Spatial Visualization vs.Simple Visualization

There are several differences between visualizations based on spatial data and those visuals created for traditional structured data or computer-aided drafting (CAD) or graphic applications. Following are four critical distinctions:

Utilizes explicit real-world relationships. In spatial data, there is an explicit relationship between the real-world geometric feature and its associated attribute information so that both are always available when you work with the data. If you select particular features displayed on a map, you can automatically select the records containing the attributes of these features whether they reside in separate tabular files or in a database. When you click on a customer, for example, you have access to all the tabular information associated with that customer such as address, sales, products purchased and whatever else your operational data store (ODS) or data warehouse may capture on the customer.

Geo-coded to known locations around the globe. Spatial data is geo-referenced to known locations on the Earth's surface. To ensure that location is accurately recorded, spatial data always employs a specific coordinate system, unit of measurement and map projection. When spatial data is displayed, it has a particular scale just like any paper map. Graphic files used in CAD programs or other commercial graphics packages are typically stored in units such as inches rather than geographic coordinate systems required for spatial data.

Exploits specific geographic features. Spatial data is designed to enable specific geographic features and phenomena to be managed, manipulated and analyzed easily and flexibly to meet a wide range of needs. Other types of graphic data may be oriented solely toward presentation and display, and may store features such that they can only be analyzed in a limited number of ways. For example, drive time analysis is not possible with linear data created by a CAD or graphics application because there is no explicit network connectivity and direction maintained. Similarly, none of the graphics objects created in these packages have any idea of where they are in relation to all the other graphics, which prohibits any kind of proximity or adjacency analysis. They are just "dumb" graphic objects.

Thematically organized. Spatial data is organized thematically into different layers, like the layers of an onion. There is one layer for each set of geographic features or phenomena for which information will be recorded. For example, census tracts, streets, sales territories and customer locations will each be stored as a separate spatial layers, rather than trying to store them all together in one as CAD or other graphic applications do. This makes it easier to manage and manipulate the data, especially as much of the power of working geographically comes from being able to analyze the spatial relationships between different geographic layers.

A Spatial Dashboard

The highest level of integration for technology is always at the functional level.4 It is here that we see an effort to make transparent the use of underlying technology for the user. Map Intelligence is one example of leading technology that achieves this level of integration, simplifying the integration of geographical data and traditional data sources into your BI applications. The technology effectively applies traditional data, geographic data and advanced visualization into cohesive BI applications. Illustrated in Figure 1 and Figure 2 are dashboards implemented using Hyperion Dashboard Builder and Map Intelligence from Integeo Pty Ltd.5,6

Figure 1: Insurance Analysis

Figure 2: Traffic Analysis

Map Intelligence reaches across the IT architecture to source the data where it is stored natively. Consequently, there is no extra effort necessary on the part of IT or the user to exploit the combined data and technology. All the heavy lifting is done by Map Intelligence, from source to interactive querying to visualizations.

With Map Intelligence there are several immediate advantages:

  • Faster Visualization
  • GIS Link
  • Analytic Granularity Resolved

The combined advantages are where leading products such as Map Intelligence excel - establishing a common analytic platform that reaches across disparate data and technologies, bringing them together is a single, cohesive, synchronized data discovery event for user communities.

Map Intelligence is a middleware component between your BI application and a geographic information system (GIS) and other traditional data sources. It is deployed to act as a link between a GIS server and the BI client. The Map Intelligence server itself is BI client independent and currently supports both Hyperion Dashboard Builder and Excel clients.

The back end is an application server that resides within a Web server (servlet) environment. The application server component is responsible for interactions with the underlying map service. Refer to Figure 3. All spatial capability is derived and served up from the map service being accessed, for example, MapXtreme from MapInfo or ArcIMS from ESRI, Inc., both of which are Internet map servers.7,8 Future releases of Map Intelligence are under development to interface with ArcGIS, from ESRI, Inc. and Envinsa from MapInfo to take advantage of the advanced spatial analytic and presentation capabilities these products offer.

Figure 3: Map Intelligence Architecture

The Hyperion Intelligence dashboard contains a special Map Intelligence component responsible for starting the browser session and formatting the dashboard data that is being transmitted to the Map Intelligence server. This data is efficiently sent through the browser.

The user session is then controlled by the Map Intelligence application server that resides in a Web server as a Web application. Interactions with the map service take place through the Map Intelligence server.

The underlying map service is invoked via a map service connector by the Map Intelligence server to provide GIS information. The Web application that is displayed in the browser is a composite of a map and a Web application. The application content comes from the Map Intelligence server. The Map Intelligence server communicates with the map service using the map service protocol. The Web browser communicates with the Map Intelligence server using HTTP.

A mapping session is created when the user first calls the mapping server. This session is maintained until either the user closes the Hyperion Intelligence dashboard or closes the session from the mapping application.

Because of mature applications such as geographic information systems and leading technology such as Map Intelligence and Hyperion Intelligence, it is now possible to design BI dashboards that support both traditional and spatial data. The technologies ensure the transparent integration of visualization technology and spatial data into BI applications, evolving visualization into a means to help transform data into human understanding.9 


  1. Mace, Thomas H., USEPA National Data Processing Division.
  2. Gonzales, Michael L. IBM Data Warehousing. Indianapolis: Wiley, 2003.
  3. Woodbury, Henry. "Why Your Ideas Need Visual Explanation." Dynamic Diagrams, October 2003.
  4. "Visualization and GIS Integration." Panel Presentation, IEEE Visualization Conference, 1994.
  5. www.hyperion.com
  6. www.integeo.com
  7. www.mapinfo.com
  8. www.esri.com
  9. Peter Kochevar, San Diego Supercomputer Center.

For more information on related topics visit the following related portals...
Business Intelligence (BI), Data Visualization and Scorecards and Dashboards.

Michael L. Gonzales has been a chief architecture and solutions strategist for more than a decade, specializing in business intelligence technologies and techniques. Gonzales is currently a Principle at Claraview, Inc., where he leads the Education department teaching a series of DW/BI courses internationally. He is a successful author; his latest book is BI Strategy: How to Create and Document. You can reach him at michael.gonzales@claraview.com.

View Full Issue View Full Magazine Issue
E-mail This Article E-Mail This Article
Printer Friendly Version Printer-Friendly Version
Related Content Related Content
Request Reprints Request Reprints
Site Map Terms of Use Privacy Policy
SourceMedia (c) 2007 DM Review and SourceMedia, Inc. All rights reserved.
SourceMedia is an Investcorp company.
Use, duplication, or sale of this service, or data contained herein, is strictly prohibited.