Microsoft® Data Analyzer
Product Guide
Published: October 2001
Table of Contents
Introducing Microsoft Data Analyzer .................................................................. 1
Overview ..................................................................................................... 1
Data Analysis for the Desktop ........................................................................ 2
Data Analyzer Views ........................................................................................ 3
Creating Views ............................................................................................. 4
Display Types .............................................................................................. 5
Analysis Capabilities ........................................................................................ 8
Navigation ................................................................................................... 8
Filtering ...................................................................................................... 8
Sorting ........................................................................................................ 9
Template Measures and the Template Measure Editor ...................................... 9
Business Center ......................................................................................... 11
Find Similar ............................................................................................... 13
Publishing and Reporting Features ................................................................... 14
Send view by e-mail ................................................................................... 14
Export to PowerPoint® ................................................................................ 14
Export as Web page .................................................................................... 15
Export to Excel ........................................................................................... 15
Architecture and Deployment .......................................................................... 16
Client-Server Deployment ........................................................................... 16
Internet Deployment ................................................................................... 17
Local Cubes ............................................................................................... 17
Extensibility and Custom Solutions .................................................................. 18
XML Support .............................................................................................. 18
Custom Solutions ....................................................................................... 18
Benefits ....................................................................................................... 19
Introducing Microsoft Data Analyzer
Welcome to the Microsoft® Data Analyzer product guide. Microsoft Data Analyzer is
an easy-to-use data analysis tool that makes sophisticated business intelligence
accessible to all users, regardless of their technical expertise. Microsoft Data
Analyzer enables organizations to get the maximum value from the wealth of
business data stored in existing enterprise systems and collected on an on-going
basis through e-commerce and other channels. Data Analyzer unlocks the hidden
meanings buried in this mountain of data and puts this knowledge directly in the
hands of those who need it most: decision-makers at all levels of the organization.
Overview
Data Analyzer’s innovative graphical analysis interface reveals trends, opportunities,
and potential issues at a glance. By providing a complete overview on one screen,
Data Analyzer helps users quickly understand their business data. It enables any
business decision-maker to unlock hidden knowledge in enterprise data, expanding
the analysis capabilities of desktop productivity tools.
Data Analyzer enables users to quickly view data, publish and share data, and
enhances data analysis productivity. Microsoft Data Analyzer is also designed to
take advantage of the powerful capabilities of Microsoft® SQL Server™ 2000
Analysis Services.
Microsoft Data Analyzer offers the following features:

Quickly Analyze Business Data. Unique visualization capabilities and graphical
views enable people to rapidly identify opportunities and trends, find business
anomalies, and review multiple sets of data in one interface for better decisionmaking.

Improve Data Analysis Productivity. Options for displaying multiple
measures, such as gross profit, unit sales, or quantity, or for displaying
relationships between unlimited business dimensions, such as customer, region,
or product, are available in a single, easy-to-use interface. Data Analyzer
includes full support for all new dimension types available in Microsoft® SQL
Server™™ Analysis Services, including parent-child dimensional hierarchies.

Easily Publish and Share Business Data. Data Analyzer Publishing and
Reporting Capabilities make it easy to share data with others, access dataanalysis tools via the Web, and publish data graphically using other Office
applications, such as Microsoft® Excel 2002 and Microsoft® PowerPoint® 2002.

Find business data anomalies and trends automatically. Guided analysis
tools provide standardized questions for analyzing data; built-in template
measures can easily identify key performance indicators; and filters can quickly
select and filter certain criteria.

Leverage existing applications and computing environment. With Data
Analyzer, users can easily access data analysis tools over a network, over the
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Web, or in remote/offline scenarios. In addition to client-server access, Data
Analyzer supports remote connection to OLAP (Online Analytical Processing)
cubes, as well as analysis of local cubes.

Integrate with enterprise business intelligence tools. Data Analyzer
extends the business intelligence capabilities of Microsoft Office XP by adding
rich visualization and analysis capabilities. Data Analyzer’s intuitive user
interface and predefined queries complement the powerful analysis features
provided by Excel 2002, Office Web Components, and digital dashboards.
Designed to work with SQL Server™ 2000 Analysis Services, Data Analyzer is a
valuable component of any organization’s business intelligence strategy and a
key player in the Microsoft business intelligence platform.
Data Analysis for the Desktop
Successful companies already recognize the value of converting vast quantities of
data collected by their e-commerce, customer relationship management (CRM),
enterprise resource planning (ERP), and other systems into actionable information.
Most organizations already employ some form of data warehousing, data mining, or
OLAP analysis to unlock the value of their data; however, the tools and skills
required to do so are traditionally the realm of dedicated analysts.
By providing easy-to-use tools for sophisticated data analysis, Microsoft Data
Analyzer reduces the organization’s dependency on analysts by enabling any
business decision-maker to extract knowledge from enterprise data. This
dramatically increases the speed with which raw data becomes actionable
information.
Companies that rely on Microsoft SQL Server™ and SQL Server™ Analysis Services
to collect and store business data in their enterprise systems can take immediate
advantage of Data Analyzer’s guided analysis features by giving more users access
to this information and giving existing analysts a new tool. Data Analyzer enables
organizations to utilize the advanced features of SQL Server™ 2000 Analysis
Services, including

Full support for drill-through analysis

Support for SQL Server™ Analysis Services dimensional data types, including
ragged dimensions and parent-child dimensional hierarchies

Support for the Analysis Services security model

Support for remote access to OLAP cubes via the Internet
For users and decision-makers who lack a background in the technical aspects of
data analysis, Data Analyzer provides a simple way to view data. The intuitive visual
interface reduces information overload, while a set of predefined queries enable
users to understand their business and explore new opportunities. Guided analysis
features are particularly powerful for these users.
For experienced OLAP users, Data Analyzer provides a powerful new data analysis
tool that enables rapid analysis without sacrificing sophisticated query capabilities.
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Data Analyzer allows analysts to move quickly from graphic views of data to Excel
PivotTable® dynamic views. The ability to build custom template measures using
multi-dimension expressions (MDX) provides the power and flexibility to perform
focused analysis.
Data Analyzer also offers opportunity for Microsoft Partners to develop custom data
analysis solutions. Data Analyzer technology complements the Microsoft business
intelligence platform as well as many of the industry’s third-party offerings. Data
Analyzer is fully extensible, exposing its functionality through the Data Analyzer
Object Model and enabling a wide variety of custom solutions and integration with
existing enterprise systems.
Data Analyzer Views
Data Analyzer views present intuitive, visual representations of complex, multidimensional data. The flexible, dynamic views display the relationships between two
or more dimensions of business data, such as time, product, and region. For
example, a user can view and analyze sales and profit data across geographic
regions for selected products over the last 12 months. A view connects to an OLAP
database and displays the selected dimensions and measures, either in a grid
(table) or as a bar or pie chart.
A view consists of a connection to an OLAP data source, a user-selected set of
dimensions and measures, and a display type. Users define the properties for the
view using the New View Wizard. The wizard creates a specific view type; however,
users can quickly change the display type, add or remove dimensions and
measures, and drill up or down by using the tool bar buttons or menu commands.
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Figure 1. A typical Microsoft Data Analyzer view showing three Dimension panes.
Creating Views
Data Analyzer enables users to quickly generate views of their data using a wizard.
The New View wizard prompts the user to select or add a connection to an existing
OLAP cube—either an OLAP server (accessible over a network or HTTP), or an OLAP
cube file (.cub) stored locally on the user’s computer.
After selecting a data source, the user can follow the wizard step-by-step, selecting
from lists of available dimensions and measures, or simply click Finish to allow Data
Analyzer to generate views based on the properties of the cube. If the user
continues with the wizard, the Define View – Dimensions dialog box (Figure 2)
lists all the defined dimensions for the selected cube, and enables the user to select
one or more dimensions to analyze in the view. The dimensions displayed in the list
correspond with fields displayed in field list of an Excel PivotTable® or PivotChart®
bound to the same OLAP cube.
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Figure 2. The Dimensions dialog of the New View wizard lists the dimensions
that were defined in the database when the OLAP cube was created.
The Define View – Measures dialog box is used to select the measures
(precalculated totals) that should be displayed in the view for each dimension. The
measures available for selection include the measures defined when the cube was
created as well as “template measures” calculated by Data Analyzer. Bar and Pie
chart views display two measures (one represented by color and one by the length
of the bar or size of the slice). Grid view can display multiple measures in columns.
Display Types
Data Analyzer offers three display types for viewing data in an OLAP cube:

Bars view displays either a horizontal or vertical bar chart of the data.

Pie chart view displays data in a familiar pie chart.

Grid view displays the data in a tabular format.
Bars view is the standard view for displaying data in Data Analyzer. Each graph bar
represents a single dimension member, with the length and color representing two
measures. The Color Scale allows easy recognition of patterns and exceptions by
non-expert users, with low values displayed in the red spectrum, midrange values in
the orange-yellow spectrum, and high values in green.
When defining the view, users define the Length property by selecting a measure
to be displayed as a horizontal or vertical bar in the bar chart. Length is typically
assigned to a quantitative measure. For example, if Profit is selected, the length of
each bar in the chart represents the profit for that particular dimension. By
comparing the lengths of each bar, users can see at a glance the difference in profit
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for the selected dimension. This is the same as viewing an Office Chart in Bar or
Column view, and comparing the lengths of the members visually.
Users define the Color property by selecting a second, qualitative measure to be
displayed as a color for each bar. Data Analyzer displays this measure in a color
range from green to red. By default, high values are colored in green and low values
are colored in red, while the values in between are colored in shades of yellow or
orange. Typically, red represents values that might be of concern, such as low sales,
high cost, or negative growth, while green represents the opposite: high sales, low
cost, or outstanding growth. Users can customize the Color Scale of a view in many
ways; for example, by setting green to represent low or midpoint values; by
specifying a numeric range for the Color Scale; or by applying color only to filtered
values.
Figure 3 depicts a typical dimension pane showing the standard Bars view. In this
example, based on profitability data for a fictitious airline company, colors represent
number of passengers, while length represents gross profit. The view uses aircraft
type as a dimension, so the data is summarized by aircraft type.
Figure 3. A typical Dimension pane showing a Bar Chart.
In this view, the jumbo aircraft type was the most profitable for the company since
its bar was the longest. However, the orange-yellow color indicates that it was
among the lowest in number of passengers carried. Compare that with the super
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jumbo aircraft type: its bar is the third longest, which indicates that it was the third
most profitable aircraft type; however, its color is green, which indicates that it was
the highest in number of passengers carried.
The following examples show the same data set displayed in Pie Chart view and Grid
view. In the pie chart, the length measure corresponds to the size of the slice, while
color values remain the same. In Grid view, Number of Passengers and Profitability
are shown numerically alongside a third measure, Total Revenue, which is displayed
only in Grid view.
Figure 4. These Dimension panes show the airline data in Pie Chart and Grid View.
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Analysis Capabilities
Data Analyzer enables users to perform sophisticated analysis from an intuitive
graphical interface. Point-and-click navigation, filtering, and sorting enable users to
fully explore their data by drilling up, drilling down, and searching for members
based on similarities or specified criteria. For users seeking answers to complex
business questions, Data Analyzer offers two options: the Business Center provides
sophisticated queries in the form of predefined questions expressed in simple
sentences, while the Template Measure Editor allows users to quickly build multidimensional expressions (MDX expressions) for calculating custom measures.
Navigation
Data Analyzer’s graphical interface makes it easy for users to navigate through
complex data sets. Users can quickly drill up or down on an entire dimension or a
single member; go to a specific level; or go to the default member (defined in the
cube on a per-user basis). Users navigate by clicking on bars or items of interest,
and selecting from context-sensitive pop-up menus.
Filtering
Microsoft Data Analyzer allows users to quickly filter the members in a dimension,
displaying more information about a particular member or group of members. Filters
are applied graphically: simply click on a bar to filter on that member, or right-click
to select other filter options. Data can be filtered by individual dimension members,
by multiple dimension members, or by applying specific criteria to a measure.
The Reverse Filter command enables users to answer specific questions, akin to
simple what-if scenarios, for example, to determine how revenues would be affected
by discontinuing a region or product line. The command filters the members by the
opposite of the current filter. For example, if the current filter excludes certain
members, the Reverse Filter command changes the filter to exclude the original
members and then include the members that were originally excluded.
In addition to filtering by individual members of a dimension, Data Analyzer allows
users to filter on criteria. Options available for setting criteria to a filter include:

Select a specific member level to use in the filter, or choose from the properties
of all members in the dimension.

Indicate the range of values that will be used for the filter, by measure value or
member name.

If filtering by measure value, specify from a list of operators and comparisons.
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Sorting
Microsoft Data Analyzer enables users to sort the members in a dimension by using
different measures or by using a default sort order. For example, users could sort
members by name, by unit sales, or by profitability. Users can access the sorting
features from the Dimension Properties dialog box or by using one of the sorting
buttons on the Dimension toolbar.
Several options enable users to customize the sorting of their data:

Natural Sort preserves the sort order that existed in the database. This option
disables the Sort By and Preserve Hierarchy options.

The Sort By list contains all measures defined in the OLAP cube, plus template
measures created by Microsoft Data Analyzer. Additionally, users can select any
other measure, even if it is not being displayed in the view. Note that this list
also contains the Name option, which allows you to sort the dimensions by their
names, instead of a measure.

The Sort Order option is used to select whether the member should be sorted in
ascending or descending order. By default, the measures are sorted in
Descending order, so that the longest bars are at the top of the chart in
horizontal view.

The Preserve Hierarchy option preserves the existing hierarchy of the data.
For example, when sorting cities in two or more states by name without the
Preserve Hierarchy option, all cities are grouped together, regardless of state,
and sorted alphabetically. With Preserve Hierarchy selected, the same sort
displays the cities grouped and sorted by state.
Template Measures and the
Template Measure Editor
When defining a view in Data Analyzer, users select measures from a predefined
list. The available measures include the measures defined in the cube when it was
originally constructed on the OLAP server, as well as several predefined measures
that Data Analyzer calculates.
The measures calculated by Data Analyzer are called “template measures.” By
default, Microsoft Data Analyzer includes six template measures. Users cannot
modify the six template measures; however, they can use the Template Measure
Editor to create new template measures that perform specific calculations.
The predefined template measures are as follows:

Average Children's Length. Calculates the average length of this member's
child bars.

Number of Children. Calculates the number of child members for this
particular member.

Percent of total length of members in filter. Calculates the current
member's percentage of the members in the current filter.
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
Change from Last Year. Calculates the percentage of change from the
previous year

Change in Year to Date. Calculates the current member's percentage change
in year to date, using a single selected member of the time dimension as a
reference point.

Change from Previous Period. Calculates the current member's percentage
change from the previous period (month, quarter, year, and so on).
The last three template measures require that a single member of the Time
dimension be selected as a reference point. Changing the selection in time changes
the filter for all dimensions. If no time value is selected, or if two or more time
values are selected, the measures cannot be calculated.
The Template Measure Editor provides an interface for writing expressions to define
custom measures. Users can reuse existing template measures or start from
scratch, building or editing an expression using MDX functions. Expressions can
reference global or cube-specific measures.
Figure 5. Template Measure Editor
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Business Center
Another strength of Data Analyzer is the capability to guide users through the
exploration of their data, unlocking the meaning behind the numbers. The Data
Analyzer Business Center is a powerful guided-analysis tool that allows users to
easily identify trends and answer common business questions. Business Center
enables users to construct sophisticated, multi-dimensional queries using predefined
questions phrased in simple English. For example, a typical Business Center
question might ask, "How did year-to-date unit sales compare to the same period
last year?"
The Business Center feature is available at three levels (View level, Dimension level,
and Member level), and behaves differently based on the level. At the View level,
the Business Center presents a set of predefined questions about the data. At the
Dimension or Member level, it presents a single, customizable question that enables
users to change the dimension or measure being analyzed.
Figure 6. At the View level, Business Center enables users to select from
a list of predefined questions about their data.
When the user selects a question, an explanatory dialog box appears, explaining
how the view will work, what types of dimensions are now available, and what the
color and length measures represent. It gives detailed information on how users can
answer certain business questions by filtering on particular members, and what
colorless members mean.
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Figure 7. At the dimension or member level, Business Center presents a
single, customizable query that helps users find similar members or
identify trends and patterns
At the Member level, the question displayed in the dialog box is generated based on
the selected member. The question displayed will vary depending on the selected
member, and the current dimensions and measures in the view. Typically, the
question will offer the ability to see what other members of the current dimension
are similar to the selected dimension member. Items that appear as hyperlinks
allow the users to select dimension members to filter on.
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Find Similar
When an analysis turns up a member of particular interest or an anomaly, Data
Analyzer’s Find Similar feature enables users to locate additional members that
exhibit similar exceptional behavior. Accessible through the Business Center, the
Find Similar feature identifies members that have similar characteristics, and
presents them side by side for comparison.
For example, Figure 8 shows the results of a Find Similar operation on the Frozen
Foods member of the Product dimension. Moving the slider at the bottom of the
dialog box from left to right displays the similar products in decreasing order of
similarity to “Frozen Foods.”
Figure 8. The Find Similar dialog box ranks members of a dimension
according to similarity to the selected member.
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Publishing and Reporting Features
Data Analyzer includes features that make it easy for users to publish and share
vital business data with others. Data Analyzer’s intuitive graphic representations of
data communicate the facts at a glance, and users can publish these views with a
single click of the mouse.
Export features include the most popular formats for communicating business
information: e-mail, PowerPoint® slides, HTML pages, and Excel workbooks or
PivotTables®.
Send View by E-Mail
The Send to Mail Recipient command enables a user to share a view with other
Data Analyzer users who have access to the same cube. The command creates a
mail message and attaches the view as a .max file. Recipients simply open the file
to launch Data Analyzer and display the view.
Export to PowerPoint
The Export to PowerPoint command exports the current view to PowerPoint® and
opens that application. The export command creates a single PowerPoint® slide that
includes the view as well as a title that describes the dimensions and measures
displayed. The slide can be created as a standalone presentation or appended to an
existing presentation, enabling users to quickly create briefing books containing
multiple views.
Figure 9. PowerPoint® presentation created from the view example
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Export as Web Page
The HTML Report command saves a view as a single HTML page that can be
published to the Web or to a corporate intranet. The page includes the current view
as well as a descriptive title. Web page styles are defined as XSL style sheets. Data
Analyzer includes two styles; administrators can create additional style sheets that
conform to corporate templates or branding.
Figure 10. Web page created from the view example.
Export to Excel
Users who are familiar with PivotTable® analysis or are comfortable using the
analysis tools of Excel can export data from Data Analyzer views in tabular form to
an Excel workbook or PivotTable®. The Export as PivotTable command launches a
wizard that prompts the user to specify dimensions for columns and rows, and
select the measures to include. Experienced Excel users will save time by using Data
Analyzer to quickly navigate through a large cube or to select areas of interest
before creating a PivotTable® to explore in more depth.
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Architecture and Deployment
Data Analyzer allows flexible deployment scenarios to meet the needs of most
organizations. As shown in Figure 11, Data Analyzer works against data stored in
OLAP cubes created by SQL Server™ 2000 Analysis Services. Data Analyzer uses
Microsoft PivotTable® Services to connect to a SQL Server™ 2000 Analysis Server
on a network, to a local cube (.cub) file on the client computer, or to an Internetbased cube exposed through HTTP.
SQL Server™ 2000 Analysis Services provide companies with the most complete,
integrated, and Web-enabled analysis services. The OLAP component includes a
middle-tier server that enables users to perform sophisticated analyses on large
volumes of data with exceptional data-retrieval performance times. Data sources
can include any OLE DB provider, such as SQL Server™, Oracle, DB2, other
relational databases, and flat files.
Figure 11. Logical diagram showing how Data Analyzer communicates with a data source.
Client-Server Deployment
To connect to Analysis Services on a network, PivotTable® Services requires a
TCP/IP connection to a Microsoft® Windows NT®, Windows® 2000, or
Windows® XP computer running SQL Server™ 2000 Analysis Services. The server
host must be able to authenticate the client via domain authentication. This requires
that the host and client be on the same Windows domain or on two domains with
trusting relationships.
Typical client-server scenarios can be found in virtually any large company; for
example, a Finance department may have deployed an OLAP Server for analysis of
enterprise financial data. Deploying Data Analyzer to desktops throughout the
company would allow managers and decision-makers to conduct their own ad hoc
analysis of this data, providing solid statistical basis for decisions, reducing the
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burden on the financial analysts, and speeding up the company’s reaction times to
changing market conditions.
Internet Deployment
Analysis Services cubes can also be accessed over the Internet via HTTP connection.
This scenario requires Web access to a Windows NT, Windows 2000, or Windows XP
host running SQL Server™ 2000 Enterprise Edition Analysis Services. In this case,
domain authentication is performed on the server side rather than on the client, so
the client computer does not have to share a domain with the server.
Secure access via the Web enables a range of extranet scenarios. For example, a
commercial data provider could allow customers or subscribers who use Data
Analyzer to connect to their database over the Web, by distributing saved views via
e-mail. By setting user permissions in SQL Server™, the provider ensures that users
have access only to data for which they are authorized. This scenario provides
customers with real-time access to data, potentially creating new services or profit
centers for the provider.
Local Cubes
Local cubes are particularly useful in small organizations or in support of a
mobile/offline workforce. Local cubes can be exported from a standard OLAP cube
by an OLAP administrator, or a user can export a subset of the data or a set of
query results from Excel using the OLAP Cube Wizard or Offline Cube Wizard.
Local access provides mobile professionals with access to business data anytime,
anywhere, enabling fast, sophisticated analysis in the office, at a client site, at
home, or on the road.
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Extensibility and Custom Solutions
The Data Analyzer Object Model exposes the application’s user interface and
functionality, enabling a wide range of extensibility or custom third-party solutions.
Developers can drive any aspect of the Data Analyzer user interface
programmatically. Thus, they can include any view in a Web page or digital
dashboard with full (or partial) functionality, including full or limited ability to filter,
sort, and drill up or down.
Developers can customize Data Analyzer by using the documented application
programming interface (API). They can also automate Data Analyzer features with
Microsoft Visual Basic®, Microsoft Visual Basic® for Applications (VBA), Microsoft
Visual Basic® Scripting Edition, and Microsoft Visual C++®.
XML Support
Extensibility and integration with custom solutions are aided by extensive use of
XML in Data Analyzer. Data Analyzer files, views, and template measures are saved
as XML-formatted files. Business Center questions are also formatted in XML.
Although detailed schemas are not included with in the API, developers can easily
modify the XML files, or generate XML programmatically to create custom views or
template measures and Business Center questions for a specific line of business. By
modifying the XML that defines the view—for example, by embedding MDX
expressions in place of specific members—developers can create dynamic views that
can be incorporated into Web-based solutions.
Custom Solutions
Data analyzer offers numerous other opportunities for customization as well as
integration with Microsoft Office and third-party enterprise systems:

Integration with digital dashboards. The Data Analyzer includes an
ActiveX® control that can be embedded in a Web page or digital dashboard. By
including a dynamic, predefined view on a home page or corporate portal,
companies can ensure that users are kept aware of pertinent business data in
real time—without waiting for distribution of published reports.

Embedded views. Data Analyzer views can be embedded in any Microsoft
Office document using the Insert Object command. Views can be created on
the fly or by opening a saved view (.max file). This enables analysts to create
dynamic reports that present concise graphical information and invite readers to
explore business data on their own.

Automated reporting. By programming Data Analyzer to export views as HTML
pages and automating the publishing of these pages to the Web or corporate
intranet (or by e-mail), organizations can update shareholders, managers,
and/or employees at regular intervals, with little or no administrative overhead.
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
Custom presentation. Data Analyzer functionality can be used to add a userfriendly interface to virtually any system that performs data mining or reporting
against an OLAP data source. By incorporating the Data Analyzer controls in the
front-end applications of enterprise solutions, such as ERP, CRM, and financial
systems, developers can provide a highly intuitive, visual interface, making
business data accessible to a much wider audience.
Benefits
Microsoft Data Analyzer enables organizations to get the maximum value from the
wealth of business data stored in existing enterprise systems and collected on an
on-going basis through e-commerce and other channels. This results in benefits to
knowledge workers and decision makers, to teams, and to the entire enterprise.
For individual users, Data Analyzer unlocks knowledge that was previously
inaccessible. Users can quickly view the data stored in enterprise systems and easily
conduct sophisticated analysis or queries. This leads to faster, more informed
decision making at all levels of the organization.
Teams and workgroups benefit from Data Analyzer’s integrated publishing tools. The
capability to quickly output views as PowerPoint® slides, to send by e-mail, or to
publish to the Web fit easily with any team’s communication style and ensure that
business information is shared and disseminated as soon as it is available. By
automating publishing of standard views, or incorporating Data Analyzer views into
digital dashboards or intranet portals, organizations can keep entire teams up to
date and in sync with important trends or data.
Organizations that deploy Data Analyzer will benefit immediately from broad, datadriven decision making across the enterprise. Business decisions will be executed
faster and with greater insight, leading to enhanced organizational agility. Data
Analyzer’s intuitive interface and ease of use reduce or eliminate training costs
associated with many business intelligence initiatives. Perhaps most importantly,
Data Analyzer allows organizations to capitalize on the knowledge now locked in
their enterprise applications and data stores, improving the return on investment in
these systems.
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