- Slice information into
multiple dimensions and present information at various levels
- View trends and develop
historical tracers to show operations over time.
- Produce pointers to
synergies across multiple enterprise dimensions.
- Provide exception analysis
and identify isolated (needle in the haystack) opportunities.
- Monitor adversarial
capabilities and developments.
- Create indicators of
- Conduct What-if Analysis
and Cross-Analysis of Variables.
How It Works
modeling (MDM) is a decision support architecture applied to data
warehouses. This technique conceptualizes business models as a set
of measures described by ordinary facets of business and views information
from the perspective of a "slice of time". Unlike OLTP
systems, which record discrete events or transactions such as journal
entries, purchase orders or billing items, MDM systems are concerned
with the quantitative results of events at intervals in time. Three
concepts are fundamental to understanding MDM: Dimensions,
Attributes, and Facts.
are broad classes of business processes and functions. Common Dimensions
include: Time, Geography, Company, Customer, Product, and View/Scenario.
can be broken down into smaller categories called Attributes -- provide the structure for the exploration
Attributes are focused classes or subsets of Dimensions. They provide the depth of Dimensions beyond identifying codes. For example, Attributes of a Product Dimension might include a hierarchy of Item, Class,
Brand, and Division.
Facts are the numerical measures of the business
process being modeled. These measures describe numeric computations
aggregated by OLAP engines. Types of Facts include: Gross
Sales, Percentage Gain/Loss, Transaction Volume, etc.
techniques, relationships and patterns are revealed by the existence
of Facts at the cross section of Dimensions. For example, if there are sales from Customer
X of Product Y, then a relationship between Customer and Product
and Dimensions are physically represented in a relational database called a "star
schema". Each Dimension in the star schema is described in its own
table. Facts are arranged in a single large table indexed
by a multipart key composed of the individual keys of each Dimension.
Fact tables contain the quantitative data about a business and can consist
of many columns and millions of rows. Dimension tables are smaller and hold descriptive data that reflect the Dimensions of the business. They describe Facts.
For example, if a Fact is "Sales",
Dimensions might be "Time, Geography, Customer, or Product".
MM-Data Mining supports the reporting and analytical needs
of knowledge workers and executive decision makers by visually presenting
numerical and categorical information in a Dimensions and Fact-based
environment. Information Landscape schemas let decision makers interactively
select Dimensions from the OLAP/MDM star schema and visually
represent and explore them in relation to one another.
X and Y-axis of the Information Landscape schema are comprised of any two Focus
Dimensions provide the structure for the exploration of Facts.
the numeric measures of the business process being modeled-- comprise
the Z-axis. For example, an icon's color and height above the Information
Landscape indicates discrete, Product Sales information.
3-D icons represent
the cross section of Dimensions
Icons of different shape, color, texture and behavior visualize
the implicit relationship modeled by MDM at a given level of granularity.
Hundreds or even thousands of discrete icons populate an Information
Landscape in a structured, scalable manner.
Suppose an executive
decision maker has the following query:
Sales, in Dollars and Units, by Company for the last three years,
as compared with Shipments and Budgeted Sales for the same period?"
This query provides
us with a business process (Retail Sales Data), Facts (Sales
Dollars, Sales Units, Return Units, Shipment Units, Price), and
(Customer, Product, Time, and View). Attributes
(Sales, Shipments By
Week and Store) are worked out by evaluating the more detailed features
of the Dimensions
at various levels of granularity or detail.
for this query can be diagrammed as follows:
occupy the (X) axis of the Information Landscape schema. Geographic Sales Regions
are depicted along the (Y) axis. 3-D icons depict different Products (SKUs)
within a Product Line. Height of the icons above or below the resulting
grid (Z axis) represents Sales Volume or Volume Changes. Color and
other visual characteristics (spinning, flashing) encode discrete
numerical, as well as outlier statistics concerning attributes,
making patterns for both extremes of opportunity and concern easily
recognizable. Certain icons flash to indicate which attributes of
the Products in what Sales Regions have been/are meeting Projected
Sales Goals. Arrows associated with each icon indicate a change
in the business' overall performance level, measured daily, weekly
or monthly. These arrows clearly depict outlier dimensional attributes
as they intersect Sales Facts.
Information Landscape schemas
can be generated on the fly and can be manipulated to show, for
example, criteria data, development trends, research gaps, duplicative
efforts, and synergistic applications. Outputs can be used to document
real and projected performance within specific areas to support
and justify the funding activities.
Composite Information Landscape
schemas (CLS's) aggregate landscapes hierarchically for scalability.
CLS's provide users with enhanced situation awareness: the ability
to quickly review the state of the enterprise and to keep track
of progress being made based on variable criteria. More important,
synergies of efforts can be detected and recommendations made about
their integration. Since the entire enterprise can be presented
within a CLS, decision makers can determine if strategic objectives
are being met.
Analysis. A decision support system for managers in
the retail/distribution industry. This product monitors all aspects
of sales and indicates a business' opportunities and problems by
tracking important dimensions such as products, subproducts, location
and sales representatives. It provides an overview of a company's
business by letting the user choose, measure and monitor a company's
vital factors, such as: sales level; pricing; actual versus expected
goals; global and detailed level indicators and geographies. Drill
down to precise text and numbers on any given store or other granular
level in the hierarchy with a mouse-click.
Banking. A decision support system for managers in
charge of medium to large-sized, geographically dispersed retail
banks. This product offers in-depth analysis of pre-selected retail
indicators, navigation through information landscapes that are
scalable to access multiple data sources. With a click of the mouse,
indicators can be viewed geographically by areas, regions, territories,
districts or specific locations.
Detection. A decision support system for managers to
track potential fraud activity. This product offers in depth-analysis
of pre-selected fraud indicators, navigation through information
and client/server access to scalable data warehouses. Comparisons
and projections of key indicators reveal present and future trends
in addition to spotting opportunities and problems. By maintaining
these triggers, an executive will be proactive rather than reactive
to fraudulent transactions.
MM-Credit Scoring. Evaluates credit-risk
scoring parameters on hundreds of thousands of consumer loans. Credit
performance can be depicted by location and other dimensions. In
this way, the efficiency of different bank credit policies can be
compared. Drill down helps the user identify the characteristics
of similarly performing loans, as well as determine the most accurate
measures for specific regions. A default view utility enables users
to set preferences which render critical information and reveal
relationships in credit performance patterns.
MM-Healthcare. Decision support tools for corporate benefits
professionals that graphically render health industry data as three-dimensional
information landscape, within which users navigate and interact.
Programs compare the costs and benefits of HMO's, hospitals and
doctors nationwide, breaking them into their component cost centers
and clinical outcomes; or, provider practice - patterns by peer
group and by national benchmarks.
MM-Bioinformatics. A decision support and investigative tool
for combinatorial chemistry, high throughput screening, bioinformatics
and genomic databases. In these areas, finding the needle in the
haystack significantly enhances the product development cycle. Visual
database mining tools are ideal for helping navigate through vast
amounts of genomic data in the quest for new information about human
MM-Image Event Detection (IED). A decision support system for analysts to track potential enemy activity within a 10 by 10 Km square digital image.
This product offers in depth-analysis of pre-selected event indicators, navigation through image data and client/server access to scalable digital images. Comparisons of key indicators reveal present and future trends in addition to spotting events. By maintaining these triggers, defense forces will be proactive rather than reactive to IED or rocket attacks.