A Visible Solution Paper
A Strategic Approach to Data Warehouse
Development
Printable
PDF Version
By
Alan Perkins
Vice President, Consulting Services
The business-driven, data-centric Enterprise
Engineering methodology pioneered by Visible
provides an effective, productive, common sense approach to
developing strategic data (information) warehouses.
Copyright © 1997, Visible
Systems Corporation
An "Information Warehouse" is a
collection of computer-based information that is critical to
successful execution of enterprise initiatives.
The Visible definition of an
information warehouse expands the concept of data warehouse. An
information warehouse is more than an archive for corporate data
and more than a new way of accessing corporate data. An
information warehouse is a subject-oriented repository designed
with enterprise-wide access in mind. It provides tools to satisfy
the information needs of enterprise managers at all
organizational levels — not just for complex data queries,
but as a general facility for getting quick, accurate, and often
insightful information. An information warehouse is designed so
that users can recognize the information they want and access
that information using simple tools.
An information warehouse is a blending of
technologies, including relational and multidimensional
databases, client/server architecture, graphical user interfaces
and more. Operational (legacy) systems create, update and delete
production data that "feed" the information warehouse.
The principal reason for developing an information warehouse is
to integrate operational data from various sources into a single
and consistent architecture that supports analysis and
decision-making within the enterprise.
For those enterprises that believe information
is a valuable resource, an information warehouse is analogous to
a physical warehouse. Operational systems create data
"parts" that are loaded into the warehouse. Some of
those parts are summarized into information
"components" and stored in the warehouse. Information
warehouse users make requests and are delivered information
"products" that are created from the components and
parts stored in the warehouse.
Information warehousing is one of the hottest
industry trends — for good reason. A well-defined and
properly implemented information warehouse can be a valuable
competitive tool.
Information
Warehouse Benefits
Implementing an information warehouse provides
significant benefits -- many tangible, some intangible.
- More cost-effective decision making.
An information warehouse allows reduction of staff and
computer resources required to support queries and
reports against operational and production databases.
This typically offers significant savings. Having an
information warehouse also eliminates the resource drain
on production systems when executing long-running,
complex queries and reports.
- Better enterprise intelligence.
Increased quality and flexibility of enterprise analysis
arises from the multi-tiered data structures of an
information warehouse that support data ranging from
detailed transactional level to high-level summary
information. Guaranteed data accuracy and reliability
result from ensuring that an information warehouse
contains only "trusted" data.
- Enhanced customer service. An
enterprise can maintain better customer relationships by
correlating all customer data via a single information
warehouse architecture.
- Business reengineering. Allowing
unlimited analysis of enterprise information often
provides insights into enterprise processes that may
yield breakthrough ideas for reengineering those
processes. Just defining the requirements for an
information warehouse results in better enterprise goals
and measures. Knowing what information is important to an
enterprise will provide direction and priority for
reengineering efforts.
- Information system reengineering.
An information warehouse that is based upon
enterprise-wide data requirements provides a
cost-effective means of establishing both data
standardization and operational system interoperability.
Information warehouse development can be an effective
first step in reengineering the enterprise’s legacy
systems.
The following primer describes each of the
components of an information warehouse (see figure). This
description is based upon the work of W. H. Inmon, credited as
the father of the data warehouse concept.
- Lightly summarized data are the
hallmark of an information warehouse. All enterprise
elements (department, region, function, etc.) do not have
the same information requirements, so effective
information warehouse design provides for customized,
lightly summarized data for every enterprise element (see
Data Mart, below). An enterprise element may have access
to both detailed and summarized data, but there will be
much less than the total stored in current detail.
Highly summarized data are
primarily for enterprise executives. Highly summarized
data can come from either the lightly summarized data
used by enterprise elements or from current detail. Data
volume at this level is much less than other levels and
represents an eclectic collection supporting a wide
variety of needs and interests. In addition to access to
highly summarized data, executives also have the
capability of accessing increasing levels of detail
through a "drill down" process.
- The heart of an information warehouse is
its current detail, where the bulk of data
resides. Current detail comes directly from operational
systems and may be stored as raw data or as aggregations
of raw data. Current detail, organized by subject area,
represents the entire enterprise, rather than a given
application.
Current detail is
the lowest level of data granularity in the information
warehouse. Every data entity in current detail is a
snapshot, at a moment in time, representing the instance
when the data are accurate. Current detail is typically
two to five years old. Current detail refreshment occurs
as frequently as necessary to support enterprise
requirements.
- A system of record is the source of
the data that feed the information warehouse. Data in an
information warehouse differ from operational systems
data in that they can only be read, not modified. Thus,
it is necessary that an information warehouse be
populated with the highest quality data available, i.e.,
data that are most timely, complete, accurate, and have
the best structural conformance to the information
warehouse. Often these data are closest to the source of
entry into the production environment. In other cases, a
system of record may be one containing already summarized
data.
- Reformatting, recalculating, or modifying
key structures
- Adding time elements
- Identifying default values
- Supplying logic to choose between multiple
data sources
- Summarizing, tallying, and merging data
from multiple sources
- When either operational or information
warehouse environments change, integration and
transformation programs are modified to reflect that
change.
- Information warehouse archives contain old
data (normally over two years old) of significant,
continuing interest and value to the enterprise. There is
usually a massive amount of data stored in the
information warehouse archives, with a low incidence of
access. Archive data are most often used for forecasting
and trend analysis. Although archive data may be stored
with the same level of granularity as current detail, it
is more likely that archive data are aggregated as they
are archived. Archives include not only old data (in raw
or summarized form); they also include the metadata that
describes the old data's characteristics.
- One of the most important parts of an
information warehouse is its metadata — or data
about data. Also called information warehouse
architecture, metadata is integral to all levels of the
information warehouse, but exists and functions in a
different dimension from other warehouse data. Metadata
that is used by information warehouse developers to
manage and control information warehouse creation and
maintenance resides outside the information warehouse.
Metadata for information warehouse users is part of the
information warehouse itself and controls access and
analysis of the information warehouse contents. To an
information warehouse user, metadata is like a "card
catalog" to the subjects available.
An information warehouse may have any of
several structures:
- Physical Information Warehouse -
physical database in which all the data for the
information warehouse are stored, along with metadata and
processing logic for scrubbing, organizing, packaging and
processing the detail data.
- Logical Information Warehouse -
also contains metadata, including enterprise rules and
processing logic for scrubbing, organizing, packaging and
processing the data, but does not contain actual data.
Instead, it contains the information necessary to access
the data wherever they reside. This structure is
effective only when there is a single source for the data
and they are known to be accurate and timely.
- Data Mart - subset of an
enterprise-wide information warehouse, which typically
supports an enterprise element (department, region,
function, etc.). As part of an iterative information
warehouse development process, an enterprise builds a
series of physical data marts over time and links them
via an enterprise-wide logical information warehouse or
feeds them from a single physical warehouse.
There are three popular "approaches"
for information warehousing. Unfortunately, two of them are
quick-fix solutions that ultimately waste resources and do not
fully meet enterprise information needs.
- Data Dump -- all enterprise data
are replicated or made available with no attempt to
"scrub" or even categorize the data. This is
like dumping all the contents of a physical warehouse in
the middle of the floor – new stuff, old stuff, and
broken stuff -- and asking your customers to pick out
what they need from the pile.
- Magic Window -- to the data
wherever they exist in the enterprise, again without
ensuring data quality. This is like a big sack in which
there are rubies and emeralds and gold nuggets and broken
glass and rat droppings and poisonous snakes. Sometimes
you can "mine" a gem, but at some point you
will quit putting your hand in the bag.
- Strategic Information Warehouse --
results in enterprise information based upon business
requirements and a common data architecture!
The Visible methodology and
computer-based tools provide flexibility and capability to easily
develop an enterprise-wide, strategic information warehouse.
Visible has pioneered a business-driven,
data-centric Enterprise
Engineering methodology uniquely suited
for information warehouse development. Coupled with Visible's
highly sophisticated, Computer-Assisted System Engineering (CASE)
tool, Visible Advantage
ä ,
the methodology provides a practical and effective way to develop
an information warehouse.
Visible Advantage is a computer-based,
enterprise engineering support system that can be a valuable
asset to any enterprise involved in information warehouse
development. Visible Advantage includes an integrated
encyclopedia (see box: "What Is a Visible Advantage
Encyclopedia?"), extensive reporting capability, and
state-of-the-art modeling, charting, analysis and information
system design tools.
Visible Advantage includes Visible-developed
database applications, reports, utilities, and interfaces. Visible
Advantage has menus and forms that provide easy access to its
powerful tools. These features, along with automatic prompts and
on-line "Help," make Visible Advantage very
user-friendly. Visible Advantage is Windows™-based
and is available in both stand-alone and enterprise (Novellä and WindowsNTä ) versions.
For those enterprises that have not begun to
develop an Enterprise
Information Architecture, Visible’s
Universal Model accelerates the development of the
architecture and provides a foundation for the information
warehouse. The Universal Model reflects Visible’s
over 20 years of business and Enterprise Engineering
experience. The Universal Model is a high-level data model
containing nearly 50 business subject areas and encompassing over
400 entities with 1,000 attributes.
The Visible Universal Model is based on the premise that there are a common set of
functions performed by all enterprises -- business and
government. In the Universal Model, these functions are
grouped into subject areas (business objects). The Universal
Model shows the relationships between the business objects,
as well as the relationships between the data entities that are
necessary to support the object.
By starting to build its enterprise data
architecture with the Universal Model, an enterprise will
significantly reduce the time and resources necessary to develop
its information warehouse. Only the specific detailed data
elements that reflect the enterprises unique information
requirements (and possibly its competitive advantage) need to be
added to the model.
Visible has discovered that the key to
success in information warehouse development is using an
iterative approach that includes active participation of
potential information warehouse users.
Like any other large information systems
project, information warehouse development can get bogged down if
the scope is too broad and the number of people involved is too
large. A clear purpose and scope is necessary to manage the
application of information systems resources, as well as the
expectations of potential information warehouse users. Visible
limits the scope by building an information warehouse one data
mart at a time. Each data mart supports a single organizational
element, enterprise function or business object (e.g., customer,
product, account, etc.), and the scope of development is limited
by the data mart requirements. For the initial data mart, which
usually provides the information warehouse proof-of-concept, the
scope must be sufficient to provide real, immediate, and high
profile benefits. After the first data mart is developed and
implemented, additional data marts can be developed and
integrated over time as enterprise needs dictate and as resources
are available.
Designing and developing an information
warehouse using the Visible Enterprise Engineering
approach involves five very different activities: (1) establish
sponsorship; (2) identify enterprise needs; (3) design
information warehouse architecture; (4) apply appropriate
technology; and (5) implement the information warehouse.
- The first step is to establish sponsorship
for the information warehouse, if it does not already
exist. Establishing the right sponsorship chain will help
ensure successful development and implementation. The
sponsorship chain includes an information warehousing
manager and two other key individuals. At the top of the
chain is an executive sponsor with resources to invest in
information infrastructure improvement. A project
"driver" between the executive sponsor and the
warehousing manager keeps the project moving and on
schedule.
An important
aspect of establishing sponsorship is ensuring everyone
in the enterprise understands the purpose of the
information warehouse, its potential benefits, and the
enterprise’s plan for implementation. The plan
should be developed early in the information warehouse
engineering cycle and should address all of the remaining
activities.
- Identifying enterprise needs is a major
component in the engineering life cycle for any
information system, and it is crucial when engineering an
information warehouse. When developing operational
systems, there is often one single enterprise sponsor or
one group of users with a clear view of what they need,
what the system should look like, and how it should
function. Conversely, when developing an information
warehouse, there are normally multiple potential users,
each with a different idea of what an information
warehouse is and what it should provide, and all
requesting or demanding action. Because of this lack of a
single focused direction, identifying precise enterprise
needs is critical to the success of an information
warehouse project.
Visible
expresses enterprise information warehouse needs in terms
of enterprise measures and critical success factors. An
enterprise’s business plans typically provide the
basis for defining preliminary enterprise needs. Visible
also interviews key enterprise managers and analyzes
other pertinent documentation to determine whether the
enterprise is ready to begin developing a strategic
information warehouse. Visible’s findings are
presented to enterprise management in the form of a
Visible Information Warehouse Evaluation Report that
describes the critical success factors for information
warehouse development, how the enterprise currently
addresses the success factors, and a preliminary plan for
overcoming shortfalls.
Once, information warehouse
development actually begins, Visible conducts a
series of facilitated focus group sessions to refine
preliminary enterprise information needs gathered from
business plans and executives. (If no business or
performance plans exist, similar sessions can be used to
create the plans.) The participants in any these sessions
are the potential information warehouse users for whom
the information is important.
- Completely defining an enterprise measure
includes describing the cycles or time periods used for
the measure. Are quarters, months, or hours appropriate
for capturing useful measurement data? How much
historical data will be needed? These vary greatly by
enterprise. The United States Federal Reserve Bank views
enterprise measures in monthly, quarterly and annual
increments and uses years of historical data to determine
trends in the economy. An insurance company requires
decades of actuarial data for meaningful measures. A
telephone sales operation, on the other hand, uses hourly
enterprise measures and may only keep a few weeks of
information.
- After identifying and defining enterprise
needs, it is advantageous to communicate them throughout
the enterprise. One of the best justifications for
undertaking an architecture project is the synergy
achieved through the process of defining and then
communicating its critical success factors and measures.
Everyone becomes aware of precisely what defines success
and how it is measured. In addition, the measures undergo
a "reality check" by people who were not
involved in their development, but who may be measured by
them and who will be involved in creating the raw data
from which the measures will be derived. Their feedback
is used for refining the measures.
- A well-defined information warehouse model
cannot contain homonyms, synonyms, and other data
definition conflicts. The reason these data conflicts may
exist is because most enterprises have one or more major
terms that are used by everyone in the enterprise, but
mean different things in different organizational units.
One of the most commonly misused terms is
"customer."
To
the Accounting Department, "customer" could
mean the organization (or individual) that receives a
bill. "Customer" could also mean an individual
receiving service or buying a product. To the Sales
Department, "customer" could mean the
organizations on which the salesperson calls. Providing
any one of these interpretations as the enterprise
definition of "customer" would not meet the
needs of the enterprise and would doom its information
warehouse effort to failure. Additionally, each
department could use different names to describe the same
data entity (Customer vs. Client vs. Prospect vs…).
Visible takes great pains
to resolve all data conflicts in the information
warehouse model before continuing with the next phase of
the development cycle.
- Visible documents enterprise
measures and critical success factors as planning
statements in an Visible Advantage encyclopedia,
and documents the supporting information warehouse data
entities in a corresponding data model. Information
warehouse data entities are those that, at any point in
time, tell information warehouse users how well their
enterprise is performing. Providing a clear and
unambiguous definition of every warehouse data entity,
describing the way each is used, as well as defining
derivation formulas, aggregation categories and time
periods, are activities critical to capturing a clear
understanding of an enterprise’s measures. The
resulting enterprise architecture model (see
"Blueprint for an Information Warehouse"),
which links enterprise needs with information warehouse
data entities and enterprise rules, becomes both
requirements documentation and a source for communicating
the contents of the information warehouse (its metadata)
to its users.
|
"Engineering" an information
warehouse is a lot like "engineering" a
physical warehouse. Both involve a rigorous development
cycle and require the right tools.
A building is constructed using
architectural diagrams (blueprints) that clearly depict
the building's infrastructure (structural elements,
walls, electrical wiring, plumbing, etc.). Visible
builds information warehouses from architectural models
of enterprise infrastructure (policies, goals, measures,
critical success factors, etc.).
Blueprints are also used to enlarge a
building or make any significant modifications. Without a
diagram of the infrastructure, such changes are quite
difficult and very costly. It is the same with
information warehouses. Visible first updates an
enterprise's architecture model so that it reflects
changes (new product lines or services, for example) and
then modifies the information warehouse to support the
changed enterprise.
Information warehouse engineering is
easier and less costly when based upon an accurate
architectural model of the enterprise. Further, an
information warehouse is easier to use and consistently
produces desired outcomes when decision-makers have
access to an enterprise architecture (metadata) that
accurately reflects enterprise infrastructure.
|
- Clearly defining enterprise information
warehouse architecture also involves identifying the
correct source of raw operational data to populate the
information warehouse. This effort also addresses
possible integration and transformation logic.
Identifying the systems of record for information
warehouse data entities is one means of validating
enterprise measures.
- After defining and thoroughly documenting
enterprise needs (measures and critical success factors),
Visible begins actual information warehouse
architecture (metadata) design. This activity also
involves active user participation in facilitated design
sessions. There are two types of information warehouse
metadata: structural and access.
Structural
metadata is used for creation and maintenance of the
information warehouse. It fully describes information
warehouse structure and content. The basic building block
of structural metadata is a model that describes its data
entities, their characteristics, and how they are related
to one another. The way potential information warehouse
users currently use, or intend to use, enterprise
measures provides insight into how to best serve them
from the information warehouse, i.e., what data entities
to include and how to aggregate detailed data entities. A
Visible Advantage information warehouse data model
provides a means of documenting and identifying both
strategic and operational uses of enterprise measures. It
also provides the capability to document
multi-dimensional summarization of detail data.
Naturally, the number and specificity
of data aggregation categories in an information
warehouse will depend directly on the types of
individuals who participate in design sessions.
- Strategic thinkers tend to
look for "big picture" answers, and
therefore need very few aggregation categories.
The "roll-ups" for each strategic
aggregation of data, however, can be quite
complex.
- Operational thinkers have a
tendency to want to dissect and review every
measure by every category used in their part of
the enterprise, and thus tend to require large
numbers of less complex aggregation categories.
Structural metadata identifies the
system of record for all information warehouse data
entities. It also fully describes the integration and
transformation logic for moving each information
warehouse entity from its system of record to the
information warehouse. In addition, structural metadata
defines the refreshment schedule and archive requirements
for every data entity.
When the information warehouse
structure changes, its metadata is changed accordingly.
Old versions of the structural metadata are kept to
document the changing nature of the information warehouse
and allow access to archive data.
Structural metadata also includes
performance metrics for programs and queries so that
users and developers know how long programs and queries
should run. Information warehouse performance tuning also
uses these metrics.
Access metadata is the dynamic link
between the information warehouse and end-user
applications. It generally contains the enterprise
measures supported by the information warehouse and a
dictionary of standard terms including user-defined
custom names and aliases. Access metadata also includes
the location and description of information warehouse
servers, databases, tables, detailed data, and summaries
along with descriptions of original data sources and
transformations.
Access metadata provides rules for
drill up, drill down and views across enterprise
dimensions and subject hierarchies like products,
markets, and customers. Access metadata also allows rules
for user-defined custom calculations and queries. In
addition, access metadata contains individual, work
group, and enterprise security for viewing, changing, and
distributing custom calculations, summaries, or other
analyses.
- Only after fully defining enterprise
requirements and designing the information warehouse
architecture should an enterprise begin to select the
technology for the information warehouse. Key technology
issues, in addition to determining the hardware/software
platform for the information warehouse, include
developing programs for loading information into the
information warehouse, implementing access control
(security) mechanisms and selecting one or more user
interface tool sets.
- The following are some important
considerations for determining a hardware platform:
How much data will be in the information
warehouse and how much can the platform accommodate
economically? How scaleable is the platform? Is it
optimized for information warehouse performance? Will the
platform support the software selected for the
information warehouse?
Concurrent with hardware selection is
the selection of system software to support the
information warehouse. Among the choices are operating
systems, development software, and database management
systems. The structure and size of the information
warehouse will determine system software requirements.
For example, an information warehouse that includes data
marts will require not only relational technology, but
also multidimensional access and a client/server
architecture.
- Integration and transformation programs
are necessary to extract information from operational
systems and databases for both initial load and
subsequent updates of the information warehouse.
Sometimes, it is possible to develop a single program for
both initial load and periodic updates of the information
warehouse, but often circumstances make this an
unacceptable development option.
- A separate initial load program is
necessary when the volume of initial data is so large
that it cannot be transferred without adversely impacting
other users of the operational systems. This is
particularly true when initial load and update volumes
are significantly different.
- Separate programs also should be
considered for capturing historical data from the
operational systems for loading into the information
warehouse, because this is usually a one-time process.
- An additional reason for separate initial
warehouse loading programs involves historical data
maintained separately from the operational systems (many
operational systems only maintain the most recent values
for data). This situation usually requires retrieval of
historical data from archive and backup files.
- Under either of these circumstances, one
set of integration and transformation programs initially
loads the information warehouse, and a second set
periodically updates the information warehouse. Update
programs are generally smaller and simpler than programs
developed to load the information warehouse. Update
programs often are built into operational systems to trap
new occurrences of data as they are added. This works
best for well-documented, in-house operational systems.
Update programs that extract data from commercial
off-the-shelf software or from older, poorly documented,
legacy systems typically capture and transform just the
changes made since the last update.
- An information warehouse is a read-only
source of enterprise information, therefore developers
need not be concerned unduly with controlling create,
update and delete capabilities. However, developers will
need to address the trade off between protecting a
valuable corporate asset against unauthorized access and
making the data accessible to anyone within the
enterprise who can put it to good use. The best solution Visible
has found is to allow everyone in the enterprise to have
access to the enterprise measure definitions and
derivations, but only allow access to the underlying
detailed data only on an approved, need-to-know basis.
In addition to access security, an enterprise
must be concerned with physical security for its
information warehouse. Because its contents are an
extremely valuable corporate resource, they must be
protected against loss and damage. This protection is
available in many forms ranging from simple backup and
off-site storage strategies to installation of no-break
power and redundant disk storage and computer systems.
- Information warehouse users get useful
information from the information warehouse through user
interfaces. It is these user interfaces that have the
most impact on how effective and useful the information
warehouse will be perceived by its users. Two criteria
for selecting an effective user interface are ease of use
and performance. For ease of use, most enterprises turn
to graphical user interfaces. For performance, developers
must ensure that the hardware/software platform fully
supports and is optimized for every chosen user
interface.
The most important
selection criteria for user interfaces are the
information needs and the level of computer literacy of
potential users. A general rule is that users of highly
summarized data need simple, extremely graphical
interfaces, and detail data users need more complex, but
less graphical tools.
The final user interface criterion is
that it supports the access metadata designed for the
information warehouse. If a user interface is easy to
use, allows all potential users to get the information
they need in the format they need, and does it in an
acceptable amount of time, it is the right interface.
- Information warehouse implementation
includes loading the preliminary data, implementing
transformation programs, designing a user interface
"look and feel," developing standard queries
and reports, and thoroughly training information
warehouse users.
Back to Contents.
Although "information warehouse" is a
relatively new term, many Executive Information Systems (EIS),
Decision Support Systems (DSS) and Management Information Systems
(MIS) were developed by Visible and its clients using the
underlying concepts described in this paper long before the term
existed. Visible perfected its approach to information
warehouse development through years of experience.
The Visible Enterprise Engineering
methodology and tools are uniquely suited to support development
of an enterprise information warehouse. Visible Advantage is
the only integrated CASE tool that allows enterprise needs and
measures to be linked directly to the information warehouse data
model, data dictionary, integration and transformation process
models, and the information warehouse database design in a single
relational architecture.
Just as a powerful word processing system is
not very useful to someone who does not write, Visible
Advantage is not useful without appropriate knowledge and
skills. The skills include the acquisition, interpretation and
representation of all the details that go together to make up a
model of an enterprise and transform the model into an
information warehouse architecture. Visible helps clients
gain the necessary expertise to use Visible Advantage for
effectively information warehouse development through
facilitation, training, education and consulting. Visible
prides itself on its ability to transfer the skills and knowledge
that allow clients to gain mastery of Visible’s
methodology and tools. Generally, this requires three complete
development cycles of defining and modeling enterprise needs,
designing the information warehouse architecture, applying
appropriate technology, and implementing a data mart.
- During the first cycle, one or more Visible
consultants help the enterprise complete a prototype data
mart for the information warehouse while training
enterprise personnel on an information warehouse
"Action Team." Visible consultants are
actively involved in every aspect of this initial
development cycle. During this and every phase of
information warehouse development, enterprise analysts,
developers, and information warehouse users receive
appropriate training to ensure they have the skills and
knowledge to participate effectively. This
"just-in-time" training is a hallmark of Visible.
- In the second cycle, Visible
consultants closely monitor and coach enterprise
personnel as the Action Team completes the next
information warehouse element.
- Finally, internal enterprise personnel
perform a complete information warehouse development
cycle with minimal assistance from Visible,
typically in the form of progress and quality assurance
reviews. By the end of this third cycle, enterprise
personnel are fully capable of developing information
warehouse components on their own.
The enterprise is ultimately responsible for
ensuring that their information warehouse is developed,
implemented and becomes an enterprise asset. We provide the
guidance and expertise that allows the enterprise to develop a
superior information warehouse effectively and efficiently.
Visible's approach to information
warehouse development results in a useable, effective information
management tool that exactly meets the needs of an enterprise,
business or government, large or small. More information about
our methodology and tools can be found in the Visible Solution,
"Enterprise Engineering."
Information is a valuable resource. A
well-defined information warehouse, properly implemented, can be
a valuable tool for managing and using that resource. It
translates the vast volumes of detailed, unorganized data an
enterprise captures via its operational systems into useful
feedback, predictors, and warnings that help information
warehouse users at every organizational level make informed
decisions.
Back to Contents.
For more information concerning this Visible
Solution, please contact:
North
America
Visible Systems Corporation
201 Spring Street Lexington MA 02421 USA
Phone: +1-781-778-0200 · Fax +1-781-778-0208
Web Site: http://www.visible.com
Email: mcesino@visible.com
|
Asia-Pacific
Clive Finkelstein,
Managing Director
Information Engineering Services Pty Ltd
PO Box 246, Hillarys Perth WA 6923 Australia
Phone: +61-8-9402-8300 Fax: +61-8-9402-8322
Web Site: http://www.ies.aust.com/
Email: cfink@ies.aust.com
|
|
