DSS News
                     D. J. Power, Editor
            March 02, 2003 -- Vol. 4, No. 5
         A Bi-Weekly Publication of DSSResources.COM


 AMCIS 2003 Call for Papers.  Submissions due March 17, 2003.



 * Ask Dan! - What do I need to know about Data Warehousing/OLAP?
 * What's New at DSSResources.COM
 * DSS News Releases


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Ask Dan!
by Daniel J. Power

What do I need to know about Data Warehousing/OLAP?

The answer to this question depends upon who is asking. Managers need to 
be familiar with some DW/OLAP terminology (the basic what questions) and 
they need to have an idea of the benefits and limitations of these 
decision support components (the why questions). More technical people 
in Information Systems need to know how and when to develop systems 
using these components. This short DW/OLAP FAQ consolidates answers from 
some recent email questions and from a number of questions previously 
answered at DSSResources.COM. The bias in this FAQ is definitely towards 
what managers need to know. Some more technical question related to 
DW/OLAP were answered in the Ask Dan! of February 17, 2002. That Ask 
Dan! answered the following questions: Is a Data Warehouse a DSS? What 
is a star schema? How does a snowflake schema differ from a star schema? 
Also, for people who want definitions for technical terms like derived 
data, hypercube, pivot and slice and dice the OLAP Council glossary 
(1995) is online at 

Q. What is a Data Warehouse?

A. A data warehouse is a database designed to support a broad range of 
decision tasks in a specific organization. It is usually batch updated 
and structured for rapid online queries and managerial summaries. Data 
warehouses contain large amounts of historical data. The term data 
warehousing is often used to describe the process of creating, managing 
and using a data warehouse.

Q. What is On-line Analytical Processing (OLAP)?

A. OLAP is software for manipulating multidimensional data from a 
variety of sources. The data is often stored in a data warehouse. OLAP 
software helps a user create queries, views, representations and 
reports. OLAP tools can provide a "front-end" for a data-driven DSS.

Q. What is the difference between data warehousing and OLAP?

A. The terms data warehousing and OLAP are often used interchangeably. 
As the definitions suggest, warehousing refers to the organization and 
storage of data from a variety of sources so that it can be analyzed and 
retrieved easily. OLAP deals with the software and the process of 
analyzing data, managing aggregations, and partitioning information into 
cubes for in-depth analysis, retrieval and visualization. Some vendors 
are replacing the term OLAP with the terms analytical software and 
business intelligence. 

Q. When should a company consider implementing a data warehouse? 

A. Data warehouses or a more focused database called a data mart should 
be considered when a significant number of potential users are 
requesting access to a large amount of related historical information 
for analysis and reporting purposes. So-called active or real-time data 
warehouses can provide advanced decision support capabilities.

Q. What data is stored in a data warehouse?

A. In general, organized data about business transactions and business 
operations is stored in a data warehouse. But, any data used to manage a 
business or any type of data that has value to a business should be 
evaluated for storage in the warehouse. Some static data may be compiled 
for initial loading into the warehouse. Any data that comes from 
mainframe, client/server, or web-based systems can then be periodically 
loaded into the warehouse. The idea behind a data warehouse is to 
capture and maintain useful data in a central location. Once data is 
organized, managers and analysts can use software tools like OLAP to 
link different types of data together and potentially turn that data 
into valuable information that can be used for a variety of business 
decision support needs, including analysis, discovery, reporting and 

Q. Database administrators (DBAs) have always said that having 
non-normalized or de-normalized data is bad. Why is de-normalized data 
now okay when it's used for Decision Support? 

A. Normalization of a relational database for transaction processing 
avoids processing anomalies and results in the most efficient use of 
database storage. A data warehouse for Decision Support is not intended 
to achieve these same goals. For Data-driven Decision Support, the main 
concern is to provide information to the user as fast as possible. 
Because of this, storing data in a de-normalized fashion, including 
storing redundant data and pre-summarizing data, provides the best 
retrieval results. Also, data warehouse data is usually static so 
anomolies will not occur from operations like add, delete and update 
of a record or field.

Q. How often should data be loaded into a data warehouse from 
transaction processing and other source systems?

A. It all depends on the needs of the users, how fast data changes and 
the volume of information that is to be loaded into the data warehouse. 
It is common to schedule daily, weekly or monthly dumps from operational 
data stores during periods of low activity (for example, at night or on 
weekends). The longer the gap between loads, the longer the processing 
times for the load when it does run. A technical IS/IT staffer should 
make some calculations and consult with potential users to develop a 
schedule to load new data.

Q. What are the benefits of data warehousing?

A. Some of the potential benefits of putting data into a data warehouse 
include: 1. improving turnaround time for data access and reporting;  2. 
standardizing data across the organization so there will be one view of 
the "truth"; 3. merging data from various source systems to create a 
more comprehensive information source;  4. lowering costs to create and 
distribute information and reports; 5. sharing data and allowing others 
to access and analyse the data; and 6. encouraging and improving 
fact-based decision making. 

Q. What are the limitations of data warehousing?

A. The major limitations associated with data warehousing are related to 
user expectations, lack of data and poor data quality. Building a data 
warehouse creates some unrealistic expectations that need to be managed. 
A data warehouse doesn't meet all decision support needs.  If needed 
data is not currently collected, transaction systems need to be altered 
to collect the data. If data quality is a problem, the problem should be 
corrected in the source system before the data warehouse is built. 
Software can provide only limited support for cleaning and transforming 
data. Missing and inaccurate data can not be "fixed" using software.  
Historical data can be collected manually, coded and "fixed", but at 
some point source systems need to provide quality data that can be 
loaded into the data warehouse without manual clerical intervention.

Q. How does my company get started with data warehousing?

A. Build one! The easiest way to get started with data warehousing is
to analyze some existing transaction processing systems and see what 
type of historical trends and comparisons might be interesting to 
examine to support decision making. See if there is a "real" user need 
for integrating the data. If there is, then IS/IT staff can develop a 
data model for a new schema and load it with some current data and start 
creating a decision support data store using a database management 
system (DBMS). Find some software for query and reporting and build a 
decision support interface that's easy to use. Although the initial data 
warehouse/data-driven DSS may seem to meet only limited needs, it is a 
"first step". Start small and build more sophisticated systems based 
upon experience and successes. 

     Check free DSS articles online in the journal 
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DSS News Releases - February 17 to February 28, 2003

02/28/2003 iStrategy announces HigherEd Analyzer (TM) data warehouse and 
analytical portal solution for colleges and universities. 

02/27/2003 Webplan provides Hana Microelectronics Group the power to
streamline their supply chain processes. 

02/27/2003 North America's newest medical school selects PDxMD(TM) as 
its point-of-care clinical decision support tool. 

02/26/2003 ADVIZOR Solutions(R) introduces ADVIZOR(TM) 3.0 data 
visualization software to understand and profit from key mission 
critical data. 

02/25/2003 Altova's XML Document Editor, AUTHENTIC 5, will be included 
with Software AG's Tamino Server at no additional cost.

02/24/2003 Polycom announces breakthrough new products and 
interoperability solutions for video, voice and web conferencing, and 

02/24/2003 Documentum delivers eRoom Enterprise; best-in-class 
collaboration technology fully integrated with leading enterprise 
content management platform. 

02/24/2003 DecisionPoint Applications announces new Financial Compliance 
Dashboard to support Sarbanes-Oxley reporting requirements. 

02/20/2003 Ascential Software announces findings from first customer 
advisory board session. 

02/19/2003 Stellent brings high level of content management efficiency 
to UK's largest central government organization. 

02/18/2003 Oracle and Nokia to bring collaboration capabilities to the 
enterprise mobile workforce. 

02/18/2003 Developers of GPS awarded the 2003 Draper prize. 

02/18/2003 AskMe unveils AskMe Enterprise 6.7; newest version of 
award-winning knowledge sharing software incorporates customer best 
practices to foster rapid deployment and adoption. 

02/17/2003 Internet2 Abilene backbone network upgrade passes 
transcontinental milestone; first phase of 10 gigabit per second network 
for research and education complete.

02/17/2003 MedWell Group announces general availability of FreedomSuite, 
the physician's safe path to amobile practice. 

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