MIS

1. Data Warehouse
Done by : Safaa Taamsah
Alzaytonah University
201527036

2. Definition
•In computing, a data warehouse (DW or DWH),
also known as an enterprise data
warehouse (EDW), is a system used
for reporting and data analysis, and is considered a
core component of business intelligence. DWs are
central repositories of integrated data from one or
more disparate sources. They store current and
historical data in one single place that are used for
creating analytical reports for workers throughout the
enterprise.

3. The Concept of Data Warehouse
• The concept of data warehousing dates back to the late 1980s
• when IBM researchers Barry Devlin and Paul Murphy developed
the "business data warehouse". In essence, the data warehousing
concept was intended to provide an architectural model for the
flow of data from operational systems to decision support
system. The concept attempted to address the various problems
associated with this flow, mainly the high costs associated with it.

4. The Concept of Data Warehouse
• A data warehouse is a relational database that is
designed for query and analysis rather than for
transaction processing. It usually contains historical data
derived from transaction data, but it can include data
from other sources. It separates analysis workload from
transaction workload and enables an organization to
consolidate data from several sources.
• In addition to a relational database, a data warehouse
environment includes an extraction, transportation,
transformation, and loading (ETL) solution, an online
analytical processing (OLAP) engine, client analysis
tools, and other applications that manage the process of
gathering data and delivering it to business users.

5. Characteristics of a data warehouse
A common way of introducing data warehousing is to
refer to the characteristics of a data warehouse as set
forth by :
Subject Oriented
Integrated
Nonvolatile
Time Variant

6. • Subject Oriented
• Data warehouses are designed to help you analyze data. For example, to learn more
about your company's sales data, you can build a warehouse that concentrates on sales.
Using this warehouse, you can answer questions like "Who was our best customer for
this item last year?" This ability to define a data warehouse by subject matter, sales in
this case, makes the data warehouse subject oriented.
• Integrated
• Integration is closely related to subject orientation. Data warehouses must put data
from disparate sources into a consistent format. They must resolve such problems as
naming conflicts and inconsistencies among units of measure. When they achieve this,
they are said to be integrated.
• Nonvolatile
• Nonvolatile means that, once entered into the warehouse, data should not change. This
is logical because the purpose of a warehouse is to enable you to analyze what has
occurred.
• Time Variant
• In order to discover trends in business, analysts need large amounts of data. A data
warehouse's focus on change over time is what is meant by the term time variant.

7. Data Mart
• A data mart is the access layer of the data warehouse environment that is
used to get data out to the users. The data mart is a subset of the data
warehouse and is usually oriented to a specific business line or team.
• A data mart is basically a condensed and more focused version of a data
warehouse that reflects the regulations and process specifications of each
business unit within an organization. Each data mart is dedicated to a specific
business function or region. This subset of data may span across many or all
of an enterprise’s functional subject areas. It is common for multiple data
marts to be used in order to serve the needs of each individual business unit
(different data marts can be used to obtain specific information for various
enterprise departments, such as accounting, marketing, sales, etc.)

8. Types of Data Stored in a Data
Warehouse
• Historical Data
A data warehouse typically contains several years of historical data.
The amount of data that you decide to make available depends on
available disk space and the types of analysis that you want to
support. This data can come from your transactional database
archives or other sources.

9. • Metadata is "data [information] that provides information about
other data". Three distinct types of metadata exist: descriptive
metadata, structural metadata, and administrative metadata.
Descriptive metadata describes a resource for purposes such as
discovery and identification. It can include elements such as title,
abstract, author, and keywords.
Structural metadata is metadata about containers of data and
indicates how compound objects are put together, for example,
how pages are ordered to form chapters. It describes the types,
versions, relationships and other characteristics of digital
materials.
Administrative metadata provides information to help manage a
resource, such as when and how it was created, file type and
other technical information, and who can access it.

10. • Derived data
A derived data element is a data element derived from other data
elements using a mathematical, logical, or other type of
transformation, e.g. arithmetic formula, composition, aggregation
• Raw data
Also known as primary data, is data (e.g., numbers, instrument
readings, figures, etc.) collected from a source.

12. Business Intelligence and Data Warehousing
 One ultimate use of the data gathered and processed in the data life cycle is
for business intelligence.
 Business intelligence generally involves the creation or use of a data
warehouse and/or data mart for storage of data, and the use of front-end
analytical tools such as Oracle’s Sales Analyzer and Financial Analyzer or
Micro Strategy’s Web.
 Such tools can be employed by end users to access data, ask queries, request
ad hoc (special) reports, examine scenarios, create CRM activities, devise
pricing strategies, and much more.
 More advanced applications of business intelligence include outputs such as:
• financial modeling
• budgeting
• resource allocation
• and competitive intelligence.

13. Data Warehouse Applications
oRetail Industry: Forecasting, Market research,
Merchandising etc.
oManufacturing and distribution : Sales history/trends,
Market demand projects etc.
oBanks : Spot market trends, Marketing, Credit cards etc.
oInsurance Companies : Property and casualty fraud etc.
oHealth Care Providers : Fraud detection, Patient
matching etc.

14. Data Warehouse Applications
o Government Agencies : Auditing tax records, information sharing
across different agencies etc.
o Internet Companies : Analyzing shopping behavior, CRM etc.
o Telecommunications : Telemarketing, Product development etc.
o Sports : Analyzing strategies, Winning player combinations etc.

15. Data Warehouse Sizes
Terabyte (10^12) - Walmart (24 TB)
Petabyte (10^15) - Geographic Information Systems
Exabyte (10^18) - National Medical Association
Zettabyte (10^21) - Weather Images
Zottabyte (10^24) - Intelligence Agency (Video)