|
|
<- Back |
|
|
|
Data Warehousing |
|
|
|
We offer Data Warehousing solutions for enterprises, to help them position themselves
better, to achieve market success. By integrating applications or parts of the business,
as a centralized repository of historical data, these solutions will further assist
in decision-making activities like Online Analytical Processing (OLAP), Executive
Information Systems (EIS), and Data Mining (DM). |
|
|
|
What is a Data Warehouse? |
|
|
|
A single, complete and consistent store of data obtained from a variety of different
sources made available to end users in a way they can understand and use in a business
context. |
|
|
|
A data warehouse is a collection of data that is used primarily in organizational
decision making: |
|
|
|
- Technique for assembling and managing data from various sources for the purpose of answering business questions. Thus making decisions that were not previous possible
- A decision support database maintained separately from the organization’s operational
|
|
|
|
What is Data Warehousing? |
|
|
|
A process of transforming data into information and making it available to users
in a timely enough manner to make a difference. |
|
|
|
- Subject Oriented: Data that gives information about a particular subject instead of about a company's ongoing operations.
- Integrated: Data that is gathered into the data warehouse from a variety of sources and merged into a coherent whole.
- Time-varying: All data in the data warehouse is identified with a particular time period.
- Non-volatile: Data is stable in a data warehouse. More data is added but data is never removed. This enables management to gain a consistent picture of the business.
|
|
|
|
|
|
|
|
 |
|
|
|
Data warehouse Architecture |
|
|
|
Architecture, in the context of an organization's data warehousing efforts, is a
conceptualization of how the data warehouse is built. There is no right or wrong
architecture. The worthiness of the architecture can be judged in how the conceptualization
aids in the building, maintenance, and usage of the data warehouse. |
|
|
|
 |
|
|
|
One possible simple conceptualization of a data warehouse architecture consists
of the following interconnected layers: |
|
|
|
Operational database layer |
|
|
|
The source data for the data warehouse - An organization's ERP systems fall into
this layer. |
|
|
|
Data access layer |
|
|
|
The interface between the operational and informational access layer - Tools to
extract, transform, load data into the warehouse fall into this layer. |
|
|
|
Metadata layer
|
|
|
|
The data directory - This is usually more detailed than an operational system data
directory. There are dictionaries for the entire warehouse and sometimes dictionaries
for the data that can be accessed by a particular reporting and analysis tool.
|
|
|
|
Informational access layer
|
|
|
|
The data accessed for reporting and analyzing and the tools for reporting and analyzing
data - Business intelligence tools fall into this layer.
|
|
|
|
Benefits of data warehousing |
|
|
|
Some of the benefits that a data warehouse provides are as follows: |
|
|
|
- A data warehouse provides a common data model for all data of interest regardless of the data's source. This makes it easier to report and analyze information than it would be if multiple data models were used to retrieve information such as sales invoices, order receipts, general ledger charges, etc.
- Prior to loading data into the data warehouse, inconsistencies are identified and resolved. This greatly simplifies reporting and analysis.
- Information in the data warehouse is under the control of data warehouse users so that, even if the source system data is purged over time, the information in the warehouse can be stored safely for extended periods of time.
- Because they are separate from operational systems, data warehouses provide retrieval of data without slowing down operational systems.
- Data warehouses can work in conjunction with and, hence, enhance the value of operational business applications, notably customer relationship management (CRM) systems.
- Data warehouses facilitate decision support system applications such as trend reports (e.g., the items with the most sales in a particular area within the last two years), exception reports, and reports that show actual performance versus goals.
|
|
|
|
The Purposes of a Data Warehouse |
|
|
|
- Provide end users with access to data
|
|
|
|
- Provide one version of the Truth: The data are consistent and quality assured
before being released to business users
|
|
|
|
- To record the past accurately
|
|
|
|
- To slice and dice through Data
|
|
|
|
- Canned Reports
- Ad Hoc Reports
- OLAP views
|
|
|
|
- To separate analytical and operational processing
|
|
|
|
- To support the reengineering of decisional processes
|
|
|
|
12 Rules of a Data Warehouse |
|
|
|
- Data Warehouse and Operational Environments are Separated
- Data is integrated
- Contains historical data over a long period of time
- Data is a snapshot data captured at a given point in time
- Data is subject-oriented
- Mainly read-only with periodic batch updates
- Development Life Cycle has a data driven approach versus the traditional process-driven approach
- Data contains several levels of detail (Current, Old, Lightly Summarized, Highly Summarized)
- Environment is characterized by Read-only transactions to very large data sets
- System that traces data sources, transformations, and storage
- Metadata is a critical component (Source, transformation, integration, storage, relationships, history, etc)
- Contains a chargeback mechanism for resource usage that enforces optimal use of data by end users
|
|
|
|
|
|
|
|
|
|
|
|
<- Back |
|
|
|
|
|