Benefits of Data Warehouse

1. Benefits of data warehouse

• Enhances data quality and consistency - Insights will be gained through improved information access.  Managers and executives will be freed from making their decisions based on limited data and their own "gut feelings".  Decisions that affect the strategy and operations of organizations will be based upon credible facts and will be backed up with evidence and actual organizational data.
• Saves time - Since business users can quickly access critical data from a number of sources, all in one place, they can rapidly make informed decisions on key initiatives. They won't waste precious time retrieving data from multiple sources.
• Generates a high ROI - Companies that have implemented data warehouses and complementary BI systems have generated more revenue and saved more money than companies that haven't invested in BI systems and data warehouses. According to a 2002 International Data Corporation (IDC) study "The Financial Impact of Business Analytics", analytics projects have been achieving a substantial impact on a business' financial status.
• Increased query and system performance - Data warehouses are purposely designed and constructed with a focus on speed of data retrieval and analysis.  Moreover, a data warehouse is designed for storing large volumes of data and being able to rapidly query the data.

2. Criteria for selecting DW vendors

The criteria for selecting a DW vendor is important because enterprises that invest more in BI have higher growth." Improving, upgrading and replacing enterprise decision support software can help managers make decisions that lead to a more successful organization. Providing great BI/DW capabilities related to customers and products or getting the same quality of information faster than competitors creates an advantage.

Here are some criteria that is important for selecting DW vendors

1. Does the vendor solution have easy to use tools for retrieving data?
2. Is vendor solution easy to administer and have good ETL, security and maintenance tools?
3. What is the estimated total cost of ownership?
4. What is the reputation of the vendor for service and support?
5. Are benchmarks satisfactory for anticipated requirements?
6. Is the DW product a good fit with existing systems and needs?
7. Is it likely the proposed solution will be accepted?
8. Does the proposed solution have additional benefits that are valued?

3. Bottom-up data warehouse uses enterprise data model?

Bottom-up data warehouse is an approach to data warehouse that uses data marts to provide reporting and analytical capabilities for specific business processes. The data mart then can be integrated into a comprehensive data warehouse. So the bottom-up approach doesn't use the enterprise data model.

4. Similarities and difference between inmon and Kimball DW development approaches

Ralph Kimball has created an approach to data warehouse known as the bottom-up approach which stated above. Bill Inmon also created an approach known as top-down approach which is designed using a normalized enterprise data model.

Both Inmon and Kimball share the opinion that stand-alone or independent data marts or data warehouses do not satisfy the needs for accurate and timely data and ease of access for users on an enterprise or corporate scale.

Here are some of the difference and similarities.

 

5. Different types of data warehouse architectures

-Basic architecture:

End users directly access data derived from several source systems through the data warehouse. This involves:

• Data Sources (operational systems and files)

• Warehouse (metadata, summary data, and raw data)

• Users (analysis, reporting, and mining)

-Architecture with staging area:

Where the data is cleaned and processed before it is placed in the data warehouse. The staging area is where the data is processed. This involves:

• Data Sources (operational systems and files)

• Staging Area (where data sources go before the warehouse)

• Warehouse (metadata, summary data, and raw data)

• Users (analysis, reporting, and mining)

-Architecture with data marts

This is the architecture where the data is processed and cleaned, then by adding data marts the organization will be able to analyze historical data (taken example from purchasing and sales). This involves:

• Data Sources (operational systems and flat files)

• Staging Area (where data sources go before the warehouse)

• Warehouse (metadata, summary data, and raw data)

• Data Marts (purchasing, sales, and inventory)

• Users (analysis, reporting, and mining)