OBIEE Datawarehouse Design Process Iterations

OBIEE Data-warehousing Design Process Inception Phase

Designing the Data Warehouse effort:

The Best practice iterations to design a Data warehouse can be as follows

1. Configuring an Oracle or DB2 or MS Sql Server database for use as a data warehouse

2. Designing the Warehouse if the Warehouse supports more than one LOB the Data Modeler can come up with a Physical warehouse design based on Conformed Bus Architecture with conformed dimensions & Conformed facts which allow the IT to reuse the Data sets across the Business.

3. Managing Schema objects such as Tables, Concurrent programs, Sequences, Schema, Disks, Views, Indexes & Materialized Views

4. Managing & Maintaining the User authentication its a DBA responsibility

5. Developing routines, Streams, ETL Process using an ETL tool or a Push Pull concurrent program code to Leverage operational data resemblance

6. Managing change data capture using tools like CA -7 or Siebel DAC & more

7. Creating Metadata repository, BMM Layer Meta data repository, User Authentication & Authorization Strategy through OBIEE or LDAP or Data base authentication.

8. Backing up the data base & Performing recovery & leveraging fault tolerance

9. Monitoring data-warehouse’s performance using OBIEE tool caching & performance tuning strategies

OBIEE Data-warehousing Design Process Inception Phase

Designing the Data Warehouse effort:

The Best practice iterations to design a Data warehouse can be as follows

1. Configuring an Oracle or DB2 or MS Sql Server database for use as a data warehouse

2. Designing the Warehouse if the Warehouse supports more than one LOB the Data Modeler can come up with a Physical warehouse design based on Conformed Bus Architecture with conformed dimensions & Conformed facts which allow the IT to reuse the Data sets across the Business.

3. Managing Schema objects such as Tables, Concurrent programs, Sequences, Schemas, Disks, Views, Indexes & Materialized Views

4. Managing & Maintaining the User authentication its a DBA responsibility

5. Developing routines, Streams, ETL Process using an ETL tool or a Push Pull concurrent program code to Leverage operational data resemblance

6. Managing change data capture using tools like CA -7 or Siebel DAC & more

7. Creating Metadata repository, BMM Layer Meta data repository, User Authentication & Authorization Strategy through OBIEE or LDAP or Data base authentication.

8. Backing up the data base & Performing recovery & leveraging fault tolerance

9. Monitoring data-warehouse’s performance using OBIEE tool caching & performance tuning strategies

Datawarehouse Design, Best Practices & Pragmatic approach

Datawarehouse Design, A Tool Independent Pragmatic Approach

I always asked myself during my consulting career what would be the best steps to Design a  Datawarehouse  & after musing for a while I came up with this couple a years ago which I had in my personal diary which am going to digitize today.

I always ask myself what can BA’s & Power users gain from a data warehouse ?

• Firstly it can enhance business productivity as we can see the organization’s information quickly & efficiently using a unified repository tool.
• It may provide us information to win over competitors by adjusting our prices & improving our performance.
• The data warehouse improves the CRM because it consistently provides business information across all business group hierarchies, departments & domains to make informed decisions possible.

Implementing a decision support system is like building a magnificent skyscraper which requires strategies of an Architect, Owner & Implementer / contractor.

Anonymous

To build an efficient data warehouse following 4 strategic perception should be combined together

• Datawarehouse Strategy It’s a process of identifying the fact tables, Dimension tables & ETL push-pull strategy, It represents the aggregates to be calculated during the ETL, database indexing strategy, database partition, DW refresh strategy & more.
• The Power User Strategy This is a view from the eyes of the BA, Power users & Customers on what information should be included in the warehouse for the current & future business requirements
• Sourcing the Data This is a strategic view of the Data Modeler he decides on how to gather & model te silo’s of data using specific data modeling techniques & tools like OBIEE, Cognos & Microstrategy
• The End User Perspective is to view & relate the data to business query

So the Datawarehouse design can be attained by a top down approach a bottom up approach or a combo,].

In the Top down approach the technology or DW tool is mature in the market and this approach starts with an over – all  Inception analysis & planning phase where problem definition is apt.

In the Bottom up approach starts out with POC’s & experiments

In the Combo approach we can use the strategic planning of the top down approach with the rapid implementation technique of bottom up to increase ROI.

Datawarehouse Design Process:

• The first decision is to choose a Subject Area or a business process to model, This Subject area is a functionally related Dimensions & Facts together examples are Orders, Invoices, Shipments, Inventory, LOB Performance & more.
• The second decision is to choose a Fact Table Granularity because te DW front end testing is done in at the lowest granular level for example transactional details of a Customer on a monthly snpshot or a quarterly snapshot.
• The third step is to Choose functionally related Dimensions that would eventually become a part of the  Fact.
• Te fourth Step is to Choose the Meters & Metrics, A meter can be defined as a collection of functionally dependent metrics.

Implementation Steps for a DW / DSS:

• Inception Analysis & Problem Definition.
• Requirements Study.
• DSS / Warehouse Design which comprises of Data Modeling, Database Modeling, Meta Data Repository Modeling, ETL Strategy & more.
• Integrating the Data & Regression Testing.
• Deployment, Training & Maintenance.

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Oracle Business Intelligence Application Development Lifecycle

Business Intelligence Software Development Lifecycle