Data Warehouse Buzz words.

Data Warehouse

A database designed to support decision making in an organization. Data from the production databases are copied to the data warehouse so that queries can be performed without disturbing the performance or the stability of the production systems. Data Marts Data warehouses can become enormous with hundreds of gigabytes of transactions. As a result, subsets, known as "data marts," are often created for just one department or product line. Updated at the End of a Period Data warehouses are generally batch updated at the end of the day, week or some period. Its contents are typically historical and static and may also contain numerous summaries. Operational Data Stores The data warehouse is structured to support a variety of analyses, including elaborate queries on large amounts of data that can require extensive searching. When databases are set up for queries on daily transactions, they are often called "operational data stores" rather than data warehouses (see ODS). See OLAP, DSS, EIS and BI software.

What is a data stewardship program?

Data are important assets of an organization. An organization should proactively manage, protect and increase its data assets. A data stewardship program is to establish an enterprise data environment. It promotes the data usage and integration across the enterprise. It defines the process and policy to ensure the data are correctly used and shared without putting the enterprise into risks. It provides the oversights, tools and trainings to support the individual organizations within an enterprise.

What are the key Area covered by a data stewardship program?

A data stewardship program should cover the following area:

• Data Management
  
• Data Integration
• Data Quality
• Metadata Standard
• Data Flow & Model standard
  
• Data Policy
  
• Data Security
• Access
  
• Usage
• Data Privacy
• Auditing
• Reporting

Who should define the data stewardship program?

A data stewardship program can be defined by a data stewardship committee or a council, which consist of members from IT, data source owners, and data user community. However, its execution should belong to a group that has several data stewards.

What is Master Data Management?

Master Data Management is a combination of business processes, software application, and technologies which helps you to manage your master data, such as "Customer", "Supplier", "Employee", and "Product".

Master data management ensures your data quality so you can rely on the data to do your business. Master data may be sourced and maintained in multiple systems. By deploying the master data management, you can enable the cooperation among the diverse systems and bring the consistency across these systems.

Here are the four key components of a MDM solution:

Master Data: A central data model that store the master data

The data model should be best practice based, comprehensive, flexible, configurable, extensible. It should adopt the open standards as much as possible and can be easily integrated with different sources.

Integration Services: Provide different way to access, update, and synchronize the data

The integration service should include public APIs, web services, bulk load, User Interface, UI widgets, event publishing, etc. The integration should serve both needs of the initial load and on-going maintenance.

Data Quality Services: Processes and Utilities that can help to ensure the data quality

Some data searvices should be provided: Data cleansing, duplicate identification, duplicate avoidance, data match and merge, data enrishment, and data certification.

Privacy and Compliance Services: This is getting important. By deploying a MDM process and solution, you can ensure that your organization follows the priacy policy and governance of data collection and usage. You can designate the owner of the data and audit the data creation, update, reference, and view.