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Operational Data Store

Need to create an operational data store for an information management project and want an overview of key design considerations?

What is a data warehouse?

A data warehouse is the foundation for information management andOperational Data Store involves:
  • Extracting data from operational systems;
  • Moving it into data warehouse structures;
  • Reorganizing and structuring the data for analysis purposes; and
  • Moving it into reporting structures called data marts.
What is an operational data store (ODS)?

An operational data store is a collection of operational data which is used to support operational monitoring and management. It involves:
  • Extracting data from operational systems;
  • Moving it into ODS structures; and
  • Reorganizing and structuring the data for analysis purposes
How is an ODS different from a data warehouse?

A data warehouse is intended to support strategic planning and business intelligence decision support. It should contain:
  • Integrated subject oriented data, e.g sales data;
  • Static data, e.g. data that is moved into data warehouses should not change after it is stored in the data warehouse environment;
  • Historical data, e.g. data warehouses will usually contain several years worth of historical data; and
  • Aggregated, or summarized data e.g. as data becomes "older", it is summarized to reduce data storage requirements and to improve analysis performance.
An operational data store is intended to support operational management and monitoring and should contain:
  • Integrated subject oriented data (similar to data warehouses) e.g sales data;
  • Volatile data, e.g. data that is moved into an ODS will probably change frequently;
  • Current data, e.g. an ODS will usually contain several weeks or even months worth of data instead of large volumes of historical data; and
  • Detailed  data e.g. as data becomes "older", it is summarized to reduce data storage requirements and to improve analysis performance.
ODS data is refreshed frequently to provide a "snap-shot" view of the on-line transaction processing (OLTP) systems and legacy systems.

Who uses an ODS?

An ODS is frequently used by customer support staff to access integrated data to respond to customer inquiries.

What are ODS design considerations?

An ODS should be based on an normalized relational database design, similar to a data warehouse design, usually with no data aggregation structures.

What are ODS data load considerations?

Typical methods of loading an ODS include:
  • Extract, transform and load (ETL) software e.g. Informatica, Ab Initio, Ascential DataStage is used to collect source data and populate ODS structures;
  • Materialized views, which create a snap-shot of source tables and can load custom designed views for reporting purposes; and
  • Data replication software e.g. Oracle Data Guard can be used to populate ODS structures that are identical to the source system or they can even be used to create, and populate logical, or more normalized, structures.
Requirements analysis considerations?

Do not build an ODS in hopes that some-one will want to use it!

ODS requirements should be based on approved data requirements and a logical data model.

Summary…

A data warehouse contains a large volume of historical, static, data to support business intelligence decision making.

An operational data store contains a smaller volume of current, volatile data, to support operational management and monitoring.


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