Traditional Data Warehousing & Business Intelligence solutions has been designed and built to address the Strategic decision making in an enterprise among the C-grade Executives and key decision makers however in addition to the Strategic BI initiatives, the trend is among developing solutions with the Operational BI capability.
What is this new Buzz word - Operational Analytics?
Operational Analytics is primarily to provide decision making ability for the mid-level management staff and operational managers to manage and optimize the day-to-day business operations with appropriate information on the business events. For example, a store manager making a decision for on-the-spot promos/offers on Car related products such as Car Perfumes, Car cleaning materials based on the event of Car parking getting filled/occupied in the store parking lots.
It is important that the historical data and the on-going operational data needs to be combined to make the operational analytics more effective; this includes various product lookups, inventory status, past promo effectiveness, capturing of events and alerting of them, etc., Hence for any such realtime data warehousing; the data acquisition and data integration is a critical factor for the success of the initiative.
There are different types of Data acquisition for Real time data warehousing
Log based Change data capture approach is more towards a "Push" approach to deliver data from source to targets. In this approach, the changed data from the database transaction logs are captured which does not impact the performance on the source systems, which is not the case that of change data capture that uses database trigger or table scanning. Oracle Golden Gate uses log based, CDC capabilities to enable real-time data integration and management by capturing and delivering updates of critical information as the changes occur and providing continuous synchronized data across heterogeneous environments.
ELT (Extract, Load & Transform) has been a key factor for the real-time or operational data warehousing as the transformations tends to take place in the datawarehouse.
For Operational datawarehousing, a hybrid approach of Log-based CDC & ELT is being leveraged to consolidate data from the heterogeneous source systems into the appropriate data warehouse/datamarts.
The solution shall be designed in such a way it offers transactional, real-time data capture using the appropriate "Push" approach, i.e., as soon as a new database transaction is committed in the source system, the data is immediately captured via the database transaction logs and loaded to the datawarehouse/staging area of the datawarehouse. However, in this approach, the data transformation is expected to be minimal as much as possible, in fact, only basic row-level transformations are performed. For heavy transformation needs, the solution can be integrated with appropriate ETL/ELT components to enable end-to-end solution for data integration in the data warehouse/data mart.
For example - Oracle Golden Gate can be integrated with Oracle Data Integrator (ODI) for integrating the data from source systems in a real-time data warehouse to handle data load using log based CDC with minimal transformations and leveraging the ELT capabilities of ODI for heavy transformations.
Operational data warehousing & analytics allows the users to leverage the underlying historical data and real-time transactional data to access and respond to information in real time to improve business decisions and actions. Continuous low-latency data capture and delivery infrastructure is a critical success factor for the establishment and maintenance of such real-time data warehouse. It is becoming evident and getting proved that Organizations that leverage the most up-to-date BI in their day-to-day operations significantly improve their operational efficiency, reducing operational costs and thereby enhancing their productivity and the overall Gross Margins.
What is this new Buzz word - Operational Analytics?
Operational Analytics is primarily to provide decision making ability for the mid-level management staff and operational managers to manage and optimize the day-to-day business operations with appropriate information on the business events. For example, a store manager making a decision for on-the-spot promos/offers on Car related products such as Car Perfumes, Car cleaning materials based on the event of Car parking getting filled/occupied in the store parking lots.
It is important that the historical data and the on-going operational data needs to be combined to make the operational analytics more effective; this includes various product lookups, inventory status, past promo effectiveness, capturing of events and alerting of them, etc., Hence for any such realtime data warehousing; the data acquisition and data integration is a critical factor for the success of the initiative.
There are different types of Data acquisition for Real time data warehousing
- Batch oriented ETL/ELT (with near real-time)
- EAI
- Log-based Change Data capture approach
Log based Change data capture approach is more towards a "Push" approach to deliver data from source to targets. In this approach, the changed data from the database transaction logs are captured which does not impact the performance on the source systems, which is not the case that of change data capture that uses database trigger or table scanning. Oracle Golden Gate uses log based, CDC capabilities to enable real-time data integration and management by capturing and delivering updates of critical information as the changes occur and providing continuous synchronized data across heterogeneous environments.
ELT (Extract, Load & Transform) has been a key factor for the real-time or operational data warehousing as the transformations tends to take place in the datawarehouse.
For Operational datawarehousing, a hybrid approach of Log-based CDC & ELT is being leveraged to consolidate data from the heterogeneous source systems into the appropriate data warehouse/datamarts.
The solution shall be designed in such a way it offers transactional, real-time data capture using the appropriate "Push" approach, i.e., as soon as a new database transaction is committed in the source system, the data is immediately captured via the database transaction logs and loaded to the datawarehouse/staging area of the datawarehouse. However, in this approach, the data transformation is expected to be minimal as much as possible, in fact, only basic row-level transformations are performed. For heavy transformation needs, the solution can be integrated with appropriate ETL/ELT components to enable end-to-end solution for data integration in the data warehouse/data mart.
For example - Oracle Golden Gate can be integrated with Oracle Data Integrator (ODI) for integrating the data from source systems in a real-time data warehouse to handle data load using log based CDC with minimal transformations and leveraging the ELT capabilities of ODI for heavy transformations.
Operational data warehousing & analytics allows the users to leverage the underlying historical data and real-time transactional data to access and respond to information in real time to improve business decisions and actions. Continuous low-latency data capture and delivery infrastructure is a critical success factor for the establishment and maintenance of such real-time data warehouse. It is becoming evident and getting proved that Organizations that leverage the most up-to-date BI in their day-to-day operations significantly improve their operational efficiency, reducing operational costs and thereby enhancing their productivity and the overall Gross Margins.
