The core objective of this project is to explore a new paradigm for harnessing livestock data culture for overall development, thus awakening the institutional Intelligence via state of the art Data staging and Analytics tools.

The Department of Animal sciences Data Warehouse Laboratory (OAU-ANS-DW), one of the legacy project by Prof. E.B Sonaiya as donated, is a unit that oversees digital systems that securely facilitates data capture, data compilation, organization and analysis of operational databases, historical data, research data and other data concerns in all aspects of animal production and value chains, as a key strategy for value creation and product development.

The OAU-ANS-DW Data project addresses a new direction in training and capacity building in livestock data analytics and intelligence for staff and students in the department. The emphasis on “Data Culture and Value Creation Culture” echoes trends in the application of data intelligence in knowledge-driven economies globally.

1. The Data warehouse provides consistent information on various cross-functional activities within the department. It supports ad-hoc reporting and query. The DataWarehouse helps to integrate many sources of data for academic, research and administrative purposes.
2. The Data warehouse significantly reduces total turnaround time for information retrieval, analysis and reporting as against traditional stop gap approaches. It allows for restructuring and Integration of data that make it easier for researchers and administrators to intelligently use data for reporting, analysis and decision making.
3. The Data warehouse allows all so privileged users to access critical data from the number of sources in a single place. Therefore, it will save user’s time of retrieving data from multiple sources. For example, results, transcripts and other critical academic records usually consistently in high demand may now be centrally accessed even at the departmental level.
4. The Data warehouse will accumulate and store redundant historical departmental data. This helps to analyze data patterns associated with different time periods and trends for predictive analytics.
5. The Data warehouse shall provide a solid foundation for further planned capacities in the adoption of Machine learning, Deep Learning and Internet of Things (IoT) for livestock value chain optimization and business intelligence for better livestock yields in preparation towards an AI first future.