Agricultural research is getting more and more data driven and data intensive. Data generation technologies are continuously evolving, on one end high-throughput genotyping, phenotyping, GIS/RS platforms are generating petabytes of data and on the other, socio-economic scientists are producing hundreds of thousands of data points over years with temporal and spatial stamping. ICRISAT recognized the importance of data science and started to implement and provide various data solutions to facilitate researchers in making data driven research decisions. In this line Breeding Management System (BMS) of Integrated Breeding Platform (IBP) for field experiment data, Genomic Open-source Breeding Informatics initiative (GOBii) for high-throughput genotypic data, CMAP for genetic maps, GBrowse for Genomes, VDSA for part of socio-economic data and various other platforms are implemented. However, for GIS/RS, Weather, Soil and range of socio-economic data we are still using flat files and using dataverse platform as a solution. Availability of information in queryable databases from multiple disciplines has provided an opportunity for scientists to develop research questions based on cross analyses, query and visualization. The major challenge is that there does not exist any such comprehensive platform to assist in decision making. Developing tools to do that are a major big data analytic challenge including ontologies standardization and protocols for data storage, retrieval, analytics, and visualization and also sharing with other researchers and collaborators. At ICRISAT we started to modernize our data management infrastructure by developing and adopting BrAPI compliant tools and databases and also strengthening international collaboration to develop and deploy big data analytic platforms.