Science with a Human Face

ICRISAT leverages the power of SAS for development, food security, poverty alleviation and environmental protection

Objective
With an underlying objective to help address and resolve real human needs, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), through effective technologies, policies, infrastructure development and most importantly through research, has been addressing and resolving real human needs like reducing poverty, hunger and environmental degradation.

ICRISAT's goal is to harness the power of technology for development, food security, poverty alleviation and environment protection.

In the past 27 years, ICRISAT has generated a lot of important technologies, and this comparative advantage has been instrumental in improving agricultural productivity and reducing poverty, while working with the national agricultural research systems (NARS) and other partner institutions like NGOs and the private sectors.

With strong focus on the socio-economic research, it was imperative for ICRISAT to deploy software that could provide extensive statistical capabilities with tools for both specialized and enterprise-wide analytical needs, ensuring analytically & statistically accurate research data.

The Statistical Analysis Challenge 
To empower a research analyst to plan the research studies with scientifically and statistically valid designs, and, to arrive at valid and meaningful conclusions by applying appropriate statistical methods, it was essential that the person undertaking the research work was reasonably acquainted with the potentialities of applications of socio-economic research techniques, and that the same was statistically and analytically accurate.

With large data sets used for multivariate analysis at ICRISAT, there was need of a software that could perform cluster analysis using large data sets and provide a complete, comprehensive and integrated platform for data analysis. There was also a distinct need of a solution that has ready-to-use procedures to handle a wide range of statistical analyses, including analysis of variance, regression, and multivariate analysis.

Why SAS?
ICRISAT chose SAS to address its requirement for powerful and sophisticated statistical techniques for multivariate analysis of large data sets. Also being multi-tasked and user-friendly tool with strong econometric analysis capabilities, SAS was the obvious choice to offer a complete, comprehensive and integrated platform for socio-economic data analysis.

Explaining the rationale behind the choice of SAS for socio-economic research at ICRISAT, Dr. Cynthia Bantilan, Global Theme Leader, SAT Futures and Development Pathways, ICRISAT said, “SAS is multi-tasked & user-friendly tool with strong econometric analysis capabilities. Capable of handling huge volumes of data comfortably, SAS facilitates analysis from multiple angles with features including simple descriptive analysis to representation in map form.”

Highlighting the interoperability and strong statistical capabilities of SAS, she added, “SAS provides a complete, comprehensive and integrated platform for data analysis. SAS' extensive statistical capabilities can meet the needs of our organization. With SAS, we can easily access data from any source like importing data from Excel, perform data management, carry out statistical analysis and then present our findings in a variety of reports and graphs.”

Key Benefits
The SAS/STAT software, an integral component of the SAS analytic platform, provides extensive statistical capabilities with tools for both specialized and enterprise-wide analytical needs. With ready-to-use procedures, SAS has simplified the research work at ICRISAT and empowered the research community to perform wide range of statistical analyses.

According to Dr. Bantilan, "SAS has been very effective in analysis of data emanating from socio-economic research studies."

Interoperability of SAS has helped develop a complete, comprehensive and integrated platform for data analysis which has offered numerical and statistical accuracy in specialized and enterprise-wide analysis.

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