Data-Driven Decision Making in Indian Agriculture: the Present and the Future
by Udit Poddar (@poddarudit) on Saturday, 30 April 2016
View session in schedule
- Crisp talk
- Technical level
Data-driven decision making is critical in sectors like agriculture, health, and education where well-planned initiatives have the power to literally change lives. Lack of a consolidated platform with access to relevant data, however, hinders objectivity and efficiency in the decision making process for the decisions that matter most. In this session, we reveal how we integrated relevant data — previously divided into silos — to undertake targeted investment decisions in agriculture. We then explore what decision science means for the future of agriculture in India.
- Introduction to data-driven decision making in agriculture.
a. Overview of current decision-making process in agriculture sector. b. Discussion of the types of decisions that can be driven by data in the agriculture sector.
- The current state of agriculture data in India.
a. Overview of data that is openly available in agriculture sector. b. Importance of specific data sets for information about agriculture in India. c. Insights from Government MIS systems
- Challenges and solutions in consolidating data.
a. Overview of challenges faced in consolidating independent data sets into a master data set that can be used for generating insights. b. Explanation of the solutions developed by us to stitch different datasets together. c. Data curation problems with the current agricultural data in India
- Regularity of data and data gaps in the agricultural sector.
a. Overview of current data gaps in agricultural sector in India. b. Explanation of difference between input and output metrics in the context of agriculture. c. Argument for importance of tracking output metrics at the lowest administrative division.
- Introduction to precision farming
a. Overview of the concept of precision farming. b. Explanation of how can sensor-based precision farming revolutionize the agriculture sector in India. c. Challenges to implementation of precision farming in the Indian context.
- Machine learning in agriculture
a. How can machine learning help in farmer preparedness on various issues? Predicting crop productivity, disease outbreaks, pest infestations etc. using machine learning algorithms.
Udit is a Data Scientist at SocialCops, a data intelligence company in New Delhi. He has considerable experience in decision science and works on analyses to solve critical problems in agriculture, health, and retail. Previous projects include targeting agriculture investments to the people and places with the greatest impact, analyzing the socio-economic potential of international markets, and analyzing granular data on online retailers’ customer base. His mission is to create data-centered solutions to problems faced by agricultural India. Previous speaking experience includes debating and conducting sessions on the R programming language.