Satya Jayanty

“Data for Good” initiatives often start with strong intent but stall due to weak data foundations, unclear ownership, and an inability to move from insight to action at scale. In practice, social impact requires the same discipline as commercial systems: reliable data, transparent assumptions, and operational delivery—not one-off dashboards or hackathon prototypes. This session focuses on practical, production-grade Data for Good: how organisations use data and AI to drive outcomes across public health, climate, financial inclusion, and social services while maintaining trust, ethics, and accountability. We’ll examine how to structure Data for Good as a portfolio of data products with clear beneficiaries, success metrics, and feedback loops, rather than isolated analytics projects. You’ll see real-world patterns for working with imperfect, sensitive, and cross-organisation data: federated sharing models, privacy-preserving techniques, consent and purpose limitation, and quality checks that reflect real-world bias and data gaps. We’ll show how AI and GenAI can be applied responsibly—forecasting demand, targeting interventions, summarising complex evidence, and supporting frontline decision-making—without introducing opaque or unaccountable automation.

Let's delve into the challenges and opportunities of ensuring data privacy and compliance in AI-driven ecosystems. Discuss strategies for implementing privacy-preserving AI algorithms and complying with evolving regulations. Let's not forget to explore the ethical considerations and implications of data governance practices, frameworks for ethical data use, responsible AI development, and mitigating biases in data-driven decision-making.

DBA is key when a database platform change occurs and necessary to support the application, release processes and there is a miracle waiting to happen! Based on my experience DBA is left out in the key element of DEVOPS, this is unfortunate.