How Big Data Can Influence Decisions That Actually Matter | Prukalpa Sankar | TEDxGateway

Summary

In this compelling story, the speaker explores the challenges and potential of data-driven decision-making, drawing on real-world examples like Myanmar's failed census and India's LPG distribution project. The speaker highlights how outdated data systems hinder critical decisions in sectors like healthcare, education, and environment. The solution, a data intelligence platform, transforms fragmented data into actionable insights, helping leaders make smarter, more efficient choices. The speaker envisions a future where data improves lives in real time, from tracking crime to predicting health outcomes, underscoring the transformative power of accessible, accurate data.

Takeaways

• 😀 Myanmar faced a major census crisis in 2014 when advanced technology failed, leading to the urgent procurement of 170,000 pencils for manual data collection.
• 😀 The census revealed that Myanmar's population was only 51 million, significantly lower than the previously estimated 60 million, causing major disruptions in government planning.
• 😀 Big data was revolutionizing fields like sports and finance, but sectors like healthcare, education, and crime still struggled with efficient data usage.
• 😀 The founder and his co-founder, Ven, established Social Cops to solve the data challenges faced by decision-makers in developing countries.
• 😀 One of the main barriers in using data in India was that it was often recorded in physical registers, in local languages, or stored in disconnected and siloed systems.
• 😀 In India, for example, the health data system in Nagpur was spread across 32 different systems, making it difficult to obtain a unified view.
• 😀 Data mismatches due to inconsistencies in names (e.g., 'Mumbai' vs 'Bombay') made data analysis a nightmare, especially when matching data sets from different sources.
• 😀 Social Cops developed a data intelligence platform that brought together various datasets, enabling more accurate decision-making in real-time.
• 😀 In collaboration with India's oil companies, Social Cops helped plan the opening of 10,000 new LPG centers by analyzing various data points, such as population density, infrastructure, and current LPG availability.
• 😀 The platform’s algorithms accounted for complex variables like geographic proximity, accessibility, and infrastructure to strategically open LPG centers close to underserved villages, benefiting people like Sunita.
• 😀 Social Cops' platform now supports real-time decision-making for various sectors, including government programs, business campaigns, and philanthropic investments, transforming how organizations plan and execute projects.

Q & A

Why did Myanmar's census face issues despite the use of advanced technology?

Myanmar's census faced a significant challenge because the technology backbone supporting the entire census failed, and the country ran out of pencils, requiring an emergency international procurement of 170,000 pencils.

How did Myanmar's census data differ from its previous population estimates?

Myanmar had previously estimated its population to be 60 million, but the census data revealed that the actual population was only 51 million, showing a discrepancy of 9 million people, or 15% of the population.

What was the challenge in using data in sectors like healthcare, education, and crime compared to football?

In sectors like healthcare and education, data is often not used as effectively as in football due to issues like unstructured, incomplete, and disconnected data systems, which hinder real-time data-driven decision-making.

What was the main problem identified during the founders' year of pilots in India?

The main problem identified was the lack of structured and accessible data, especially in developing countries where data is still recorded manually and often stored in disconnected systems, making it difficult to use for decision-making.

How did data inconsistencies impact the process of matching data sets in India?

Data inconsistencies such as the use of different names for the same places (e.g., Mumbai vs Bombay) and different spellings led to very low matching rates (only 15%) when trying to merge data sets, complicating the process of extracting usable insights.

What is the primary function of the data intelligence platform developed by Social Cops?

The data intelligence platform developed by Social Cops aims to bring together scattered and disconnected data sets, presenting them in an easy-to-understand way that helps decision-makers make data-driven choices in real time.

What challenge did Sunita face before the LPG centers were opened, and how did the data intelligence platform help?

Sunita, a woman in rural India, faced the challenge of cooking on a traditional firewood stove, which is harmful to health. The data intelligence platform helped by identifying the optimal locations to open LPG centers, improving access to clean cooking fuel for Sunita and others in similar situations.

What factors were considered in deciding where to open new LPG centers in India?

The factors considered included population density, infrastructure, accessibility, profitability for entrepreneurs, and ensuring that the centers were located close to villages, particularly in areas where people like Sunita lacked easy access to LPG centers.

How did the platform overcome the challenge of missing LPG center locations in India?

The platform overcame this challenge by launching an Android app that allowed 177,000 distributors across India to submit location coordinates, which helped map every LPG center in the country.

What is the vision for the future as presented in the script?

The vision for the future is a world where data is used not just to predict events like elections, but to solve pressing problems like crime, education, traffic congestion, and disease outbreaks, ultimately improving the lives of billions of people.