India joins the UN Committee of Experts on Big Data and Data Science for Official Statistics

Recent Context

1. In January 2024, India joined the prestigious UN Committee of Experts on Big Data and Data Science for Official Statistics (UN-CEBD)
2. UN-CEBD was created to further investigate the benefits and challenges of Big Data, including the potential for monitoring and reporting on the sustainable development goals.
3. The milestone coincides with India's recent membership in the United Nations Statistical Council in 2024.
    

What is the UN Committee of Experts on Big Data and Data Science for Official Statistics (UN-CEBD)?

1. Background

   1. The UN-CEBD was established by the United Nations Statistical Commission in its 45^th session in 2014.
   2. Australia was the first chair.
   3. Members: It consists of 31 member states and 16 international organizations (including India).

UN Statistical Commission It was established in 1946 and is the highest body of the global statistical system bringing together the Chief Statisticians from member states from around the world. It oversees the work of the United Nations Statistics Division (UNSD), and it is a Functional Commission of the UN Economic and Social Council (UN ECOSOC). Members: The Commission consists of 24 member countries of the United Nations elected by the United Nations Economic and Social Council. Members have a four-year term. India became a member in 2024 after a gap of two decades. Prior to this, it was a member in 2004. Headquarters: New York

2. Mandate

   1. Strategic vision, direction and coordination: For a global programme on big data for official statistics, including for indicators of the 2030 Agenda for Sustainable Development.
   2. Promotion of practical use of big data sources: Including cross-border data, while building on existing precedents and finding solutions for the many existing challenges
   3. Capacity-building, training and sharing of experience
   4. Communication and advocacy: For the use of big data for policy applications, especially for the monitoring of the 2030 Agenda for Sustainable Development
   5. Building public trust: In the use of big data for official statistics

What is the significance of India’s inclusion to UN-CEBD?

1. Streamline Statistical Production: Drive innovation in data collection, processing, and analysis to reduce the time lag in data availability.
   1. This can be possible by integrating non-traditional data sources such as Internet of Things (IoT), satellite imagery, and private sector data streams.
2. Improve Decision-Making: Provide policymakers with real-time insights for evidence-based decisions, addressing key socio-economic challenges.
3. Showcase India’s initiatives: Including the establishment of the Data Innovation Lab and exploration of alternate data sources such as satellite imagery and machine learning for policy making
   1. The Data Innovation Lab focuses on fostering an ecosystem for innovation to strengthen the National Statistical System (NSS)
4. Foster International Collaboration: Share India’s expertise while learning from global best practices to create robust, future-ready statistical frameworks.

What is Big Data?

1. Big data refers to extremely large and diverse data that continues to grow exponentially over time that cannot be stored, processed and analysed by traditional data management systems.
2. The amount and availability of data is growing rapidly, spurred on by digital technology advancements, such as connectivity, mobility, the Internet of Things (IoT), and artificial intelligence (AI).

3. Characteristics of Big Data

   1. Volume: The sheer amount of data generated every second, from various sources.
   2. Velocity: The speed at which data is generated, collected, and analysed.
   3. Variety: The different types of data (structured, unstructured, semi-structured) from multiple sources.
   4. Veracity: The quality and accuracy of the data, which can sometimes be uncertain or incomplete.
   5. Value: The potential insights and benefits derived from analysing the data.
   6. Variability: The meaning of collected data is constantly changing, which can lead to inconsistency over time.

4. How is Big Data used?

   1. Integration: Collection of huge amounts of data from different sources.
   2. Management: Big Data must be stored using tools like cloud storage. It also needs to be processed and made available in real time.
      1. Cloud Storage is a mode of computer data storage in which digital data is stored on servers in off-site locations by a third-party provider.
   3. Analysis: It involves analysing and acting on big data. It includes using tools to create data visualizations like charts, graphs, and dashboards.

What are the Applications of Big Data?

1. Governance:

   1. E-Governance: Using citizen data to improve public service delivery, transparency, and policymaking.
   2. Urban Management: Traffic management, waste management, and real-time monitoring of urban infrastructure.

2. Healthcare

   1. Disease Prediction & Prevention: By analysis of medical records, genetics, and wearable device data to predict health risks.
   2. Personalized Medicine: Forming tailored treatment plans based on patient data.

3. Education

   1. Personalized Learning: Adaptive learning platforms that customize study materials based on student progress.

4. Agriculture

   1. Precision Farming: Using satellite imagery, IoT sensors, and weather data to optimize crop yields.
   2. Supply Chain Optimization: Predicting demand and reducing wastage in the food supply chain.

5. Business

   1. Advertising: Tracking consumer behaviour and shopping habits to deliver hyper-personalized retail product recommendations tailored to individual customers.
   2. Fraud detection: By monitoring payment patterns and analysing them against historical customer activity.

What are the Challenges with Big Data?

1. Data Storage & Management: Storing petabytes (1024 terabytes) or even exabytes (1024 petabytes) of data efficiently is a challenge. Traditional databases struggle to handle large-scale data.
2. Problems with data quality: Raw data is messy and can be difficult to curate. Thus, poor quality data can impact decision-making, data analytics, and planning strategies.
3. Integration complexity: Combining data from diverse sources (social media, sensors, logs) is challenging.
4. Lack of data talent and skills: This can impact the rate of data processing and analysis.
5. High costs: This is in the form of high computational costs by using powerful servers, GPUs, or cloud resources.
6. Data security and privacy concerns: Big data contains valuable business and personal information, making big data stores high-value targets for attackers.

What are the steps to address these challenges?

1. Cloud Storage: Use platforms like AWS S3, Google Cloud Storage, and Azure to store large amounts of data.
2. Efficient data processing: Using methods like –
   1. Edge Computing: Process data closer to the source to reduce latency.
   2. Parallel processing: A computing method that uses multiple processors to work on different parts of a task simultaneously
3. Continuous Learning & Training: Development of data talent and skills through courses on Coursera, Udacity, or edX.
4. Data Integration Tools: Data can be integrated from multiple sources using data integration tools like Talend, Informatica etc.
5. AI-Driven Resource Optimization: Usage of AI to predict workloads and scale resources dynamically.
6. Ensuring Data Security & Privacy: Using methods like -
   1. End-to-End Encryption: A secure communication process that prevents third parties from accessing data transferred from one endpoint to another.
   2. Regular Security Audits: Continuously monitor and assess security risks.

India’s initiatives towards Big Data

1. National Data & Analytics Platform:

   1. The National Data and Analytics Platform (NDAP) facilitates and improves access to Indian government data.
   2. Through the platform, data sets from across India's extensive administrative landscape can be accessed.
   3. It was launched by NITI Aayog in 2022.

2. Big Data Management Policy:

   1. Drafted by CAG for auditing large chunks of data generated by the public sector in the states and the union territories.

3. National Data Warehouse on Official Statistics

   1. It will be established by the Ministry of Statistics and Programme Implementation.
   2. It will encompass information regarding education, employment history, financial activities, and health documentation.

Conclusion

India’s joining the Committee of Experts on Big Data and Data Science for Official Statistics can help in revolutionizing statistical production and dissemination. It can ultimately contribute to a more resilient and data-informed world. This recognition will strengthen India’s ability to influence global statistical practices, reinforcing its commitment to data-driven progress and sustainable development.