## Data that Delivers

## Source details

**Canonical URL:** [Data that Delivers](https://www.imf.org/en/publications/fandd/issues/2025/12/editor-letter-data-that-delivers)

## Other formats

- [Markdown version](/en/publications/fandd/issues/2025/12/editor-letter-data-that-delivers/index.md)
- [Structured JSON version](/en/publications/fandd/issues/2025/12/editor-letter-data-that-delivers/index.json)
- [Bundle manifest](/en/publications/fandd/issues/2025/12/editor-letter-data-that-delivers/bundle-manifest.json)

## Bibliographic details
- Authors: GITA BHATT
- Published: November 18, 2025

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### Overview and framing
- Publication: F&D Magazine — Data that Delivers
- Author: GITA BHATT
- Date: December 2025
- Reading indicator: Less than a minute(0 words) Read
- Core thesis: To serve the public good, data needs to help us see our world more clearly, make better decisions, and respond intelligently to complexity.
- Closing observation: "Data, after all, is a means not an end."

### The data deluge and core questions
- New data sources (satellites, smartwatches, social media, transaction swipes) and techniques are expanding measurement to levels previously unimaginable.
- Key questions posed:
  - How can we distinguish meaningful signals of economic activity from noise in the age of artificial intelligence?
  - How should new data sources be used to inform policy decisions?
  - To what extent can new sources of data complement or replace official statistics?
  - Are we measuring the metrics that matter in today’s increasingly digital economy, or merely tracking legacy indicators?

### Alternative/private data versus official statistics
- Kenneth Cukier: harnessing alternative data requires a new mindset; economists risk clinging to outdated metrics despite faster, granular, and sharper alternative insights that better reflect "ground truth".
- Limitations of alternative data:
  - Often a by-product of private business activity and carries private-sector biases.
  - May lack long continuity and robust methods that underpin official indicators.
- Conclusion: Official statistics remain essential.

### Central banks, statistical agencies, and transparency
- Claudia Sahm: central banks are using new data to fill gaps (including falling response rates to national surveys) but do so in tandem with trusted official sources.
  - Recommendation: strong ties between statistical agencies, private providers, government officials, and academics to improve data quality.
  - Caution: reliance on data sources not available to the public erodes transparency and central bank accountability.
- Bert Kroese (IMF): reliance on private data must not reduce resources for official number crunching; strong, independent national statistical agencies are vital to data integrity and policy credibility.

### Measurement modernization and intangible assets
- Rebecca Riley: core economic metrics like GDP and productivity are increasingly misaligned with a rewired, data-driven economy.
  - Recommendation: modernize measurement systems to better capture intangible assets such as digital services and the evolving structure of global production.

### Data concentration, sharing, and geopolitics
- Viktor Mayer-Schönberger: concentration of data collection among a handful of Big Tech companies threatens competition and innovation.
  - Policy case: mandate broader data sharing.
- Thijs Van de Graaf: AI's data hunger creates material pressures (energy, chips, minerals, water) that are reshaping global power dynamics.

### Valuing, applying, and scaling data innovations
- Laura Veldkamp: explores how to price, use, and share information and proposes approaches to make intangible data countable.
- Jeff Kearns: highlights nowcasting and other innovative approaches helping developing economies close information gaps.
- Saurabh Garg (head of India’s statistical agency): discusses tackling challenges of scale as public demand for real-time data grows.

### Synthesis and policy implications
- Better measurement is not solely about more data but about using data wisely, especially as AI amplifies both possibilities and noise.
- Policy priorities distilled from the issue:
  - Preserve and strengthen official statistical capacity and independence.
  - Foster collaboration among statistical agencies, private providers, government, and academia.
  - Ensure transparency by prioritizing publicly available data sources when possible.
  - Modernize measurement frameworks to account for digital services and intangible assets.
  - Implement policies to reduce data concentration and promote broader data sharing.
  - Recognize and manage the material and geopolitical implications of AI-driven data demand.

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## Content in this bundle

- **Data that Delivers**
  - [Data that Delivers (Markdown version)](/-/media/files/publications/fandd/article/2025/12/editors-letter.pdf.md){rel="alternate" type="text/markdown"}
  - [Data that Delivers (PDF)](/-/media/files/publications/fandd/article/2025/12/editors-letter.pdf){rel="external" type="application/pdf"}
- **fd-dec25-lowres - 2024. Each dot represents a country’s**
  - [fd-dec25-lowres - 2024. Each dot represents a country’s (Markdown version)](/-/media/files/publications/fandd/article/2025/12/fd-dec25-lowres.pdf.md){rel="alternate" type="text/markdown"}
  - [fd-dec25-lowres - 2024. Each dot represents a country’s (PDF)](/-/media/files/publications/fandd/article/2025/12/fd-dec25-lowres.pdf){rel="external" type="application/pdf"}

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_Source: https://www.imf.org/en/publications/fandd/issues/2025/12/editor-letter-data-that-delivers_