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The data deluge is not just a growing pile of files. It describes more data arriving faster and more often, from an expanding variety of sources. That abundance can help reveal patterns and improve decisions, but only when people select, interpret, and govern the data well. There is no verified current global total in the sources cited here; the available figures below are historical estimates and forecasts, not measurements of today’s data.
What is the data deluge?
The phrase describes three related changes: the volume of data is increasing, data is emitted and transmitted more rapidly and frequently, and data comes from more kinds of sources. UN Global Pulse’s 2012 paper emphasized that speed and frequency, combined with the growing number and variety of sources, make the phenomenon more than a matter of storage capacity.
Those sources can include mobile and social services, which produce both real-time streams and behavioral traces left through ordinary use. A large archive may be difficult to manage, but a continuous flow from many different sources creates additional challenges: data can arrive faster than it can be checked, interpreted, or put to use.
How large is the data deluge?
The figures often used to illustrate the trend are dated. They help explain why the phrase gained traction, but they should not be read as current totals.
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| Figure | What it represents |
|---|---|
| 150 exabytes in 2005 and 1,200 exabytes in 2010 | UN Global Pulse’s 2012 paper reported these as estimates of available digital data worldwide. |
| 44-fold growth between 2007 and 2020, doubling every 20 months | A projection made in UN Global Pulse’s 2012 paper, not a verified measurement of what was ultimately produced by 2020. |
| About 2.7 million emails sent and received, 74,860 YouTube videos watched, and 59,879 gigabytes of internet traffic carried in a sample second in July 2018 | A snapshot cited in the World Bank Group’s 2019 report; it describes that dated sample, not current per-second activity. |
| About 200 exabytes of global internet traffic per month by 2020, or 127 times its 2005 volume | A Cisco forecast cited by the World Bank Group in 2019, not a verified outcome. |
These measures describe different things: estimated data stock, internet traffic, and activity in a particular second. They are not interchangeable. The dated sources cited here do not establish a current global data-volume total or a present-day growth rate.
Why can more data be useful?
Data can help uncover patterns, inform policy, and improve public services when it is relevant to a real decision or need. Businesses also analyze information generated deliberately and information produced as a by-product of ordinary activity. In either setting, the point is not to collect the largest possible amount; it is to use suitable information to produce a better understanding or outcome.
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The World Bank Group’s 2019 report describes data as having public and private value when appropriate protective frameworks are in place. More information can make a question easier to investigate, but volume by itself does not establish that a pattern is meaningful, that a dataset is accurate, or that acting on it will help.
Why does more data create problems?
Volume and velocity can outrun analysis
Large, frequently changing datasets take work to discover, collect, assess, and communicate. For official statistics, the World Bank notes a practical tension between accuracy and timeliness: waiting can improve checking, while delay can make information less useful for a fast-moving decision.
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Varied sources do not automatically fit together
Data from different services or systems may have different formats, coverage, meanings, or levels of quality. A combined dataset can appear comprehensive while still omitting relevant people or events, or combining measures that do not mean the same thing. Interpretation and scrutiny are necessary before treating a pattern as evidence.
Personal data raises privacy and protection concerns
Some data describes identifiable people or sensitive behavior. Collecting or sharing it can create risks, and openness is not automatically a public good. The World Bank Group emphasizes data protection and privacy; an OECD-hosted 2017 background paper also flags sensitivity, ownership, and privacy as concerns to address during data discovery.
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Data can be hard to preserve and reuse
Information that is poorly documented, curated, or disseminated may be difficult for others to understand or use again. A 2012 Nature Cell Biology editorial summarizing the Royal Society’s open-science recommendations highlights accessibility, curation, and dissemination as parts of making research data useful beyond its initial collection.
How should organizations handle a data deluge?
There is no universal fix. The right approach depends on the decision or service at stake, the data’s quality and sensitivity, how quickly it changes, and whether others need to reuse it. A useful workflow begins before analysis and continues through communication.
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- Discover: Identify what data exists, where it came from, what it represents, and whether it is sensitive or subject to ownership or access constraints.
- Collect selectively: Gather information that serves a defined purpose. Consider whether the expected value justifies the collection and the privacy or protection obligations it creates.
- Analyze critically: Check whether the data is relevant and fit for the question. Examine how it was produced, what it leaves out, and whether apparent patterns withstand scrutiny.
- Visualize and communicate: Present findings in a way that helps the intended audience understand their meaning, limitations, and relevance to a decision.
- Curate for appropriate reuse: Document and preserve data where useful, with access and sharing rules that respect privacy, ownership, and other protections.
This sequence reflects the data-literacy workflow described in an OECD-hosted 2017 background paper: discovery, collection, analysis, and visualization. Its practical lesson is that useful data work involves selection and communication as well as computation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to assess before investing in data capabilities
- Purpose and value: Which decision, discovery, or service should improve?
- Quality and interpretation: Is the information suitable for the question, and have plausible alternative explanations been considered?
- Timeliness and scale: How much data arrives, how quickly does it change, and does the task actually require real-time results?
- Privacy, ownership, and access: Does the data involve people or sensitive information, and what protections and sharing rules apply?
- Curation and reuse: Will the data be documented, preserved, and made appropriately usable by others?
These considerations point to a broader response than simply buying more storage. The challenge described by the data deluge involves analytical choices, data quality, curation, communication, and governance alongside infrastructure.
Further reading
For an introductory overview, Oxford University Press lists Dawn E. Holmes’s Big Data: A Very Short Introduction, print ISBN 9780198779575. Its listed topics include the data explosion, storage, analytics, applications in medicine and business, security, and society. It is background reading, not a solution to an organization’s data-management needs.
Quick Recap
Sources
- UN Global Pulse, Big Data for Development: Challenges & Opportunities (2012)
- World Bank Group, Information and Communications for Development 2018: Data-Driven Development (2019)
- OECD-hosted background paper on data literacy (2017)
- Nature Cell Biology editorial on open science and research data (2012)
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