Do you have any idea how much data is generated and used in the world today? That's what this article is about. How Much Data is Used and Stored in the World? (2025) attempts to answer, arriving at figures that most of us can't even imagine. In 2022 alone, the world generated more than 100 zettabytes of data, that is, more than 100,000,000,000,000,000,000,000 bytes (A byte is one of the smallest units of memory used in computers and cell phones, the most basic unit for representing a character). To give an idea of the magnitude of this amount, one zettabyte is equivalent to approximately 77 billion Brazilian censuses from 2010.
And projections point to a global data volume with exponential growth, reaching approximately 394 zettabytes in 2028, meaning the volume more than doubles each year, and nothing indicates that this will change. The only bottleneck may be storage capacity, which has been increasing over the years, but without keeping pace with the rate of data production. We are increasingly immersed in a world of data. In our daily lives, every digital interaction produces a data footprint: whether it's online shopping, social media likes, reading news, or bank transfers. This massive production is no longer a peculiarity of technology companies, but rather the underlying structure that permeates all aspects of human life.
And how did we get to this point? As economists would say, and coincidentally, very much in line with this year's (2025) Nobel Prize winners in Economics, economic advances driven by humanity's own evolution, innovation, and resulting technological advancements have enabled our current data processing and storage capabilities.
However, history shows how various civilizations used the material mechanisms available at the time to record quantities and, therefore, data. The Incas used quipus, textile artifacts made of wool or cotton, composed of a main cord and hanging cords, encoding the quantities of items (such as agricultural production or the size of the army) through knots and cord colors (Schmidt; Santos, 2017). In Mesopotamia, accounting records were made on clay artifacts, starting with balls, which when marked and grouped represented a contract and its respective monetary value (Universidade Federal De Minas Gerais, 2020).
As time passes, we have reached an era where the material merges with the immaterial, and quantities, as well as various attributes of an object or problem, including ourselves, are no longer even recorded on paper, but rather in bytes on data storage systems, from personal and corporate computers to data centers. This is the famous datafication or transformation of all aspects of our lives into data, which allows us to quantify, measure, and analyze what until recently was unimaginable, enabling us to understand patterns and behavior like never before.
The multidimensional value of data
Human evolution and technological advancements have also fostered the emergence of institutions to regulate these value contracts, as well as the recognition of what this data (or artifact, material or immaterial) represents. Currently, we have reached the point where even a click (a like) has a certain value. But for whom? And how is this value determined?
To do this, we would have to define what data is. I did this mental exercise which led me to philosophical realms where I could hardly see a way out. As a data enthusiast, I felt somewhat indignant for never having stopped to think about it, until I came across a 1989 article by the brilliant Russell Ackoff, a pioneer in applying systemic approaches to management. Ackoff proposes what is known as the Hierarchy. Data, Information, Knowledge and Wisdom (or Understanding), or hierarchical DIKW, usually represented by a pyramid.
The base of the pyramid is formed by data, raw symbols that represent properties of an object or event. Information, which is processed data, is slightly above and gains value and meaning through its organization and context, answering questions such as who, what, when, where, and how many. A little higher up is knowledge, applied in a way that provides instructions and answers to how-to questions, generating a deeper understanding of a topic. Finally, wisdom sits at the top of the hierarchy, referring to decisions made based on ethics and value judgments, defining what must be done to achieve effectiveness. It is wisdom and/or understanding that provides the explanations, answering the questions that ask "why".
Let's consider, for example, the current minimum wage, equivalent to R$1518. Without any context, it's just a number. But if we analyze the values of this minimum wage over the last decade and its variation over time, we have information. If this is placed in the economic context of the time, including crises, wage policies, etc., we will understand this value for Brazilians and their families, generating knowledge on the subject. A public manager can use this knowledge (impact analysis) to assess the need for new salary adjustments at the base level. Thus, wisdom is the ethical and social judgment of the manager, who decides if and how the intervention will be made, transforming knowledge into a fair and effective decision. In other words, if data and information are like a look at the past, knowledge and wisdom are associated with what is done today and what is desired to be achieved in the future.
In my view, in terms of public management, the relevance lies at all levels of this hierarchy: how to make decisions based on information and knowledge extracted from unreliable data? Some say that data is the new oil, which generates controversy, but there is no denying the role it plays in all social and economic spheres and, therefore, its value as intangible assets from a commercial, social, and geopolitical point of view. A "like" may have no value to me, but it certainly will to Meta, generating extraordinary profits from its exploitation and, consequently, contributing to the GDP of certain countries. Data, therefore, possesses a multidimensional value, manifesting itself in distinct ways in the commercial, social, political, and economic spheres.
The social challenge of data
It is important to emphasize that the value of data lies not in its volume, but in its ability to contribute to the generation of knowledge and understanding, especially in public administration, where the capacity to mitigate biases that lead to social injustice is fundamental.
Thus, data, as the basis for the process of generating knowledge and wisdom, is essential for reducing inequalities by leading us to evidence-based decision-making, showing where social and economic vulnerabilities lie. And, in this sense, it is fundamental to never stop learning, as the DIKW hierarchy demonstrates.
From this perspective, and bringing these ideas to the field of decision-making with AI-based support systems, the predictions it generates would constitute the knowledge extracted from the processing of raw data. But it cannot stop there. AI can predict potential regions of greater climate risk, but what to do with that information? And why? Without answers to these questions, the risk of perpetuating our own biases embedded in the data is high, because knowledge without judgment ignores social value. This is where wisdom comes in: when value judgment and ethics are applied to the acquired knowledge, transforming prediction from an action guided by efficiency into a just action, deciding not only guided by how much or where, but by why. And this requires transparency not only to measure and inform, but to transform society, guided by social justice.
The circular cycle
Ackoff's hierarchy is extremely important for understanding the process of knowledge generation and decision-making through data-driven problem comprehension. However, in my view, this proposal has an implicit circular character.
Humanity continues to evolve. Technologies do too. Therefore, these changes are captured through data, and with that, the data itself also changes. Although it is difficult to imagine them, history shows us that, in the future, other ways of capturing data will emerge more precisely or even from different perspectives, impossible now. And, with new quality data, new information will be generated, new knowledge will be extracted, and a new understanding of a given problem may arise. In this sense, wisdom itself is not an end, it is a stage in this circular process, which generates feedback that can reveal the need for new ways of collecting data, for example, starting a new round.
Thus, the data-driven world will always be connected, always tracking, always monitoring, always listening, and always observing – because it will always be learning. And public administration will be no different.
Finally, let's note that, however rapidly AI advances, it can certainly assist with data collection and processing, generating information and knowledge, but wisdom and understanding, at least for now, have a 100% human character, as they are guided by ideals, such as achieving a more just and equitable society. In short, wisdom has a human character and is something a machine cannot acquire.
This text does not necessarily reflect the opinion of Unicamp.
Bibliographic referencess
SCHMIDT, P.; SANTOS, JL dos. The use of quipus as a tool for tax control and accountability among the Incas.Brazilian Journal of Business Management, São Paulo, v. 19, n. 66, p. 613-626, Oct./Dec. 2017. Accessed on: Oct. 17, 2025.
Federal University of Minas Gerais. History of writing. In: UFMG KNOWLEDGE SPACE. Belo Horizonte, 2020. Accessed on: October 16, 2025.
ACKOFF, Russell L. From data to wisdom. Journal of Applied Systems Analysis, Lancaster, vol. 16, no. 1, p. 3-9, 1989.
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