DICS: From Data to Wisdom for Decision-Making

DICS is the Spanish acronym for data, information, knowledge, and wisdom. It is a conceptual model, known in English as DIKW, that helps distinguish records, context, understanding, and judgment for decision-making. It is not a proven universal hierarchy, nor does it imply that accumulating data automatically produces wisdom. In marketing, it can help us ask what we know, what we infer, and which decision we can justify.

The Difference Between Data, Information, Knowledge, and Wisdom

Data: records of observations, such as a click, a date, or an inquiry received. Their meaning depends on how they were obtained and what they represent.

Information: data organized with context. For example, separating website inquiries by period, source page, and service type.

Knowledge: understanding that enables information to be interpreted and acted upon. It requires testing explanations against experience and evidence; a coincidence does not demonstrate causation.

Wisdom: judgment about what should be done, considering objectives, limitations, and consequences. In this model, choosing not to expand a campaign until contact quality is verified can be more reasonable than pursuing more clicks.

An Example of DICS Applied to Marketing

Imagine a team receives inquiries from two pages. Recorded submissions are data. By classifying them by service and checking which are genuine requests, it obtains information. By reviewing questions, forms, and sales conversations, it builds knowledge about difficulties in the journey. Finally, it decides what to change and what not to change according to resources, priorities, and risks. This is a hypothetical example, not a measured Agencia Cero case.

To apply the model: first define the decision; review the origin and quality of the data; add missing context; test explanations; and record the decision and its results. Our approach to digital analytics and marketing data connects measurement with specific business questions.

Origins and Limitations of the DIKW Model

The academic review by Jennifer Rowley, published in 2007, compares different representations of the hierarchy and cites Russell Ackoff’s From data to wisdom, from 1989. The entire model should not be attributed to a single inventor: its concepts and antecedents have several formulations, and definitions differ between authors.

The ISKO review of the knowledge pyramid describes the model and its criticisms. In practice, experience also guides which data to collect and how to interpret it. The framework is useful for discussing decisions, but does not replace information validation or establish an automatic chain of transformation.

Information Overload and Attention: Historical Context for Understanding DICS

The discussion that follows preserves the article’s ideas, examples, and historical references. Several appear in Jorge Juan Fernández García’s Más allá de Google, a collection of reflections on information and knowledge. Its “laws” and proportions should be read as analogies, aphorisms, or descriptions of a context, not as physical laws or current measurements applicable to every organization.

The Malthusian Law of Information 

“The fraction of each new quantity of information produced that is actually consumed tends toward zero.”

The Malthus analogy contrasts a growing information supply with limits on time and attention. Describing exponential supply growth and linear consumption expresses that tension, but does not demonstrate that both quantities always follow those functions in every context.

The historical reference cited for this idea is Ithiel de Sola Pool and his book Communications Flows: A Census in the United States and Japan (1984). It is retained as a bibliographic antecedent to the discussion, not as proof of a universal law.

The Principle of Information Overload [Formulated by Alfons Cornella]

At the time, Alfons Cornella ventured to define a neologism that enjoyed some success: infoxication. The word emerged to describe information excess or information intoxication, where you have more information to process than is humanly possible, with anxiety arising as a consequence, something technically termed information fatigue syndrome. This term was coined in 1996 by British psychologist David Lewis, to whom we owe the interesting statement: “Knowledge is power, but information is not”. Lewis authored the Reuters-funded report Dying for Information, which described the miserable life of the “information-flooded” executive.

The usual English term for infoxication is information overload. Information overload is already a problem in our society and therefore also a major source of opportunities. The historical comparison with the web’s early years helps explain the concern, but does not demonstrate that everyone experiences more overload today or that spam emerged after search engines.

The amount of information you receive per unit of time can increase as channels and sources multiply. Stimuli arriving through email, audio, video, telephone, instant messaging, feeds, and so on can accumulate. Sending a bit anywhere keeps getting cheaper. But the informationally critical variable, attention, is precisely the opposite of bandwidth: the time we can devote to each input keeps shrinking because we must divide our finite time among more and more pieces of information. Our attention is “the” scarce resource.

The original version cited a 2005 news story about a study for HP and compared the distraction of being always connected with the effects of marijuana. That journalistic comparison is insufficient to establish a clinical or causal equivalence, so we do not use it as health advice. The practical question remains useful: which interruptions can be grouped, filtered, or handled at defined times? The historical reference was BBC News, E-mails hurt IQ more than pot (2005).

This law relates to Goldhaber’s principle of the “attention economy.” The article that originated the idea appeared in First Monday in April 1997: “The Attention Economy: The Natural Economy of the Net” (http://www.firstmonday.org/issues/issue2_4/goldhaber/index.html), and later, in December 1997, appeared as “Attention Shoppers!” in Wired (http://www.wired.com/wired/5.12/es_attention.html?topic=&topic_set).

Michael Goldhaber developed the concept of the attention economy, according to which excess information makes the time people can devote to it especially valuable. His reference website is http://www.goldhaber.org. The discussion also includes Herbert Simon’s well-known observation that an abundance of information consumes attention; here it serves as a conceptual framework, not a measurement of every user’s behavior.

John Hagel (http://www.johnhagel.com/) provided an excellent review of the Economics of Attention concept on his blog (http://edgeperspectives.typepad.com/edge_perspectives/2006/12/the_economics_o.html). Juan Freire also addressed it on his blog (http://nomada.blogs.com/jfreire/), in a post titled “From the persuasion of analog advertising to the absolute sincerity or deception of digital marketing” (http://nomada.blogs.com/jfreire/2007/01/de_la_persuasin.html). Both readings are highly recommended.

Gaining attention depends on message relevance, context, and available alternatives. The historical report How Much Information? 2003, from the University of California at Berkeley (http://www.sims.berkeley.edu/research/projects/how-much-info-2003), estimated around 5 exabytes of new information stored during 2002 in the media it studied. That figure belongs to its period and scope; it does not represent current annual production of all digital information.

The “attention problem” could be summarized in two points: ·  The information “bandwidth” people receive keeps growing because technology enables more information to be sent in less time and because more agents are broadcasting to potential recipients. This “personal bandwidth” is the amount of information someone receives per unit of time, b=i/t, a quantity that grows with each passing day. ·  At the same time, however, “personal attention,” the time someone can devote to each piece of information received, a=t/i, keeps decreasing. This is logical because the two variables are inverses: greater “personal bandwidth” means less capacity for “personal attention.”

What solutions are there? We need to design tools to reduce the informational noise we receive and increase the productivity of the attention time available to us. In fact, tools already exist, but we do not use them properly. For example, feed readers can automatically deliver new publications from pages that interest us without requiring visits to them, a significant evolution from mass media to my media. The difficulty lies in being selective when subscribing to information sources.

But the critical point is communicating more efficiently: synthesizing the message, presenting it attractively, and combining text, images, and any other format that enriches it and makes it easier to understand. Telling stories rather than giving data. Matching what you want to communicate to the recipient’s moment of attention, knowing when they are in the best state of mind to digest, or metabolize, the information provided. Presenting information in a useful, engaging informational diet: information is what surprises us, not what we already know. Information arriving without judgment or passion is noise. And noise is annoying.

This principle was formulated by Alfons Cornella, founder and president of Infonomia.com, the innovators’ network, a think tank on how organizations transform through the intersection of technologies, ideas, and people, in his book Futuro Presente, illustrated by Sergi Rucabado.

The Law of Gravity [Formulated by Juan Francisco Martínez]

“Those who know attract others.”

The metaphor “those who know attract others” suggests that someone able to solve problems can become a reference for others. It is an analogy with gravitational attraction, not a physical relationship or a formula for calculating inquiries from accumulated knowledge.

It should be noted that the law, “those who know attract others,” applies to human beings and physical media. This attraction may occur between internet robots that search, catalog, and store knowledge. Or it may attract people and search engines to them, as happens with Wikipedia (https://www.wikipedia.org).

Within a team, experience may lead certain people to receive more inquiries. Demand also depends on their availability, trust, and how learning is shared. The analogy should not be confused with Newton’s second law or with a demonstrated proportional relationship between knowledge and attention.

In this way, the power of attraction, the number of times someone is consulted or sought out, depends on the difference between what they know and what others know, as well as how unique, scarce, and organizationally necessary their knowledge is. Thus, provided organizational hierarchies are horizontal, open, and flat, the greater the difference between someone’s knowledge and others’, the more often they will be consulted. The way to avoid a bottleneck would be to share knowledge, reducing differences and multiplying the number of nodes through which it can be acquired.

Since the knowledge economy is not based on a zero-sum game, the knowledge someone gains does not require the person sharing it to lose it, making knowledge available to others strengthens the owner of the original seed knowledge as a node within the knowledge network created (more information at: http://matenomia.blogspot.com/2006/07/ley-de-la-gravedad-leyes.html.

The 90-9-1 Rule 

Lurkers: shhhhhh

The 90-9-1 rule describes an indicative distribution: many people read without posting, a minority contributes occasionally, and a small group produces much of the content. Jakob Nielsen explained it in his article on participation inequality. The percentages are not a universal constant: the community, period, and definition of participation need to be measured.

Lurkers remain “shhhhilent.” They are like a silent letter “h,” written in a word but not participating in its pronunciation. As Wikipedia notes, however: “According to communities of practice theory, a lurker has legitimate peripheral participation and is expected, over time, to become integrated as an active member of the community.”

A research reference for this idea of “participation inequality” includes work by Will Hill of Bell Communications Research with other authors: Steve Whittaker, Loren Terveen, Will Hill, and Lynn Cherny (1998): “The dynamics of mass interaction”, Proceedings of CSCW 98, the ACM Conference on Computer-Supported Cooperative Work (Seattle, WA, November 14-18, 1998), pp. 257-264. Available at http://www.ghostweather.com/papers/dynamics-mass-interaction.pdf.

Aili McConnon in Business Week, InData, p. 40, IN, September, 2006, produced highly interesting complementary visual information, available at: http://www.businessweek.com/magazine/content/06_39/b4002423.htm#search=%22Business%20Week%2C%20InData%22. Also relevant is the column “Participation Inequality: Encouraging More Users to Contribute” in Jakob Nielsen’s well-known fortnightly newsletter Alertbox, dated October 8, 2006: http://www.useit.com/alertbox/participation_inequality.html. It is a good article in which Nielsen explains the negative aspects of participation inequality and what can be done to avoid it, as far as possible.

Nielsen (http://www.useit.com/jakob/) is one of the world’s most respected authorities on web usability and co-founder of Nielsen Norman Group (http://www.nngroup.com/) alongside Donald Norman (http://www.jnd.org/), another global usability expert.

The difficulty in estimating these figures is that a lurker, by definition, cannot be recognized online. While the 9% and 1% figures can be calculated directly from the number of commenters and the frequency of their comments, the 90% can only be estimated indirectly.

The Olympic Principle of Informational Resignation [Formulated by Jorge Juan Fernández]

“In a context of information overload, you cannot absorb everything you believe you should know.” 

Corollary: in informational terms, you must know how to lose. In other words, what matters is taking part…

The Asymptotic Syndrome Paradox

“Knowing that you are getting ever closer while still knowing that you never arrive.”

This paradox is aphorism no. 624 formulated by Jorge Wagensberg in his book A más cómo, menos por qué (Tusquets Editores, Metatemas 92).

Jorge Wagensberg was a physicist, professor at the University of Barcelona, and science communicator.

The 99/1 Principle [Formulated by Jorge Juan Fernández]

“A knowledge worker devotes 99% of their time to ingesting information and only 1% to digesting it.”

The 99/1 principle is an aphorism about spending too much time receiving information and too little interpreting it. It is not a verified statistic about how every worker divides their time. Its usefulness lies in encouraging time to compare sources, discuss learning, and turn it into decisions.

In short, the problem created by information overload can be addressed by defining an informational diet and giving ourselves time to digest what we ingest. Because “there are no toxic substances, only toxic doses” (aphorism no. 682 formulated by Jorge Wagensberg in A más cómo, menos por qué).

The Information Search Systems Paradox [Formulated by Alfons Cornella]

“We have machines to generate more information, but no tools to discover which information is most relevant to our needs at a given moment.”

Cornella’s historical criticism highlights a tension: a tool can increase available results without resolving which are relevant. Search systems and AI tools have changed, but sources, context, and relevance still need checking before using an answer to decide.

Underlying this is the dilemma between comprehensiveness, “everything about,” and relevance, “what is most useful about.” The “Google-ized” world is a world of comprehensiveness. But the human world, at least until we can replace our brains with something better, is one of limited information processing: we cannot increase our waking hours. To survive informationally, relevance is critical. Will we ever have the tools and capabilities to turn that comprehensiveness into relevant knowledge?

The Principle of Collective Wisdom [Compiled by Alfons Cornella]

“Under certain circumstances, large groups, or crowds, are smarter than the smartest people in the group.”

In a fantastic and instructive book, The Wisdom of Crowds, translated into Spanish as Cien mejor que uno, James Surowiecki explains that aggregating a crowd’s intelligence generates excellent collective intelligence. Surowiecki therefore suggests that rather than always looking for an “expert,” someone smarter who has the solution to a particular problem, it might be better simply to “ask the crowd.” 

Surowiecki warns, however, that four conditions must be met for a crowd to be intelligent: 

1. Diversity: the group is sufficiently diverse for the full spectrum of possible opinions to be represented.

2. Independence: each person decides based on the individual information they have, not on others’ behavior.

3.  Decentralization: people in the group have a degree of specialization in their local area of knowledge; in other words, expertise is distributed.

4.  Aggregation: there is some mechanism for gathering individuals’ opinions to derive a collective decision.

PageRank is often used as an analogy for distributed assessment through links. This is a simplification: the system does not merely count links, and not all links have the same weight. Nor does it, on its own, amount to Google’s current ranking system. The example helps discuss how to aggregate signals, not justify buying or artificially accumulating links.

The “votes” are cast by millions of pages, behind which are the people who created them, on incredibly diverse topics. They have linked to other pages with considerable independence, although perhaps we should discuss this further, because some pages become de facto references without people even knowing them well, as with newspapers.

Returning to the four conditions, I have the impression that some paradoxes are beginning to emerge. This is a moment in history when practically all four could be met every day. In principle, there is access to as much information as one wants, everyone can specialize in what they prefer, and innovative mechanisms exist for collecting opinions. Yet, curiously, it is also perhaps the moment when we are most misinformed and most susceptible to mass manipulation. In other words, ensuring that people are informationally independent is harder in practice.

James Surowiecki is the author of The Wisdom of Crowds, known in Spanish as Cien mejor que uno. The original reference also mentioned his work in The Financial Page column of The New Yorker; this is included as historical context, not confirmation of his current position.

The Unwanted Email Principle [Formulated by Jorge Juan Fernández]

There are three types of unwanted email that affect knowledge workers and organizations differently:

The first is spam: unsolicited email, often sent in bulk, which may have commercial or fraudulent purposes. The original article cited old estimates from Radicati Group (http://www.radicati.com) and a supposed doubling every 18 months. Without a verified series, date, and methodology, these figures do not describe current volume or constitute a law of growth.

·  The second is PPT spam: solicited or unsolicited email with attachments, coming from known email addresses, without commercial purposes, but stealing a knowledge worker’s time and clogging the server. Topics vary, but focus on three areas: sex, advice on life and love, and sporting rivalries.

·  The third is informational spam: email generated within organizations to make sure “my boss knows how much I work…” It is closely related to the SNAFU principle, “Situation Normal: All Fucked Up”: quality communication is possible only between equals.

The following table preserves the conceptual comparison between the three types. Asterisks indicate relative intensity within the example, not measurements. Source, attachments, size, and server effects vary; the table is neither a technical nor a legal diagnosis.

In any case, if we accept that email is a communication medium whose function is to connect people, each type of unwanted email is addressed differently:

·  Spam can be fought, see kNewton: buscando un orden en la información (2004) for an analysis of the different options, by destroying its business model, primarily by making sending spam more expensive for spammers.

For PPT spam: agree with your contacts on which materials are relevant and through which channel to share them. Avoid forwarding files without a purpose and without considering the recipient’s time. The historical reference to Postel’s law is used here as an invitation to take care with what we send, not as a universal technical solution.

For informational spam: define recipients, purpose, and expected action before sending an email. Use summaries and shared repositories when they reduce duplication. Responsibility depends on both individual habits and team rules; a filtering tool alone does not solve it.

How to Move from Accumulating Information to Making Better Decisions

Start with a specific question, not all available data. Identify who produces it, when it was updated, and its limitations. Separate facts, interpretations, and hypotheses. Decide what additional evidence would change your conclusion, and assign someone to review the result. This process gives DICS practical use without turning it into an infallible recipe.

For marketing teams, the objective may be reviewing contact quality, understanding a drop in inquiries, or prioritizing content. In every case, analysis should connect with strategy and document what is still unknown. You can review our approach to integrated marketing.