Relationship Between Data Information And Knowledge Pdf

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Before one can begin to talk about knowledge management KM , one must start by clearly defining the meaning of the word "knowledge".

A major part of that lexical challenge is the terms data, information, and knowledge. These three terms are often misused, abused, and used interchangeably to the point that their real meaning is often unclear. These three terms must be formally defined and consistently used […].

Data Topics

I suggest to de-emphasize the wisdom part and to insert evidence between information and knowledge DIEK. This framework defines data as raw symbols, which become information when they are contextualized. Information achieves the status of evidence in comparison to relevant standards. Evidence is used to test hypotheses and is transformed into knowledge by success and consensus. As checkpoints for the transition from evidence to knowledge I suggest relevance, robustness, repeatability, and reproducibility.

Data, information, and knowledge are central concepts in health informatics and data science. It is not always clear how authors define these entities and how they envision the transition from data to knowledge to work. Ackoff in [ 1 ]. I also discuss the transition process of from data to knowledge, with a focus on the transition from evidence to knowledge.

I hope that the ideas summarized here will prove helpful to those in charge of knowledge generation in health informatics and data science. Russell L. Ackoff introduced what is now known as the knowledge hierarchy or knowledge pyramid Fig.

He starts with the notion that wisdom is situated at the top of a hierarchy of types of content in the mind, followed by understanding, knowledge, information, and data Fig.

He defines data as symbols that are properties of observables, and information as descriptions. The difference between the two is not structural, but functional, and information is inferred from data. Ackoff discusses management needs in terms of information availability. He states that managers are usually confronted with an information overload and do not necessarily need more relevant information but less irrelevant information, a truism then and now.

He defines knowledge as know-how that comes from learning, i. This process requires understanding what error is, why error occurs, and how to correct it. Ackoff thinks that 1 information systems can be automated and generate information out of data, 2 that computer-based knowledge systems require higher-order mental faculties; "they do not develop knowledge, but apply knowledge developed by people", and 3 that wisdom adds value, endures forever, and will probably never be generated by machines.

Probably for this reason, Jennifer Rowley uses the term "wisdom hierarchy". Her review reiterates two opinions; first, her view that data, information, and knowledge are connected, one helping define the other, and second, her view about the organization of the hierarchy as such.

The ways how the individual items in the hierarchy are converted and elevated to the next level is less well defined. Although the Ackoff hierarchy has received much attention over the years, I strongly believe that in our current evidence-based environment some modifications are in order. First, in a book co-authored with philosopher of science Ben Smart, I suggest dropping the notion of wisdom because, first, the term is fraught with too much baggage from non-scientific context [ 3 ].

More importantly, I do not think that wisdom adds much to the decision-making based on the hierarchy. Instead, I hold that knowledge deserves the position at the pinnacle of the hierarchy. Knowledge can be defined, in the context of medical and public health informatics and data science, as predictive, testable, consistently successful belief , if there is a causal connection between the facts represented by the data, information, and evidence on the one hand, and our beliefs on the other.

In the context of Public Health Informatics, Mensah and Goderre define "data" as raw facts, statistics, context-free numbers [ 4 ]. These include numbers resulting from measurements or from text-mining, images, sound recordings, survey results, simulations, and so on.

They can usually be tabulated and depicted as graphs, or displayed as figures. More formally speaking, data are quantitative or qualitative values of variables. Figure 2 displays a framework for transitions from data to knowledge, and what the arrival at each new stage is good for. Framework for the transition from data to knowledge left and what each level is good for right reprinted from [ 3 ]. I think that information is data in context.

Information is data that have been processed so it is clear what they are about. Once they are collected and contextualized, data are information. According to this view, all information is data, but not all data are information. Information thus conceived can give rise to evidence, which has been defined as "information bearing on the truth or falsity of a proposition".

Thus, all evidence is information, but not all information is evidence. The comparison of information in support of competing conjectures helps define what counts as evidence that, in turn, generates the knowledge that a certain overarching claim is true.

Evidence is generated by comparing information to reference values or standards, which prepares the information for further analysis. In the context of public health, Brownson and colleagues have argued that. They describe three kinds of evidence in public health contexts: 1 the causes of illness and the magnitude of risk factors, 2 the relative impact of specific interventions, and 3 how and under which contextual conditions interventions were implemented [ 6 ].

We discuss the intervention-related part of these kinds of evidence in more detail elsewhere [ 7 ]. In general, evidence is information that bears on the truth of a proposition compared to a standard. According this definition, information becomes evidence only if it bears on the truth or falsity of the proposition that the gardener was indeed the murderer. Only if we can find good evidence that is coherent with this claim can we say that we have knowledge that he really is the culprit.

Actionable knowledge is usually generated from coherent evidence from multiple independent sources of information [ 8 ]. If we refer to evidence as information that supports a specific proposition by bearing on its truth, evidence is context-dependent, because it becomes evidence only by virtue of being relevant as support for a specific proposition, and relevance is, by definition, a contextual concept.

The traditional tripartite concept of knowledge as justified, true belief goes all the way back to Plato [ 9 ]. Gettier argued in that the tripartite definition is not sufficient to constitute knowledge, in essence by offering two counterexamples in which some justified, true beliefs clearly do not count as knowledge [ 10 ]. Multiple strategies to defeat Gettier have been suggested [ 11 ]. In our present context, I think that knowledge consists of beliefs that.

In other words, I suggest that beliefs qualify as knowledge if they predict outcomes with satisfactory precision, if they can be translated into scenarios that put the belief to the test, and if actions based on such beliefs are consistently successful. In short, knowledge is predictive, testable, consistently successful belief.

Indeed, this is exactly what we refer to some belief as being evidence-based. This is why evidence -based medicine and public health should actually be considered knowledge -based once the evidence has turned out to be predictive, is tested, and interventions have been designed and are consistently successful. Of course, the decision when that point has been reached is not made by any one person, but by consensus [ 12 , 13 ]. Thus, all knowledge is evidence, but not all evidence is knowledge.

Are there checkpoints that support the decision to promote evidence to the level of good before we have seen the quality of its predictions, witnessed its testability, and received the good news that interventions based on such evidence are being consistently successful? Here is a collection of candidate checkpoints that I think allow us to proceed from evidence to knowledge.

Since we ask this question with an intervention in mind, our query is not really what makes evidence so good that it is knowledge, but rather what makes evidence so good that it is useful in our context. Usefulness, in turn, is simply the possibility to use this knowledge in ways that turn out to help improve the health of individuals and populations. We need knowledge to justify action. First, although this should go without saying, good evidence is relevant to the problem at hand.

Consider this quote from the Annual Review of Public Health :. Legislators and their scientific beneficiaries express growing concerns that the fruits of their investment in health research are not reaching the public, policy makers, and practitioners with evidence-based practices. Practitioners and the public lament the lack of relevance and fit of evidence that reaches them and barriers to their implementation of it [ 14 ].

The focus on usefulness is, yet again, motivated by the goal of health informatics efforts to inform decision making which leads to effective action. Second, good evidence is robust. This is what Broadbent has called the stability of a result, i. Third, good evidence is repeatable in the sense that similar data gathering and integration efforts lead to similar evidence repeatedly: "Repeatability concerns the exact repetition of an experiment, using the same experimental apparatus, and under the same conditions".

My version of the Ackoff hierarchy is based on what is being done to make such transitions possible, not what transitions represent or what happens when moving from one level to another, such as changes of meaning and value [ 17 ] or the physical, cognitive, and belief structuring when constructing data, information, and knowledge, respectively [ 18 ].

A similar model has been proposed by Richard Heller. In his model, accessing data yields information, appraisal of which yields knowledge.

Neither in his book [ 19 ] nor in the underlying paper [ 20 ] does he define evidence. However, in their publication, Heller and Page offer a list of statistical and implementation characteristics they see as methods with an appropriate population focus that can be aligned with the methods used in evidence-based medicine because the authors consider the entire process from data via information to knowledge to be evidence-generating.

Instead, knowledge is made. My version of the model drops the notion of wisdom, because it is too imprecise a notion to be useful in a health science context. Instead, I suggest to insert the notion of evidence into the inferential sequence between information and knowledge. Data are used mainly as raw material for information generation. When these data are put into context, they yield information that may be useful as evidence. Based on such evidence, knowledge is generated.

Knowledge is evidence-based belief that is predictive, testable, and consistently successful, as judged by consensus among stakeholders. National Center for Biotechnology Information , U. Online J Public Health Inform.

Published online Mar 5. Author information Copyright and License information Disclaimer. Corresspondence: ude. Copyright This is an Open Access article. Authors own copyright of their articles appearing in the Journal of Public Health Informatics. Readers may copy articles without permission of the copyright owner s , as long as the author and OJPHI are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. This article has been cited by other articles in PMC.

Keywords: Data, Information, Evidence, Knowledge. Introduction Data, information, and knowledge are central concepts in health informatics and data science. Figure 1. Open in a separate window. Data, Information, Evidence, Knowledge: DIEK Although the Ackoff hierarchy has received much attention over the years, I strongly believe that in our current evidence-based environment some modifications are in order.

Information vs data vs knowledge

Often the terms data, information and knowledge are used synonymously. The meaning, however, is not the same, so in this article we want to define the terms, separate them from one another, and show their connection. In order to understand where the differences and the connections between data, information and knowledge are, it is necessary to define the terms at first. Data singular: date are understood differently in different sectors. In the basic form, data are different symbols and characters whose meaning only becomes clear when they connect with context. Collecting and measuring observations generates data.

I suggest to de-emphasize the wisdom part and to insert evidence between information and knowledge DIEK. This framework defines data as raw symbols, which become information when they are contextualized. Information achieves the status of evidence in comparison to relevant standards. Evidence is used to test hypotheses and is transformed into knowledge by success and consensus. As checkpoints for the transition from evidence to knowledge I suggest relevance, robustness, repeatability, and reproducibility.


In addition, it is necessary to understand relations between data, information, and​. knowledge. Computers are information processing systems. However, as it is.


Data, Information, Evidence, and Knowledge:

Data and information are similar concepts, but they are not the same thing. The main difference between data and information is that data are a part and information is the whole. Explore how data and information differ through definitions and examples. The word data can be used for a singular fact or a collection of facts.

What is the difference between data, information and knowledge?

4 Comments

  1. Tleehamo 03.06.2021 at 07:53

    PDF | Knowledge, Information, and Data are key words and also fundamental relationship between data, information, and knowledge lies at the source of data​.

  2. Lucas F. 08.06.2021 at 16:28

    Data, Information, Knowledge, and Wisdom.

  3. Melany J. 08.06.2021 at 16:47

    To put it into context, think of data as any series of random numbers and words that hold no meaning whatsoever.

  4. Jay G. 10.06.2021 at 22:09

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