1 - Does the notion of truth presented on 133 (and critiqued on 134) privilege a Western-rational, ahistorical view of existence? I'm thinking of this primarily in relation to subjective experience, and how, realistically, the default experience (speaking from a US perspective) privileges the existence of the White Western cis-male as the 'default' and therefore most true and rational experience. How does structuring information around this notion of truth limit the information we deem valuable, and how do we expand the definition to avoid these limitations?
2 - In my opinion, the main takeaway from Fricke's article is that the DIKW hierarchy falls short because it presents a limiting view of what 'information' can be. As someone interested in archives, I think a particularly valid part of our role is to document subjective, lived experiences - does moving away from, or expanding beyond, the DIKW hierarchy allow for these lived experiences to become 'information' or should we classify them as something separate?
3 - If we follow Fricke's analysis of wisdom on 140, is wisdom something that can be taught? My interpretation is that wisdom for Fricke is somewhat equivalent to critical thinking mixed with one's own moral and ethical decision-making capabilities: if that's the case, I don't think wisdom really plays, or can reasonably play, an important role in the study of information.
1. In page 5, Martin Fricke points out that data cannot be inaccurate or mistaken. If that is true, then how is it possible that there are processes like data cleansing or data scrubbing? If the data of a temperature of a region denotes a temperature reading that is false, what do we refer the inaccuracy as? And is it feasible to put forth that data has no value at all? According to Rowley, data has the least value in the information hierarchy. How can this contradiction be validated?
2. The author argues that the DIKW schema does not support the answering of the ‘why’ question, which is mainly inductive reasoning. In this case, if there had to be statements to validate a point should there be an additional stage of processing between data and information that takes the role of logically evaluating the final information?
3. Fricke quotes Floridi’s writing which states that information is polymorphic and poly semantic. Polymorphism is defined as existence in different forms. How can information acquire different forms, if it processed data can there be different types or levels of processing so as to deliver different pieces of information from the same set of data? If that is done, will it not cause randomness in the upper levels of the hierarchy?
1. I am wondering if there has been any work that fills the gap between the cognitive sciences and the information field. For example, as cognitive science discovers more about how the brain processes information and where memory resides, should the information field be modifying its practices to be more brain-compatible? As we think about how humans might use or process “declarative knowledge” into “inexpressible procedural knowledge” does this suggest how the field should facilitate this process?
2. While I agree with Frickè’s arguments that the DIKW hierarchy is philosophically unsound, I am also wondering if there may be times when it could be useful in a more mundane or practical sense? I am thinking specially about using the hierarchy model in a less literal way and more as a visualization, for example, to help teachers form a more practical definition of the differences between data, information, and knowledge. Do you think this could help teachers identify the distinction between overloading students with information and imparting some useful declarative knowledge in the classroom?
3. I love the way that Frickè’s ends this article by dismantling the relationship between wisdom and “truth” by stating that wisdom is the extensive collection of appropriate and practical knowledge. I am curious if after reading this, if anyone believes that there can be any objective “truths” contained in data, information or knowledge? I am also curious if anyone has done additional work that builds on Friké’s arguments by creating a new model for looking distinctive qualities of data, information, knowledge, and wisdom and if so, what these models might look like?
1. I like this paper because it revealed the sufficient parts of DIKW model. The first question is how will the supporters response to the statements like "Most of the snakes are dangerous.", as mentioned in the paper? Such a statement is a kind of information for sure while it is a useful knowledge as well. Or this means information and knowledge are overlapped in the DIKW model?
2. In the paper, the author claimed that DIKW model might have side effect on data analysis due to it encourages people collect data as much as they can. This assertion is unreasonable for me. Though I highly doubt the usage of DIKW model, I believe data is the foundation of our world and , of course, is the basis of information. My question is what kind of the relationship between data and information is sound to the author of this paper based upon his description in the paper?
3. I agree with most of the ideas that Frickè arises in the paper, however, I think a good critique essay can build a better solution as well as finding the problems in the old one. In this case, base upon the problems that the author revealed, what is the better model in the author's mind to describe the relationships among data, information, knowledge and wisdom?
1. Fricke suggests that DIKW theory “encourage uninspired methodology.” This can include collection of vast amounts of non-useful data to hope that it will one day ascend to information. (Fricke, 135) Several services on the web serve specifically to archive older creations of the web, newsgroup postings, emails given to them, and other parts of the enormous amounts of human data that we now generate. Being electronic, this data would be lost if not specifically preserved. Is it unsound methodology to preserve it as a historical record, without immediate research goal?
2. One argument to be made against the blind collection of data is a basic privacy objection. When dealing with enormous collections of unsorted data, it is easy to trawl in items that might embarrass or damage a person, whether they were the copyright owner or not. How should one address this side effect of data mining? At present, our tools for manipulating such enormous fields are limited and often in use by restricted entities.
3. At the conclusion of the paper, Fricke states that “The DIKW account of wisdom, in its Ackhoff version, is reasonably in harmony with [this],” this being Fricke’s own statements about wisdom, of its broader incorporation of information or weak-knowledge into knowledge-that, and being able to hold this in a broader ethical framework. (Fricke, 140). Are these notions of wisdom truly compatible, or is wisdom itself still too vaguely defined to truly debate definitions?
1. In his article, Frické spends a good amount of time differentiating between what is and what isn’t data. He states regarding the accuracy of data that, “inaccurate or mistaken data is not going to be data at all” (4). However, is this really true? Just because the data is incorrect, does that really negate the fact that it is data? Or is it more correct to believe what Frické adds near the end of the article – that “data needs to be true” (7)?
2. Frické also claims that the DIKW pyramid “encourages the mindless and meaningless collection of data in the hope that one day it will be ascend to information” (5). I understand that while continual collection of data may seem “mindless” or “meaningless”, how does anyone (especially Frické) know that said collection is worthless? One never knows when a certain piece of data is going to be needed. Something that seems trivial may be to another person of the utmost importance. And, if that’s the case, is a “mindless” collection of data really “meaningless”?
3. In Frické’s article, he seems to advocate a view of the relationship between knowledge and information that would allow “knowledge and information [to] collapse into each other” (11). However, are the terms really so synonymous that one essentially means the other? If a person is given a random piece of information, are they really being given knowledge? Or does knowledge come after the information has been processed and becomes useful?
1. On page six Fricke says “the dilemma is either DIKW does not permit inductive or similar inference, in which case statements like ‘most rattlesnakes are dangerous’ cannot be information or DIKW does permit inductive inference in which case it abandons its core faith that data and information have to be rock solid true.” Why can’t the DIKW hierarchy embody both? Part of obtaining knowledge is that sometimes your first set of data or information is not always right and from those inaccuracies can come a newly constructed answer that might be what one is looking for.
2. Again on page six Fricke says that “the assertion being made here is that collecting data blind is suspect methodologically.” He then quotes Popper who says that “the belief that we can start with pure observations alone, without anything in the nature of a theory, is absurd,... How is approaching something you know nothing about, ex) parachute journalism, a bad way to collect data? Some of the greatest discoveries of data and information have happened by pure chance or accident. First observations are extremely valuable as they can lead to further and better research.
3. In describing the relationship between data and information on page 11, Fricke says that “all data is information. However, there is information that is not data. Almost all of science is information, but in most contexts, it is not data.” So what then is the difference between data and information? They seem to definitely have a symbiotic relationship. Returning to “there is information that is not data,” how is that possible? All data is information, how can there be information that isn’t data?
1) Fricke mentions on page 133 'while wisdom is traditionally taken to be a layer in the hierarchy, few authors use or discuss it. This may be because it is not required for the problems they address'. Could that be where part of the overall problem with the hierarchy? Since all these definitions are so subjective and contradictory, doesn't it add to the confusion when wisdom is shoved in at the top with no real in-depth discussion. About where it fits and how?
2) How do speculative questions/theories in science fit within DIKW? Moreover, why doesn't Information Science mirror science's philosophy on information?
3) If people are intent on keeping the DIKW hierarchy as a part of the world of Information Science, why is there no push to make it more flexible or accommodating to different types of data and information that are somewhat to completely intangible? Fricke quotes on page 140 stating "It is hardly to be expected that a single concept of information would satisfactorily account for the numerous possible applications of this general field". If this is so, why push to have a concrete definition within the field, when we probably need to focus on a succinct way to describe it to people outside the field?
1) I agreed with Frické’s misgivings about the unidirectional DIKW hierarchy and its apparent failure to account for the role of prior knowledge (or even wisdom) in allowing for a definition of data as purposefully-gathered observations. That said, I am not sure I agree that the DIKW hierarchy does not allow for data to be purposefully-gathered at all. Frické asserts that “that the earth goes around the sun… [cannot] be inferred from data” (135); this would seem to imply that the scientific observations of astronomers like Galileo do not constitute “data” because they were gathered in purposeful support of a theory. Is this a fair reading of Frické? Is his definition of “data” too narrow?
2) Frické reintroduces the philosophical distinction within knowledge of “know-that” and “know-how,” with “know-that” knowledge being easily transferrable and storable and “know-how” being internal and inexpressible. I am interested in how he would account for what I would term “know-of,” which would be a knowledge of the processes by which one “knows that” or “knows how.” (For instance, “know-of” would include the knowledge that one’s observations of the world are hindered by sensory and instrumental limitations.) Is “know-of” a form of knowledge? Would this instead fold into Frické’s definition of wisdom?
3) This article made good points about how questions of “why” are eliminated from the DIKW hierarchy because the hierarchy relies on so-called “objective” truth rather than sound conjecture. Why might information professionals (and scholars in general) still be susceptible to problematic beliefs in certainty and objectivity? Is this problem more pervasive in the sciences than it is in the humanities?
1. In the part "How sound or desirable is this hierarchy", the author believed "there is information about what to us is the dark world"(which I agree), and this information does not rest on data. However, even if there is the huge domain of the unobservable for which no instruments of measurement exist now, we cannot assert that there will not be any instrument in the future. I wonder could the potential of measuring be evidences "the dark world" is still resting on data.
2. It seems to be reliable that data needs to be true. However, is the mistaken data not one kind of data? I do not think so. In my view, data has no meaning: we tag it to turn it into information. As the author's example of temperature, I think 87 degrees can be information, "degree" is a tag, which imply "temperature", and the number "87" is the data, so how can "87" be true or false. There is a huge amount of data stored in the computer which even has no meaning and useless, how can this data be true or false? Since "data is true" seems to be the foundation of this article, I'm not sure did the author lead an over complicated meaning of data.
3. Discuss the concepts of weak knowledge and strong knowledge, I wonder are there standards to distinguish them? Do they have overlap between each other? Can they transform to each other? How to transform? When I read some specialized "strong knowledge" on the book but cannot deeply understand it, can I classify it as information (weak knowledge)?
1. In this article, as in almost all of the other articles we have read, wisdom is, again, only mentioned briefly. Because this article is a critique of the DIKW hierarchy, I would have liked them to discuss why wisdom was included in the first place? And if authors/scholars can pick and choose what they want from the pyramid anyways, then why use it as an accepted standard?
2. I felt that the article was interesting, but focused too much on data, whereas other articles using the DIKW pyramid focused primarily on defining information and knowledge. Was the intent of the critique to discredit data as a building block so that the whole pyramid collapses? Isn't the pyramid just a theoretical approach to the structure of information and friends? It seems that even the critics of the DIKW pyramid accept it as the standard. Is this because there isn't another model to assess?
3. In the article, on page 134, Fricke mentions the origins of the DIKW pyramid theory and the now discredited theories it was based on. From a historical standpoint, if our predecessors had succeeded on pinning down definitions for information, knowledge, etc., would it have permitted the field to grow as it has? It seems that every article is bent on defining these terms, but in doing so, would very clearly go against the amorphous nature of information. So why the motivation to provide definitions? I understand the need to work from an established structure, but information is hard to pin down for a reason, or else it wouldn't be information.
-I’m a bit confused by Fricke’s proposition that data is, by definition, accurate. He then admits that that cannot be guarunteed and that data is “fallible and conjectural” (137).So what is untrue data if not (just like true data) some of the building blocks of information, knowledge and wisdom? And how can data ever really be data if by nature it is relative or, like a measurement, never specific enough to truly be accurate?
-Fricke goes on to announce that “all data is information. However, there is information that is not data” (140). I like the idea that information goes beyond data, that it becomes more than the sum of its parts and disrupts the concept of a DIKW pyramid. So if we are willing to go this far with Fricke, do we then believe that information and knowledge are synonomous? Or is knowledge still a step beyond information?
-Fricke announces that there are many different senses of information and none of them is entirely sufficient to be used across the entire field of information science. So why is he still trying to propose his own definition, wider and more inclusive though it may be? Why not revel in ambiguity?
1. “The dilemma is: either DIKW does not permit inductive, or similar, inference, in which case statements like ‘most rattlesnakes are dangerous’ cannot be information or DIKW does permit inductive inference in which case it abandons its core faith that data and information have to be rock solid true. (p 135)” But isn’t the core concept of knowledge or wisdom to be when you apply previous learning or experiences to make extended understandings? So if one understands one rattlesnake to be dangerous, one can understand through reason that most rattlesnakes could also be dangerous, correct?
2. With ‘big data’ (one of the hottest topics in information science right now), we have large quantities of data that is being collected blindly, hoping for analysis later to find meaning. At the moment, the collectors don’t necessarily know what they are looking for so they are simply trying to collect everything; essentially, if you don’t have the data collected then you might miss opportunities to later find meanings. For example, if you are not collecting web analytics now, then you can’t compare user patterns. You need to have history to make the comparison, even if at the outset one might not know what it is that one would like to compare.
3. I am not sure if I’m completely understanding Fricklé’s agreement “Data typically is expressed by existential-conjunctive logic, information requires the full first order logic. (p. 140)” So data is recorded with a set of circumstances attached to it – such as on a certain date and a certain time the temperature was X? I don’t understand how information compares here.
1. p.136 "So much for data and information in the DIKW hierarchy. The pyramid has no foundations." Is there something about reviews that allow you to write with a human voice? 2. p.139 "...data...is that of anything recordable in a database in a semantically and pragmatically sound way." What is a database? Are telephone books databases? 3. p.141 "Knowledge was know-how, knowing how to control the systems. Then wisdom was merely a matter of using that practical know-how to achieve appropriate ends. That is a reasonably defensible view - it just does not want to be embedded in the DIKW hierarchy." Have any theories started with wisdom?
1. The author points out that information is logically stronger than data. What about emotions and intuitions, which come out without much support of logic? Should they be accounted as information as well?
2. The author discusses wisdom in the end and lists different characteristics of it. Would these characteristics vary culture to culture, community to community?
3. The author states that there is information that is not data. Information can be about pure logic. But is there is information that cannot be traced (logically deducted) back to data? Namely, is there information that has no root in data?
1. "...there are instruments to detect some unobservable entities" If there are instruments to detect these unobservable entities, then are they not being observed? For that matter, how is it known that unobservable entities exist if they can not be observed?
2. Frické's dilemma about inductive inference seems to lean too heavily on the idea that everything can be known. If the idea that everything is information or everything can be informative is accepted then everything must be known before statements like 'most rattlesnakes are dangerous' can be made. It is not possible to know everything and therefor making inferences based on what can be known is the only reasonable solution.
3. Frické's issue with the DIKW hierarchy seems to stem from a fact that was proven in Zins article. There are many definitions for the building blocks of IS and because of this, the relationships between the building blocks are debatable and unclear. Given that the field of IS is built upon blocks that which no one can agree unanimously on the specifics of, it is a given that the issues that Frické has with the DIKW hierarchy will exist.
1. Fricke makes the claim that the DIKW pyramid should be abandoned (at least I think that’s his claim), but then goes on to say that “discarding the DIKW would leave an intellectual and theoretical vacuum over the nature of data, information, knowledge and wisdom”. These statements seem to contradict one another. Could someone please clarify what Fricke means? Perhaps I’m interpreting the second statement wrongly.
2. Many of the articles thus far, including this one, disregard wisdom, although Fricke does attempt at the end to address it in slight detail. Could we potentially remove wisdom from the hierarchy since no one seems to want to define it, or is it essential? If Fricke’s argument is to discard the hierarchy entirely, then he probably doesn’t care either way.
3. Is Fricke arguing that weak-knowledge, and especially weak-knowledge-that, is better, more accurate and more certain than stronger knowledge statements?
1.Martin criticized the mindless and meaningless collection of data. But was he too subjective? In reality, we truly tried to collect the data for a data warehouses. Sometime data seem no value now could make a big difference in the future.
2.In the 3rd chapter, Martin assumed that the DIKW view had excluded the unobservable data from being any part of ‘Information’. So here comes the question, does the view of the DIKW hierarchy really exclude these kind of data?
3.Martin mentioned only data, or statement inferred from data, can be information. He also asserted that inaccurate or mistaken data are not going to be data at all. But sometime, we are unable to confirm whether former data are right or not. So how could we deal with this part of the data?
Frické states that data in the DIKW pyramid rests on data that he points out is “roughly, the outcome of measurements by instrument.” By doing this he is placing a purely empirical slant to data and ignores some of the thoughts put forth in the Zin article which, while some panelists mention data as measurable also mention data as symbols and representations which can then be interpreted in some manner. By making a choice to place data in this empirical box, as it were, does Frické have a point about data or is he simply ignoring the various thoughts about the definition of data/datum?
Frické notes that a core faith of DIKW is that data and information have to be rock solid true(135). Is this always the case? Or does this change from moment to moment when new data and information is gained?
With the amount of information or data currently available at any given moment does the collection of large amounts of data without a solid plan in mind make for shoddy methodology as Frické says? Or would collecting data allow for patterns or understanding to emerge at a given moment, e.g., when collecting data from social media?
1. On page 135, paragraph five, Frické claims that the DIKW theory "seems to encourage uninspired methodology," saying it encourages "mindless and meaningless collection of data in the hope that one day it will ascend to information--pre-emptive acquisition." Though this data-preservation "overkill" is certainly worth serious consideration, especially in light of preservation selection issues faced by archivists, his example also contains one of its own possible refutations. He talks about data-mining and scoffs at companies' practice of recording customer's purchases as an example of "meaningless collection of data." However, isn't it the case that there IS most definitely a point to this type of massive data collection: it enables a business entity to improve their profit ratios when placing orders, more selectively (and thus cost-efficiently) market to customers, and see larger sales trends? I don't know that I can agree with Frické that complete data collection and preservation would be useless, if such a thing were ever technically possible; isn't it the case that unknown future entities may be able to use what we may consider "meaningless" data in heretofore undreamt-of ways?
2. I think one of Frické's main points, which he summarizes in the final paragraph of section 3, near the end of page 136, is that the DIKW model goes about making data useful in a backward way. Isn't Frické ultimately arguing that "know-that" knowledge emerges from "know-how"? Is this argument a parallel to his semantic vs. syntactic discussion on page. 139, where semantic refers to meanings, while syntactic refers to formal order and relations? Would it be possible for us to make a chart of some of his dualistic assertions, to better understand his larger philosophy?
3. Maybe I'm a dense positivist, but I don't understand Frické's claim on page 135 that "that the earth goes around the sun" cannot "be inferred from data. How else did Copernicus come to his conclusion? Certainly not from common sense, but, rather from measurements and the ensuing data, in this case, trigonometry...? Right?
1. Apparently, in this article the author aimed to abandon the DIKW pyramid theory and make it no longer is part of canon of information science field. So does the author share some common opinions with in the DIKW theory? 2. The author talked about the authenticity of data (true or false) and emphasized that inaccurate or mistaken data is not going to be data at all. So my curiosity here is that the only way to judge whether the data is mistaken or not is to be processed by human`s justification. And the author also pointed out when the data is processed into an answer to an enquiry, the data becomes information. In the situation I just raised, the data is processed to answer whether it is mistaken or not, so is there any conflicts coming out? 3. There is a dilemma being raised- either DIKW does not permit inductive or similar inference, in which case statements, or DIKW does permit inductive inference is which case it abandons its core faith that data and information have to be rock solid true. So where is the dilemma derived from? Is that from the new definitions of data and information?
Q1 In the second part of the article, p132-134, the author talked about what is DIKW based on other scholars. And in the end, the author comes to wisdom, a category given only limited discussion in the present paper. Then the author concluded that since the criticism offered in this paper will undermine the foundational layers of DIKW, so wisdom could not be an upper layer in the DIKW hierarchy. And there's a question comes to me, why we care much about the DIKW hierarchy? Although in the information field, people make their research on data, information, knowledge, how this DIKW hierarchy work in our practical research?
Q2, On the page of 135, the author mentions that a better methodology is more top-down and just-in-time and a good theory of questions may delimit exactly the information needed to answer a particular question, hence the raising of a question will itself direct the search for information, observations, or data. What is when we looking for an answer, there is no proper data or observations useful. Is that a waste of time to collect data at the time when we need the information? And about the supermarket example the author mentioned, as far as I think, it might be useful when looking for data about a patient's regular diet--the data is useful in the certain context.
Q3 On page 137, the author gives distinction of know-that and know-how. The author stated that know-hows might be articulated as procedural rules. And other know-hows cannot really be explicitly recorded. Does this kind of know-hows mean wisdom on some level? Something that has graved in our mind and we react without thinking when doing so?
1. I am glad to see the author doubt the DIKW hierarchy. I don’t think the DIKW hierarchy is appropriate since I don’t agree with its relationship between information and knowledge. But I also believe there are some reasons why this hierarchy exists. The author says that the DIKW hierarchy “should no longer be part of the canon of information science, and such related disciplines as systems theory…”(p132). Isn’t it a little too much? I mean many of these disciplines are the foundation of information science.
2. When the author talks about “how sound or desirable this hierarchy is”, he gives us an example about data,” day 1 has maximum temperature 82 degrees...”. He says the statements “are going to be true or false”, but “for ‘data’ in the sense used here, the statements will have to be true” (p134). I don’t think so. I think these statements are information and this information contains other data like ‘day 1’, so whether these statements are true or false depends on whether the ‘day’ matches the ‘temperature‘.
3. The author says that “almost all of science is information, but, in most contexts, it is not data” (140). I think it may be right, but I still believe information is formed with data and it is subjective. So what do you think about that?
In this article the author attempts to disassemble the DIKW pyramid by pointing out the flaws in its base ideas of data, information, and knowledge. He does this by assuming that the DIKW pyramid operates on a very strict definition of the terms contained within. However, in the Zins article we saw that there are many different definitions of data, information, and knowledge. Does the fact that there are so many different definitions of data, information and knowledge undermine the arguments that he makes or are his assertions still valid?
The author of this article describes two different types of knowledge that he considers "know-how" knowledge. He states that some some know-how knowledge can be written down, like how to solve a math equation, and that some know-how knowledge cannot be recorded, like how to ride a bike. Is this assertion valid? Is there a way to write down how to ride a bike, perhaps with pictures or video, that could describe the know-how knowledge of riding a bike? Is it possible to be so practiced at performing a math equation that it becomes automatic and you cannot describe how you do it?
In this article the author discusses the idea of whether data must be true or false. He asserts that data must always be true and that data that is not true is not in fact data. However in the Buckland article the author states that anything must be information-as-thing(data) because it may be relevant to something that we do not know about yet. If this is true then is all data that Frické discusses true because it is relevant to a query that he has not made yet? Is it possible that data is not true or false and that these judgements are more important for Frické's idea of information because they are relevant to some form of question about data?
1. Though the discussion of wisdom here is brief (as in past articles), I found it to be really helpful. In particular, I like that the concept of wisdom includes a call to "act in accordance" (p. 141). Would inserting the idea of action and/or observable behaviors into the discussions around DIK be useful as well? 2. I find using terms like truth, weakness, and fallible, to be problematic. These are value terms, where the value and perspective they carry has not been acknowledged. Who's truths and weaknesses are we talking about? In what kind of culture or environment is this fallible? I've been disappointed that the readings to-date fail to talk about the Western, white cultural bias that is incorporated into these frameworks of understanding. 3. Fricke states that "Data itself is of no value until it is transformed into a relevant form" (pg. 133). What is responsible for this kind of transformation--is it human experience?
1. On page 135, Fricke writes that “the earth goes around the sun is not data nor can it be inferred from data.” I am confused by this statement, because it would seem to me that the information that the earth goes around the sun could only come from data that had been specifically collected about the movement of the planets. How is that fact not inferred from data?
2. Fricke describes know-how and know-that types of knowledge on page 136. He writes that know-that knowledge “does not belong in DIKW.” It seems to me that know-that statements are some kind of knowledge or information. Is it fair to leave know-thats out of the hierarchy or is there a place for them?
3. On page 138 Fricke writes that statements with weaker assertions are more likely to be true, and that data should not be too strong or too weak. How is it possible or bad for data to be too strong?
1. The writer asserts that inaccurate or mistaken data is not going to be data at all. However, data is collected not only for analyzing the past but also to predict the future. For example, accountants predict a company sales of the next quarter based on the data of past quarters, and they also use the result to estimate the annual income. It is a process of using inaccurate or mistaken data,isn't it?
2. On page 136, the writer arises a seemingly persuasive example, that the earth goes around the sun is not data nor can be inferred from data; it is not, and could not be, DIKW information. However, I don't hold with his point of view. As we know, the alternation of day and night have been observed and recorded by people in the past for hundreds of years, and Copernicus showed that the earth moves round the sun after having advanced telescopes. In other words, this statement came from a process of continually adding data, and finally became private knowledge, and then universal knowledge as well.
3. One standpoint discussed in this article is that data is required to be true, and so, statements inferred from data will have to be true. And my question is: Does this true or false problem exist in the wisdom level as well? Is it possible for us to judge or measure wisdom?
1. On pg. 138 of his article, Fricke warns us: “Do not suppose that there is a special category of ‘data’ which can serve as the bedrock for all else.” It makes me wonder, are there people who look at the DIKW hierarchy and actually suppose that? I’m having a hard time figuring out what it is Fricke thinks we’re doing with the DIKW hierarchy. After so many readings, I’m not sure I know what we do with it any more. What is it, exactly, that information scientists do with the DIKW pyramid? What is its purpose?
2. Fricke states that one question he addresses in his paper is “whether DIKW is a useful and intellectually desirable construct to introduce.” He discusses ways in which DIKW can be confusing and how this could possibly be an argument for dropping it from Info Science canon, but he doesn’t consider how it can be clarifying, even on some basic level. For someone new to the field, distinguishing information as a concept between data and knowledge can be a helpful first step to comprehending its complexities.
3. Having read a more thorough analysis of DIKW by Zins and a more systematic approach to data and information by Buckland, what can we learn from this text? I feel like the author sees that, as a concept in itself, DIKW is overly simplistic and vague, but fails to recognize that DIKW’s inherent problems also serve to push scholars to ponder the stuff of their studies more deeply.
1. On page 134 Fricke discusses how if we don’t have data to back theoretical information it does not fit into the DIKW hierarchy. When we start to collect observable data on unproven theories wouldn’t this reinforce the DIKW hierarchy?
2. On page 140, Fricke states that “information is irreducible to data”, but Buckland states that objects just as data and documents are referred to as information because they are informative. So is “information-as-thing” an argument that helps Fricke argue against the DIKW hierarchy?
3. Fricke states that “librarians often use ‘knowledge’ and ‘information’ as synonyms. Does your profession within the information science community have a profound effect on your distinctions between data, information, knowledge and wisdom?
1. Frické's critique of the omission of "why" in Ackoff's list of information-seeking questions bothered me. In particular, his example ("Yet it is completely natural for inspectors of an airplane crash, for example to search for the information telling why the accident occurred" [135]) doesn't involve a why question at all; rather, inspectors seek information as to how the crash might have occurred. Furthermore, should the information professional concern him/herself with "why" questions, or would this rather fall under the fields of philosophy or theology?
2. Frické asserts that false inferences can be drawn from true data, and that this presents a problem for the DIKW hierarchy--either inference cannot be data information at all or the hierarchy must not necessitate true information. My question is, then, why might it be such a big deal for misinformation to find a place in the hierarchy? If the system is in place to guide understanding of how data are processed to become actionable knowledge and wisdom (whatever that may be), is it not then valid to apply the system to mistaken or false data and inferences and follow that through the system?
3. Frické includes Austin's warning that "results from data mining should be treated with scepticism" (135). Is there merit in collecting information in hopes that it might one day be useful/transcend to information and knowledge? The examples discussed are benign--the collection of weather data, for instance. However, much more "useful" information can be collected, like the metadata collected by the NSA. Dismissing the glaring ethical questions, how do members of the class feel about this as a practice?
1 - Does the notion of truth presented on 133 (and critiqued on 134) privilege a Western-rational, ahistorical view of existence? I'm thinking of this primarily in relation to subjective experience, and how, realistically, the default experience (speaking from a US perspective) privileges the existence of the White Western cis-male as the 'default' and therefore most true and rational experience. How does structuring information around this notion of truth limit the information we deem valuable, and how do we expand the definition to avoid these limitations?
ReplyDelete2 - In my opinion, the main takeaway from Fricke's article is that the DIKW hierarchy falls short because it presents a limiting view of what 'information' can be. As someone interested in archives, I think a particularly valid part of our role is to document subjective, lived experiences - does moving away from, or expanding beyond, the DIKW hierarchy allow for these lived experiences to become 'information' or should we classify them as something separate?
3 - If we follow Fricke's analysis of wisdom on 140, is wisdom something that can be taught? My interpretation is that wisdom for Fricke is somewhat equivalent to critical thinking mixed with one's own moral and ethical decision-making capabilities: if that's the case, I don't think wisdom really plays, or can reasonably play, an important role in the study of information.
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ReplyDelete1. In page 5, Martin Fricke points out that data cannot be inaccurate or mistaken. If that is true, then how is it possible that there are processes like data cleansing or data scrubbing? If the data of a temperature of a region denotes a temperature reading that is false, what do we refer the inaccuracy as? And is it feasible to put forth that data has no value at all? According to Rowley, data has the least value in the information hierarchy. How can this contradiction be validated?
ReplyDelete2. The author argues that the DIKW schema does not support the answering of the ‘why’ question, which is mainly inductive reasoning. In this case, if there had to be statements to validate a point should there be an additional stage of processing between data and information that takes the role of logically evaluating the final information?
3. Fricke quotes Floridi’s writing which states that information is polymorphic and poly semantic.
Polymorphism is defined as existence in different forms. How can information acquire different forms, if it processed data can there be different types or levels of processing so as to deliver different pieces of information from the same set of data? If that is done, will it not cause randomness in the upper levels of the hierarchy?
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ReplyDelete1. I am wondering if there has been any work that fills the gap between the cognitive sciences and the information field. For example, as cognitive science discovers more about how the brain processes information and where memory resides, should the information field be modifying its practices to be more brain-compatible? As we think about how humans might use or process “declarative knowledge” into “inexpressible procedural knowledge” does this suggest how the field should facilitate this process?
ReplyDelete2. While I agree with Frickè’s arguments that the DIKW hierarchy is philosophically unsound, I am also wondering if there may be times when it could be useful in a more mundane or practical sense? I am thinking specially about using the hierarchy model in a less literal way and more as a visualization, for example, to help teachers form a more practical definition of the differences between data, information, and knowledge. Do you think this could help teachers identify the distinction between overloading students with information and imparting some useful declarative knowledge in the classroom?
3. I love the way that Frickè’s ends this article by dismantling the relationship between wisdom and “truth” by stating that wisdom is the extensive collection of appropriate and practical knowledge. I am curious if after reading this, if anyone believes that there can be any objective “truths” contained in data, information or knowledge? I am also curious if anyone has done additional work that builds on Friké’s arguments by creating a new model for looking distinctive qualities of data, information, knowledge, and wisdom and if so, what these models might look like?
1. I like this paper because it revealed the sufficient parts of DIKW model. The first question is how will the supporters response to the statements like "Most of the snakes are dangerous.", as mentioned in the paper? Such a statement is a kind of information for sure while it is a useful knowledge as well. Or this means information and knowledge are overlapped in the DIKW model?
ReplyDelete2. In the paper, the author claimed that DIKW model might have side effect on data analysis due to it encourages people collect data as much as they can. This assertion is unreasonable for me. Though I highly doubt the usage of DIKW model, I believe data is the foundation of our world and , of course, is the basis of information. My question is what kind of the relationship between data and information is sound to the author of this paper based upon his description in the paper?
3. I agree with most of the ideas that Frickè arises in the paper, however, I think a good critique essay can build a better solution as well as finding the problems in the old one. In this case, base upon the problems that the author revealed, what is the better model in the author's mind to describe the relationships among data, information, knowledge and wisdom?
1. Fricke suggests that DIKW theory “encourage uninspired methodology.” This can include collection of vast amounts of non-useful data to hope that it will one day ascend to information. (Fricke, 135) Several services on the web serve specifically to archive older creations of the web, newsgroup postings, emails given to them, and other parts of the enormous amounts of human data that we now generate. Being electronic, this data would be lost if not specifically preserved. Is it unsound methodology to preserve it as a historical record, without immediate research goal?
ReplyDelete2. One argument to be made against the blind collection of data is a basic privacy objection. When dealing with enormous collections of unsorted data, it is easy to trawl in items that might embarrass or damage a person, whether they were the copyright owner or not. How should one address this side effect of data mining? At present, our tools for manipulating such enormous fields are limited and often in use by restricted entities.
3. At the conclusion of the paper, Fricke states that “The DIKW account of wisdom, in its Ackhoff version, is reasonably in harmony with [this],” this being Fricke’s own statements about wisdom, of its broader incorporation of information or weak-knowledge into knowledge-that, and being able to hold this in a broader ethical framework. (Fricke, 140). Are these notions of wisdom truly compatible, or is wisdom itself still too vaguely defined to truly debate definitions?
1. In his article, Frické spends a good amount of time differentiating between what is and what isn’t data. He states regarding the accuracy of data that, “inaccurate or mistaken data is not going to be data at all” (4). However, is this really true? Just because the data is incorrect, does that really negate the fact that it is data? Or is it more correct to believe what Frické adds near the end of the article – that “data needs to be true” (7)?
ReplyDelete2. Frické also claims that the DIKW pyramid “encourages the mindless and meaningless collection of data in the hope that one day it will be ascend to information” (5). I understand that while continual collection of data may seem “mindless” or “meaningless”, how does anyone (especially Frické) know that said collection is worthless? One never knows when a certain piece of data is going to be needed. Something that seems trivial may be to another person of the utmost importance. And, if that’s the case, is a “mindless” collection of data really “meaningless”?
3. In Frické’s article, he seems to advocate a view of the relationship between knowledge and information that would allow “knowledge and information [to] collapse into each other” (11). However, are the terms really so synonymous that one essentially means the other? If a person is given a random piece of information, are they really being given knowledge? Or does knowledge come after the information has been processed and becomes useful?
1. On page six Fricke says “the dilemma is either DIKW does not permit inductive or similar inference, in which case statements like ‘most rattlesnakes are dangerous’ cannot be information or DIKW does permit inductive inference in which case it abandons its core faith that data and information have to be rock solid true.” Why can’t the DIKW hierarchy embody both? Part of obtaining knowledge is that sometimes your first set of data or information is not always right and from those inaccuracies can come a newly constructed answer that might be what one is looking for.
ReplyDelete2. Again on page six Fricke says that “the assertion being made here is that collecting data blind is suspect methodologically.” He then quotes Popper who says that “the belief that we can start with pure observations alone, without anything in the nature of a theory, is absurd,... How is approaching something you know nothing about, ex) parachute journalism, a bad way to collect data? Some of the greatest discoveries of data and information have happened by pure chance or accident. First observations are extremely valuable as they can lead to further and better research.
3. In describing the relationship between data and information on page 11, Fricke says that “all data is information. However, there is information that is not data. Almost all of science is information, but in most contexts, it is not data.” So what then is the difference between data and information? They seem to definitely have a symbiotic relationship. Returning to “there is information that is not data,” how is that possible? All data is information, how can there be information that isn’t data?
1) Fricke mentions on page 133 'while wisdom is traditionally taken to be a layer in the hierarchy, few authors use or discuss it. This may be because it is not required for the problems they address'. Could that be where part of the overall problem with the hierarchy? Since all these definitions are so subjective and contradictory, doesn't it add to the confusion when wisdom is shoved in at the top with no real in-depth discussion. About where it fits and how?
ReplyDelete2) How do speculative questions/theories in science fit within DIKW? Moreover, why doesn't Information Science mirror science's philosophy on information?
3) If people are intent on keeping the DIKW hierarchy as a part of the world of Information Science, why is there no push to make it more flexible or accommodating to different types of data and information that are somewhat to completely intangible? Fricke quotes on page 140 stating "It is hardly to be expected that a single concept of information would satisfactorily account for the numerous possible applications of this general field". If this is so, why push to have a concrete definition within the field, when we probably need to focus on a succinct way to describe it to people outside the field?
1) I agreed with Frické’s misgivings about the unidirectional DIKW hierarchy and its apparent failure to account for the role of prior knowledge (or even wisdom) in allowing for a definition of data as purposefully-gathered observations. That said, I am not sure I agree that the DIKW hierarchy does not allow for data to be purposefully-gathered at all. Frické asserts that “that the earth goes around the sun… [cannot] be inferred from data” (135); this would seem to imply that the scientific observations of astronomers like Galileo do not constitute “data” because they were gathered in purposeful support of a theory. Is this a fair reading of Frické? Is his definition of “data” too narrow?
ReplyDelete2) Frické reintroduces the philosophical distinction within knowledge of “know-that” and “know-how,” with “know-that” knowledge being easily transferrable and storable and “know-how” being internal and inexpressible. I am interested in how he would account for what I would term “know-of,” which would be a knowledge of the processes by which one “knows that” or “knows how.” (For instance, “know-of” would include the knowledge that one’s observations of the world are hindered by sensory and instrumental limitations.) Is “know-of” a form of knowledge? Would this instead fold into Frické’s definition of wisdom?
3) This article made good points about how questions of “why” are eliminated from the DIKW hierarchy because the hierarchy relies on so-called “objective” truth rather than sound conjecture. Why might information professionals (and scholars in general) still be susceptible to problematic beliefs in certainty and objectivity? Is this problem more pervasive in the sciences than it is in the humanities?
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ReplyDelete1. In the part "How sound or desirable is this hierarchy", the author believed "there is information about what to us is the dark world"(which I agree), and this information does not rest on data. However, even if there is the huge domain of the unobservable for which no instruments of measurement exist now, we cannot assert that there will not be any instrument in the future. I wonder could the potential of measuring be evidences "the dark world" is still resting on data.
ReplyDelete2. It seems to be reliable that data needs to be true. However, is the mistaken data not one kind of data? I do not think so. In my view, data has no meaning: we tag it to turn it into information. As the author's example of temperature, I think 87 degrees can be information, "degree" is a tag, which imply "temperature", and the number "87" is the data, so how can "87" be true or false. There is a huge amount of data stored in the computer which even has no meaning and useless, how can this data be true or false? Since "data is true" seems to be the foundation of this article, I'm not sure did the author lead an over complicated meaning of data.
3. Discuss the concepts of weak knowledge and strong knowledge, I wonder are there standards to distinguish them? Do they have overlap between each other? Can they transform to each other? How to transform? When I read some specialized "strong knowledge" on the book but cannot deeply understand it, can I classify it as information (weak knowledge)?
1. In this article, as in almost all of the other articles we have read, wisdom is, again, only mentioned briefly. Because this article is a critique of the DIKW hierarchy, I would have liked them to discuss why wisdom was included in the first place? And if authors/scholars can pick and choose what they want from the pyramid anyways, then why use it as an accepted standard?
ReplyDelete2. I felt that the article was interesting, but focused too much on data, whereas other articles using the DIKW pyramid focused primarily on defining information and knowledge. Was the intent of the critique to discredit data as a building block so that the whole pyramid collapses? Isn't the pyramid just a theoretical approach to the structure of information and friends? It seems that even the critics of the DIKW pyramid accept it as the standard. Is this because there isn't another model to assess?
3. In the article, on page 134, Fricke mentions the origins of the DIKW pyramid theory and the now discredited theories it was based on. From a historical standpoint, if our predecessors had succeeded on pinning down definitions for information, knowledge, etc., would it have permitted the field to grow as it has? It seems that every article is bent on defining these terms, but in doing so, would very clearly go against the amorphous nature of information. So why the motivation to provide definitions? I understand the need to work from an established structure, but information is hard to pin down for a reason, or else it wouldn't be information.
-I’m a bit confused by Fricke’s proposition that data is, by definition, accurate. He then admits that that cannot be guarunteed and that data is “fallible and conjectural” (137).So what is untrue data if not (just like true data) some of the building blocks of information, knowledge and wisdom? And how can data ever really be data if by nature it is relative or, like a measurement, never specific enough to truly be accurate?
ReplyDelete-Fricke goes on to announce that “all data is information. However, there is information that is not data” (140). I like the idea that information goes beyond data, that it becomes more than the sum of its parts and disrupts the concept of a DIKW pyramid. So if we are willing to go this far with Fricke, do we then believe that information and knowledge are synonomous? Or is knowledge still a step beyond information?
-Fricke announces that there are many different senses of information and none of them is entirely sufficient to be used across the entire field of information science. So why is he still trying to propose his own definition, wider and more inclusive though it may be? Why not revel in ambiguity?
1. “The dilemma is: either DIKW does not permit inductive, or similar, inference, in which case statements like ‘most rattlesnakes are dangerous’ cannot be information or DIKW does permit inductive inference in which case it abandons its core faith that data and information have to be rock solid true. (p 135)” But isn’t the core concept of knowledge or wisdom to be when you apply previous learning or experiences to make extended understandings? So if one understands one rattlesnake to be dangerous, one can understand through reason that most rattlesnakes could also be dangerous, correct?
ReplyDelete2. With ‘big data’ (one of the hottest topics in information science right now), we have large quantities of data that is being collected blindly, hoping for analysis later to find meaning. At the moment, the collectors don’t necessarily know what they are looking for so they are simply trying to collect everything; essentially, if you don’t have the data collected then you might miss opportunities to later find meanings. For example, if you are not collecting web analytics now, then you can’t compare user patterns. You need to have history to make the comparison, even if at the outset one might not know what it is that one would like to compare.
3. I am not sure if I’m completely understanding Fricklé’s agreement “Data typically is expressed by existential-conjunctive logic, information requires the full first order logic. (p. 140)” So data is recorded with a set of circumstances attached to it – such as on a certain date and a certain time the temperature was X? I don’t understand how information compares here.
1. p.136 "So much for data and information in the DIKW hierarchy. The pyramid has no foundations." Is there something about reviews that allow you to write with a human voice?
ReplyDelete2. p.139 "...data...is that of anything recordable in a database in a semantically and pragmatically sound way." What is a database? Are telephone books databases?
3. p.141 "Knowledge was know-how, knowing how to control the systems. Then wisdom was merely a matter of using that practical know-how to achieve appropriate ends. That is a reasonably defensible view - it just does not want to be embedded in the DIKW hierarchy." Have any theories started with wisdom?
1. The author points out that information is logically stronger than data. What about emotions and intuitions, which come out without much support of logic? Should they be accounted as information as well?
ReplyDelete2. The author discusses wisdom in the end and lists different characteristics of it. Would these characteristics vary culture to culture, community to community?
3. The author states that there is information that is not data. Information can be about pure logic. But is there is information that cannot be traced (logically deducted) back to data? Namely, is there information that has no root in data?
1. "...there are instruments to detect some unobservable entities" If there are instruments to detect these unobservable entities, then are they not being observed? For that matter, how is it known that unobservable entities exist if they can not be observed?
ReplyDelete2. Frické's dilemma about inductive inference seems to lean too heavily on the idea that everything can be known. If the idea that everything is information or everything can be informative is accepted then everything must be known before statements like 'most rattlesnakes are dangerous' can be made. It is not possible to know everything and therefor making inferences based on what can be known is the only reasonable solution.
3. Frické's issue with the DIKW hierarchy seems to stem from a fact that was proven in Zins article. There are many definitions for the building blocks of IS and because of this, the relationships between the building blocks are debatable and unclear. Given that the field of IS is built upon blocks that which no one can agree unanimously on the specifics of, it is a given that the issues that Frické has with the DIKW hierarchy will exist.
1. Fricke makes the claim that the DIKW pyramid should be abandoned (at least I think that’s his claim), but then goes on to say that “discarding the DIKW would leave an intellectual and theoretical vacuum over the nature of data, information, knowledge and wisdom”. These statements seem to contradict one another. Could someone please clarify what Fricke means? Perhaps I’m interpreting the second statement wrongly.
ReplyDelete2. Many of the articles thus far, including this one, disregard wisdom, although Fricke does attempt at the end to address it in slight detail. Could we potentially remove wisdom from the hierarchy since no one seems to want to define it, or is it essential? If Fricke’s argument is to discard the hierarchy entirely, then he probably doesn’t care either way.
3. Is Fricke arguing that weak-knowledge, and especially weak-knowledge-that, is better, more accurate and more certain than stronger knowledge statements?
1.Martin criticized the mindless and meaningless collection of data. But was he too subjective? In reality, we truly tried to collect the data for a data warehouses. Sometime data seem no value now could make a big difference in the future.
ReplyDelete2.In the 3rd chapter, Martin assumed that the DIKW view had excluded the unobservable data from being any part of ‘Information’. So here comes the question, does the view of the DIKW hierarchy really exclude these kind of data?
3.Martin mentioned only data, or statement inferred from data, can be information. He also asserted that inaccurate or mistaken data are not going to be data at all. But sometime, we are unable to confirm whether former data are right or not. So how could we deal with this part of the data?
Frické states that data in the DIKW pyramid rests on data that he points out is “roughly, the outcome of measurements by instrument.” By doing this he is placing a purely empirical slant to data and ignores some of the thoughts put forth in the Zin article which, while some panelists mention data as measurable also mention data as symbols and representations which can then be interpreted in some manner. By making a choice to place data in this empirical box, as it were, does Frické have a point about data or is he simply ignoring the various thoughts about the definition of data/datum?
ReplyDeleteFrické notes that a core faith of DIKW is that data and information have to be rock solid true(135). Is this always the case? Or does this change from moment to moment when new data and information is gained?
With the amount of information or data currently available at any given moment does the collection of large amounts of data without a solid plan in mind make for shoddy methodology as Frické says? Or would collecting data allow for patterns or understanding to emerge at a given moment, e.g., when collecting data from social media?
1. On page 135, paragraph five, Frické claims that the DIKW theory "seems to encourage uninspired methodology," saying it encourages "mindless and meaningless collection of data in the hope that one day it will ascend to information--pre-emptive acquisition." Though this data-preservation "overkill" is certainly worth serious consideration, especially in light of preservation selection issues faced by archivists, his example also contains one of its own possible refutations. He talks about data-mining and scoffs at companies' practice of recording customer's purchases as an example of "meaningless collection of data." However, isn't it the case that there IS most definitely a point to this type of massive data collection: it enables a business entity to improve their profit ratios when placing orders, more selectively (and thus cost-efficiently) market to customers, and see larger sales trends? I don't know that I can agree with Frické that complete data collection and preservation would be useless, if such a thing were ever technically possible; isn't it the case that unknown future entities may be able to use what we may consider "meaningless" data in heretofore undreamt-of ways?
ReplyDelete2. I think one of Frické's main points, which he summarizes in the final paragraph of section 3, near the end of page 136, is that the DIKW model goes about making data useful in a backward way. Isn't Frické ultimately arguing that "know-that" knowledge emerges from "know-how"? Is this argument a parallel to his semantic vs. syntactic discussion on page. 139, where semantic refers to meanings, while syntactic refers to formal order and relations? Would it be possible for us to make a chart of some of his dualistic assertions, to better understand his larger philosophy?
3. Maybe I'm a dense positivist, but I don't understand Frické's claim on page 135 that "that the earth goes around the sun" cannot "be inferred from data. How else did Copernicus come to his conclusion? Certainly not from common sense, but, rather from measurements and the ensuing data, in this case, trigonometry...? Right?
1. Apparently, in this article the author aimed to abandon the DIKW pyramid theory and make it no longer is part of canon of information science field. So does the author share some common opinions with in the DIKW theory?
ReplyDelete2. The author talked about the authenticity of data (true or false) and emphasized that inaccurate or mistaken data is not going to be data at all. So my curiosity here is that the only way to judge whether the data is mistaken or not is to be processed by human`s justification. And the author also pointed out when the data is processed into an answer to an enquiry, the data becomes information. In the situation I just raised, the data is processed to answer whether it is mistaken or not, so is there any conflicts coming out?
3. There is a dilemma being raised- either DIKW does not permit inductive or similar inference, in which case statements, or DIKW does permit inductive inference is which case it abandons its core faith that data and information have to be rock solid true. So where is the dilemma derived from? Is that from the new definitions of data and information?
Q1 In the second part of the article, p132-134, the author talked about what is DIKW based on other scholars. And in the end, the author comes to wisdom, a category given only limited discussion in the present paper. Then the author concluded that since the criticism offered in this paper will undermine the foundational layers of DIKW, so wisdom could not be an upper layer in the DIKW hierarchy. And there's a question comes to me, why we care much about the DIKW hierarchy? Although in the information field, people make their research on data, information, knowledge, how this DIKW hierarchy work in our practical research?
ReplyDeleteQ2, On the page of 135, the author mentions that a better methodology is more top-down and just-in-time and a good theory of questions may delimit exactly the information needed to answer a particular question, hence the raising of a question will itself direct the search for information, observations, or data. What is when we looking for an answer, there is no proper data or observations useful. Is that a waste of time to collect data at the time when we need the information? And about the supermarket example the author mentioned, as far as I think, it might be useful when looking for data about a patient's regular diet--the data is useful in the certain context.
Q3 On page 137, the author gives distinction of know-that and know-how. The author stated that know-hows might be articulated as procedural rules. And other know-hows cannot really be explicitly recorded. Does this kind of know-hows mean wisdom on some level? Something that has graved in our mind and we react without thinking when doing so?
1. I am glad to see the author doubt the DIKW hierarchy. I don’t think the DIKW hierarchy is appropriate since I don’t agree with its relationship between information and knowledge. But I also believe there are some reasons why this hierarchy exists. The author says that the DIKW hierarchy “should no longer be part of the canon of information science, and such related disciplines as systems theory…”(p132). Isn’t it a little too much? I mean many of these disciplines are the foundation of information science.
ReplyDelete2. When the author talks about “how sound or desirable this hierarchy is”, he gives us an example about data,” day 1 has maximum temperature 82 degrees...”. He says the statements “are going to be true or false”, but “for ‘data’ in the sense used here, the statements will have to be true” (p134). I don’t think so. I think these statements are information and this information contains other data like ‘day 1’, so whether these statements are true or false depends on whether the ‘day’ matches the ‘temperature‘.
3. The author says that “almost all of science is information, but, in most contexts, it is not data” (140). I think it may be right, but I still believe information is formed with data and it is subjective. So what do you think about that?
In this article the author attempts to disassemble the DIKW pyramid by pointing out the flaws in its base ideas of data, information, and knowledge. He does this by assuming that the DIKW pyramid operates on a very strict definition of the terms contained within. However, in the Zins article we saw that there are many different definitions of data, information, and knowledge. Does the fact that there are so many different definitions of data, information and knowledge undermine the arguments that he makes or are his assertions still valid?
ReplyDeleteThe author of this article describes two different types of knowledge that he considers "know-how" knowledge. He states that some some know-how knowledge can be written down, like how to solve a math equation, and that some know-how knowledge cannot be recorded, like how to ride a bike. Is this assertion valid? Is there a way to write down how to ride a bike, perhaps with pictures or video, that could describe the know-how knowledge of riding a bike? Is it possible to be so practiced at performing a math equation that it becomes automatic and you cannot describe how you do it?
In this article the author discusses the idea of whether data must be true or false. He asserts that data must always be true and that data that is not true is not in fact data. However in the Buckland article the author states that anything must be information-as-thing(data) because it may be relevant to something that we do not know about yet. If this is true then is all data that Frické discusses true because it is relevant to a query that he has not made yet? Is it possible that data is not true or false and that these judgements are more important for Frické's idea of information because they are relevant to some form of question about data?
1. Though the discussion of wisdom here is brief (as in past articles), I found it to be really helpful. In particular, I like that the concept of wisdom includes a call to "act in accordance" (p. 141). Would inserting the idea of action and/or observable behaviors into the discussions around DIK be useful as well?
ReplyDelete2. I find using terms like truth, weakness, and fallible, to be problematic. These are value terms, where the value and perspective they carry has not been acknowledged. Who's truths and weaknesses are we talking about? In what kind of culture or environment is this fallible? I've been disappointed that the readings to-date fail to talk about the Western, white cultural bias that is incorporated into these frameworks of understanding.
3. Fricke states that "Data itself is of no value until it is transformed into a relevant form" (pg. 133). What is responsible for this kind of transformation--is it human experience?
1. On page 135, Fricke writes that “the earth goes around the sun is not data nor can it be inferred from data.” I am confused by this statement, because it would seem to me that the information that the earth goes around the sun could only come from data that had been specifically collected about the movement of the planets. How is that fact not inferred from data?
ReplyDelete2. Fricke describes know-how and know-that types of knowledge on page 136. He writes that know-that knowledge “does not belong in DIKW.” It seems to me that know-that statements are some kind of knowledge or information. Is it fair to leave know-thats out of the hierarchy or is there a place for them?
3. On page 138 Fricke writes that statements with weaker assertions are more likely to be true, and that data should not be too strong or too weak. How is it possible or bad for data to be too strong?
1. The writer asserts that inaccurate or mistaken data is not going to be data at all. However, data is collected not only for analyzing the past but also to predict the future. For example, accountants predict a company sales of the next quarter based on the data of past quarters, and they also use the result to estimate the annual income. It is a process of using inaccurate or mistaken data,isn't it?
ReplyDelete2. On page 136, the writer arises a seemingly persuasive example, that the earth goes around the sun is not data nor can be inferred from data; it is not, and could not be, DIKW information. However, I don't hold with his point of view. As we know, the alternation of day and night have been observed and recorded by people in the past for hundreds of years, and Copernicus showed that the earth moves round the sun after having advanced telescopes. In other words, this statement came from a process of continually adding data, and finally became private knowledge, and then universal knowledge as well.
3. One standpoint discussed in this article is that data is required to be true, and so, statements inferred from data will have to be true. And my question is: Does this true or false problem exist in the wisdom level as well? Is it possible for us to judge or measure wisdom?
1. On pg. 138 of his article, Fricke warns us: “Do not suppose that there is a special category of ‘data’ which can serve as the bedrock for all else.” It makes me wonder, are there people who look at the DIKW hierarchy and actually suppose that? I’m having a hard time figuring out what it is Fricke thinks we’re doing with the DIKW hierarchy. After so many readings, I’m not sure I know what we do with it any more. What is it, exactly, that information scientists do with the DIKW pyramid? What is its purpose?
ReplyDelete2. Fricke states that one question he addresses in his paper is “whether DIKW is a useful and intellectually desirable construct to introduce.” He discusses ways in which DIKW can be confusing and how this could possibly be an argument for dropping it from Info Science canon, but he doesn’t consider how it can be clarifying, even on some basic level. For someone new to the field, distinguishing information as a concept between data and knowledge can be a helpful first step to comprehending its complexities.
3. Having read a more thorough analysis of DIKW by Zins and a more systematic approach to data and information by Buckland, what can we learn from this text? I feel like the author sees that, as a concept in itself, DIKW is overly simplistic and vague, but fails to recognize that DIKW’s inherent problems also serve to push scholars to ponder the stuff of their studies more deeply.
1. On page 134 Fricke discusses how if we don’t have data to back theoretical information it does not fit into the DIKW hierarchy. When we start to collect observable data on unproven theories wouldn’t this reinforce the DIKW hierarchy?
ReplyDelete2. On page 140, Fricke states that “information is irreducible to data”, but Buckland states that objects just as data and documents are referred to as information because they are informative. So is “information-as-thing” an argument that helps Fricke argue against the DIKW hierarchy?
3. Fricke states that “librarians often use ‘knowledge’ and ‘information’ as synonyms. Does your profession within the information science community have a profound effect on your distinctions between data, information, knowledge and wisdom?
1. Why does Frické feel that with the DIKW hierarchy, data is the "outcome of measurements by instruments?"
ReplyDelete2. Explain what the author means by existential-conjunctive logic and first-order logic.
3. Frické feels that blind data collecting is faulty methodology while Buckland feels that everything could be considered data. Who is right? Explain.
1. Frické's critique of the omission of "why" in Ackoff's list of information-seeking questions bothered me. In particular, his example ("Yet it is completely natural for inspectors of an airplane crash, for example to search for the information telling why the accident occurred" [135]) doesn't involve a why question at all; rather, inspectors seek information as to how the crash might have occurred. Furthermore, should the information professional concern him/herself with "why" questions, or would this rather fall under the fields of philosophy or theology?
ReplyDelete2. Frické asserts that false inferences can be drawn from true data, and that this presents a problem for the DIKW hierarchy--either inference cannot be data information at all or the hierarchy must not necessitate true information. My question is, then, why might it be such a big deal for misinformation to find a place in the hierarchy? If the system is in place to guide understanding of how data are processed to become actionable knowledge and wisdom (whatever that may be), is it not then valid to apply the system to mistaken or false data and inferences and follow that through the system?
3. Frické includes Austin's warning that "results from data mining should be treated with scepticism" (135). Is there merit in collecting information in hopes that it might one day be useful/transcend to information and knowledge? The examples discussed are benign--the collection of weather data, for instance. However, much more "useful" information can be collected, like the metadata collected by the NSA. Dismissing the glaring ethical questions, how do members of the class feel about this as a practice?