The paper evaluates the Data-Information-Knowledge-Wisdom (DIKW) Hierarchy. This hierarchy is part of the canon of information science and management. The paper considers whether the hierarchy, also known as the 'Knowledge Hierarchy', is a useful and intellectually desirable construct to introduce, whether the views expressed about DIKW are true and have evidence in favour of them, and whether there are good reasons offered or sound assumptions made about DIKW. Arguments are offered that the hierarchy is unsound and methodologically undesirable. The paper identifies a central logical error that DIKW makes. The paper identifies the dated and unsatisfactory philosophical positions of operationalism and inductivism as the philosophical backdrop to the hierarchy. The paper concludes with a sketch of some positive theories, of value to information science, on the nature of the components of the hierarchy: that data is anything recordable in a semantically and pragmatically sound way, that information is what is known in other literature as 'weak knowledge', that knowledge also is 'weak knowledge' and that wisdom is the possession and use, if required, of wide practical knowledge, by an agent who appreciates the fallible nature of that knowledge.

The present study is part of a project to help lay people distinguish accurate from inaccurate consumer health information on the Internet. 7,8 In a recent study, Impicciatore et al. 4 looked at the accuracy of information on the treatment of fever in children. According to them, "only a few of the [41] Web pages we reviewed gave complete and accurate information for such a common and widely discussed condition." Thus, Impicciatore et al. established that there is a problem with inaccurate consumer health information on the Internet. Design: Several popular search engines (Yahoo, AltaVista, and Google) were used to find Web pages on the treatment of fever in children. The accuracy and completeness of these Web pages was determined by comparing their content with that of an instrument developed from authoritative sources on treating fever in children. The presence on these Web pages of a number of proposed indicators of accuracy, taken from published guidelines for evaluating the quality of health information on the Internet, was noted. Main Outcome Measures:Correlation between the accuracy of Web pages on treating fever in children and the presence of proposed indicators of accuracy on these pages. Likelihood ratios for the presence (and absence) of these proposed indicators. Results:One hundred Web pages were identified and characterized as "more accurate" or "less accurate." Three indicators correlated with accuracy: displaying the HONcode logo, having an organization domain, and displaying a copyright. Many proposed indicators taken from published guidelines did not correlate with accuracy (e.g., the author being identified and the author having medical credentials) or inaccuracy (e.g., lack of currency and advertising). Conclusions:This method provides a systematic way of identifying indicators that are correlated with the accuracy (or inaccuracy) of health information on the Internet. Three such indicators have been identified in this study. Identifying such indicators and informing the providers and consumers of health information about them would be valuable for public health care.

The article considers whether Big Data, in the form of data‐driven science, will enable the discovery, or appraisal, of universal scientific theories, instrumentalist tools, or inductive inferences. It points out, initially, that such aspirations are similar to the now‐discredited inductivist approach to science. On the positive side, Big Data may permit larger sample sizes, cheaper and more extensive testing of theories, and the continuous assessment of theories. On the negative side, data‐driven science encourages passive data collection, as opposed to experimentation and testing, and hornswoggling (“unsound statistical fiddling”). The roles of theory and data in inductive algorithms, statistical modeling, and scientific discoveries are analyzed, and it is argued that theory is needed at every turn. Data‐driven science is a chimera.

The Internet is increasingly being used as a source of reference information. Internet users need to be able to distinguish accurate information from inaccurate information. Toward this end, information professionals have published checklists for evaluating information. However, such checklists can be effective only if the proposed indicators of accuracy really do indicate accuracy. This study implements a technique for testing such indicators of accuracy and uses it to test indicators of accuracy for answers to ready reference questions. Many of the commonly proposed indicators of accuracy (e.g., that the Web site does not contain advertising) were not found to be correlated with accuracy. However, the link structure of the Internet can be used to identify Web sites that are more likely to contain accurate reference information.

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