Puneet Varma (Editor)

Information quality

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Information quality (IQ) is the quality of the content of information systems. It is often pragmatically defined as: "The fitness for use of the information provided."

Contents

Conceptual problems

Although this pragmatic definition is usable for most everyday purposes, specialists often use more complex models for information quality. Most information system practitioners use the term synonymously with data quality. However, as many academics make a distinction between data and information, some will insist on a distinction between data quality and information quality. This distinction would be akin to the distinction between syntax and semantics where for example, the semantic value of "one" could be expressed in different syntaxes like 00001; 1.0000; 01.0; or 1. Thus a data difference may not necessarily represent poor information quality.

Information quality assurance is the process to guarantee confidence that particular information meets some context specific quality requirements. It has been suggested, however, that higher the quality the greater will be the confidence in meeting more general, less specific contexts.

Dimensions and metrics of information quality

"Information quality" is a measure of the value which the information provides to the user of that information. "Quality" is often perceived as subjective and the quality of information can then vary among users and among uses of the information. Nevertheless, a high degree of quality increases its objectivity or at least the intersubjectivity. Accuracy can be seen as just one element of IQ but, depending upon how it is defined, can also be seen as encompassing many other dimensions of quality.

If not, it is perceived that often there is a trade-off between accuracy and other dimensions, aspects or elements of the information determining its suitability for any given tasks. Wang and Strong propose a list of dimensions or elements used in assessing Information Quality is:

  • Intrinsic IQ: accuracy, objectivity, Believability, reputation
  • Contextual IQ: relevance, value-added, Timeliness, Completeness, amount of information
  • Representational IQ: interpretability, format, coherence, compatibility
  • Accessibility IQ: accessibility, access security
  • Other authors propose similar but different lists of dimensions for analysis, and emphasize measurement and reporting as information quality metrics. Larry English prefers the term "characteristics" to dimensions. In fact, a considerable amount of information quality research involves investigating and describing various categories of desirable attributes (or dimensions) of data. Research has recently shown the huge diversity of terms and classification structures used.

    While information as a distinct term has various ambiguous definitions, there's one which is more general, such as "description of events". While the occurrences being described cannot be subjectively evaluated for quality, since they're very much autonomous events in space and time, their description can—since it possesses a garnishment attribute, unavoidably attached by the medium which carried the information, from the initial moment of the occurrences being described.

    In an attempt to deal with this natural phenomenon, qualified professionals primarily representing the researchers' guild, have at one point or another identified particular metrics for information quality. They could also be described as 'quality traits' of information, since they're not so easily quantified, but rather subjectively identified on an individual basis.

    Context of information quality in organizations

    Information quality dimensions are perceived to be differently important by different users. For instance, drawing on Porter's value chain, employees working in primary activities of the value chain as compared to employees working in secondary activities perceive information quality criteria differentially important. As such, primary areas perceive timeliness more important than secondary areas. Further, IT and HR staff perceive security of information more important than other functional areas. However, IT staff perceives completeness as less important than other areas. Thus, it is important for mangers to consider different user perspectives when working on improving information quality.

    The general satisfaction level with the data at hand can also influence the relevance judgment of information quality dimensions. Among the following criteria, security and conciseness were influenced strongest by employees' general satisfaction levels:

  • Accessible
  • Accurate
  • Believable
  • Complete
  • Concise
  • Consistently Represented
  • Secure
  • Timely
  • The eight criteria above have also been mentioned in trade-off relationships in literature. For instance, if management improves security of information, it may need to be traded-off for accessibility.

    Quality metrics

  • Authority/verifiability
  • Authority refers to the expertise or recognized official status of a source. Consider the reputation of the author and publisher. When working with legal or government information, consider whether the source is the official provider of the information. Verifiability refers to the ability of a reader to verify the validity of the information irresepective of how authoritative the source is. To verify the facts is part of the duty of care of the journalistic deontology, as well as, where possible, to provide the sources of information so that they can be verified

  • Scope of coverage
  • Scope of coverage refers to the extent to which a source explores a topic. Consider time periods, geography or jurisdiction and coverage of related or narrower topics.

  • Composition and organization
  • Composition and organization has to do with the ability of the information source to present its particular message in a coherent, logically sequential manner.

  • Objectivity
  • Objectivity is the bias or opinion expressed when a writer interprets or analyze facts. Consider the use of persuasive language, the source’s presentation of other viewpoints, its reason for providing the information and advertising.

  • Integrity
    1. Adherence to moral and ethical principles; soundness of moral character
    2. The state of being whole, entire, or undiminished
  • Comprehensiveness
    1. Of large scope; covering or involving much; inclusive: a comprehensive study.
    2. Comprehending mentally; having an extensive mental grasp.
    3. Insurance. covering or providing broad protection against loss.
  • Validity
  • Validity of some information has to do with the degree of obvious truthfulness which the information carries

  • Uniqueness
  • As much as ‘uniqueness’ of a given piece of information is intuitive in meaning, it also significantly implies not only the originating point of the information but also the manner in which it is presented and thus the perception which it conjures. The essence of any piece of information we process consists to a large extent of those two elements.

  • Timeliness
  • Timeliness refers to information that is current at the time of publication. Consider publication, creation and revision dates. Beware of Web site scripting that automatically reflects the current day’s date on a page.

  • Reproducibility (utilized primarily when referring to instructive information)
  • Means that documented methods are capable of being used on the same data set to achieve a consistent result.

    Professional associations

    IQ International—the International Association for Information and Data Quality
    IQ International is a not-for-profit, vendor neutral, professional association formed in 2004, dedicated to building the information and data quality profession.

    Information quality conferences

    A number of major conferences relevant to information quality are held annually:

    Data Governance and Information Quality Conference
    Commercial conferences held each year in the USA
    Data Quality Asia Pacific
    Commercial conference held annually or Sydney or Melbourne, Australia
    Enterprise Data and Business Intelligence Conference Europe
    Commercial conferences held annually in London, England.
    Information and Data Quality Conference
    Not for profit conference run annually by IQ International (the International Association for Information and Data Quality) in the USA
    International Conference on Information Quality
    Academic Conference launched through MITIQ held annually at a University
    Master Data Management & Data Governance Conferences
    Six major conferences are run annually by the MDM Institute in venues such as London, San Francisco, Sydney, Toronto, Madrid, Frankfurt, Shanghai and New York City.

    References

    Information quality Wikipedia