Information quality

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Information quality is the measure for the fulfillment of the "totality of the requirements for information or an information product that relate to its suitability for fulfilling given information needs". Statements on the quality of information relate, for example, to how precisely it 'describes' reality or how reliable it is, i.e. to what extent it can be used as a basis for planning one's own actions.

The term data quality (as a quality measure for data ) is very close to 'information quality '. Since the basis for information is 'data', the 'data quality' has an impact on the quality of the information obtained from the corresponding data: No “good” information from bad data.


The quality of information must be distinguished from pure significance ( semantics ) and formal information content ( statistical significance ).

There are a large number of quality criteria, the importance of which depends on the context and use of information and the data on which it is based. Typical, frequently used quality criteria are correctness , completeness, relevance , consistency (e.g. freedom from contradictions) and topicality (especially in news ). These quality criteria are usually significant from the user's point of view. It is therefore important how users of data and systems assess these criteria.

The IQ-Community (Information Quality) considers the quality of information (according to Richard Y. Wang) according to the following categories and dimensions:

  1. Information
    access : system access, access security
  2. Presentation:
    interpretability, comprehensibility, ability to manipulate, integrity and consistency
  3. Information context:
    relevance, additional benefit, topicality, completeness, scope of information
  4. Eigenvalue (inherent or intrinsic dimension):
    correctness, objectivity, credibility, reputation

The German Society for Information and Data Quality (DGIQ) has proposed a German translation based on Richard Y. Wang's rating system. She recommends using this uniformly in the German-speaking area. A graphical overview of the 15 IQ dimensions can be found on the DGIQ website.

To optimize the quality of information in information systems , the quality of individual data sources is assessed using a cost function based on various criteria. Using preferences about the quality criteria, a request to the information system can be optimized so that the answer has the highest possible information quality!

Information quality, like a general concept of quality, can refer to different ideas (according to Garvin's classical classification )

  • product-related; here quality is seen as an inherent property;
  • user-related; the use of the product defines the quality;
  • process-related; compliance with the specification is guaranteed;
  • value-related; creates a relationship between price and quality, for example.

Poor information quality can have far-reaching consequences if it is not recognized early on. Examples:

  • Hotel reservations are not found due to misspelled names.
  • Due to incomplete address information, invoices are sent to the wrong person.
  • Because of translation errors, billions of amounts ( English trillion ) become trillions.
  • Bad creditworthiness due to the use of incorrect initial data in credit scoring .

Quality criteria for data quality differ from those for information quality ; The criteria for data quality are:

  • Correctness: The data must match the reality.
  • Consistency: A data record must not have any contradictions in itself or with other data records.
  • Reliability: The creation of the data must be traceable.
  • Completeness: A data record must contain all the necessary attributes.
  • Accuracy: The data must be available with the required accuracy (example: decimal places).
  • Up-to-dateness: All data records must correspond to the current state of the depicted reality.
  • Freedom from redundancy: No duplicates may occur within the data records.
  • Relevance: The information content of data records must meet the respective information needs.
  • Uniformity: The information in a data set must be structured uniformly.
  • Uniqueness: Each data record must be clearly interpretable.
  • Comprehensibility: The terminology and structure of the data records must match the ideas of the departments.

Measures to improve data quality are, among other things, a. Called data cleansing .

Importance in different areas


Eurostat defines data quality according to the following criteria:

  • Relevance of the statistical concepts (relevance): users, user needs, level of detail and subject
  • Accuracy of the estimation results (accuracy & reliability):
  • Up-to- dateness and punctuality of the data provision (timeliness & punctuality): Time and duration of data acquisition until publication
  • Coherence & comparability of statistics (coherence & comparabilty): Between preliminary and final statistics, annual and intra-year statistics, subgroups (such as region) or time (day) to the overarching term
  • Accessibility and clarity of information: publication of data, method report, completeness (not part of the Code of Practice)

Natural and social sciences

In the natural and social sciences, one speaks of data quality particularly in relation to measurements and surveys . Above all, interference, the precision of the measurement and the size of the database , i.e. the number of measurements or surveys, play a role: The fewer possible interference, the more precise the measurement and the greater the number of measurements, the more accurate the resulting data represent reality and the better the data quality. It is important to remember that good data quality alone is not enough to construct a good model: the interpretation of the data must also be correct, especially with regard to causality .

News agencies and news services

The purpose of news agencies and secret services is to collect and make available information of the best possible quality. Above all, it is crucial that those data that are relevant for the clientele are selected from the amount of available data and that these are brought into a consistent form without distorting the statement. In particular, errors and misinformation are to be excluded, often by checking messages against multiple sources.


In the economy , information quality is of central importance, since on the basis of information z. B. Decisions are made, market opportunities are assessed and negotiations are conducted. All of this can only be as good as the underlying data or information. The term data quality or company-wide data quality is often used as a synonym for “information quality ”; However, data-related quality only refers to the stored content of data, while “information quality” includes additional aspects such as the appropriate selection of suitable data sets, the formation of (partial) sums and / or their representation.

Complex term

Colloquially, the term 'information quality' is often equated with 'high quality'. However, this is only partially correct and requires - similar to other quality terms (e.g. software, water, sound quality) or evaluative statements (such as fast, bright, loud) - to reliably determine quality: the context of use determines, which quality criteria (as a general framework) are relevant and which specific requirements are made for each criterion. The degree of fulfillment of these requirements by the respective information results - in sum - its information quality . The quality of information is therefore always dependent on the context and the user, and must never be assessed 'in isolation for oneself'.

Application context

The point of reference is the 'information' for which the quality statement should apply.

  • What was information requested / expected about?
  • Which details are specifically expected? Only what has been specifically determined can be checked. "Everything about ..." can hardly be assessed.
  • For which information is the quality to be determined? What exactly was delivered?
  • For which user (s) is the information intended? Language, level of knowledge (laypersons, specialists)?
  • What is the purpose of the information? "only interested", purchase decision, need help
  • how important is this purpose? Costs incurred, intended level of investment, vital
  • what is the importance of information quality? What happens depending on the high or low quality? With which quality criteria?

The company as a context

Studies show that there is also a context dependency within organizations. Accordingly, the importance of quality criteria in particular is assessed differently in different value creation areas and departments. Views of primary and secondary activities (according to Porter's Value Chain) can differ considerably. The point of view of IT departments can also be different from the point of view of other departments. This should be taken into account especially for those responsible for information management.

The complexity in the assessment of information quality is shown, among other things, in the fact that users in companies assess information quality criteria differently depending on their general satisfaction with the available data and information. Among the information quality criteria

Accessibility, correctness, credibility, completeness, conciseness, consistency, security and timeliness

One study revealed the greatest differences (depending on satisfaction) in the assessment of importance for conciseness and security.

Quality criteria and their relevance

The information quality results from the review or the fulfillment of relevant criteria. Nohr uses the following "dimensions of quality" (*) :

  • the task relevance and purposefulness of the information: understandable? matching the expectation?
  • the degree of certainty of being true
  • the credibility based on previous experience
  • the verifiability of the information: which sources are known? Are they reliable?
  • the accuracy of the information: is it complete? This criterion is often difficult to check. Is it consistent?
  • and the timeliness of the information

(*) Evaluation criteria for information quality are applied inconsistently . Nohr states: “There is a serious lack of criteria and assessment criteria for the quality of information.” With reference to “Rolph / Bartram 1994”, he refers to the criteria used by British managers: They “assessed the information quality on which their decisions are based overall as rather inadequate with regard to a quality scale comprising eight criteria (1 = poor, 5 = high) ": accuracy 3.64, credibility 3.31, presentation 3.18, timeliness 3.07, completeness 2.88, visible focal points 2, 84, relevance 2.80, usable format 2.80.

Such questions require a more or less detailed examination depending on the context in which the information is used and its quality is to be determined. There are u. Further research may be required - which in turn provides new information (with its own 'information quality').

In this way, the quality criteria can be assessed differently in different information sources / media. For example:

  • Traditional encyclopedias : authority, completeness, format, objectivity, style, timeliness, uniqueness.
  • Web 2.0 services : accessibility, completeness, credibility, commitment, objectivity, readability, relevance, reputation, style, timeliness, uniqueness, usefulness.

Context of meaning

A general and undifferentiated assessment of information quality is therefore not possible, but can only be derived from the degree of fulfillment of the (relevant) requirements. Deficits or gaps represent risks that are higher, the more important the application context is and the more important the potential effects of these deficits are. Under certain circumstances, the requirements / expectations of the information quality must therefore be prioritized / weighted. For example, special evaluation and documentation procedures (such as 'scoring') can be used for information of high importance.
Conversely, in the case of a less (more) important context (such as “I only care”), such considerations take a back seat; information whose up-to-dateness (e.g.) is not known could - in this respect - be certified as being of “good quality” if this criterion is not important or not relevant; because the (defined) requirements would be met. In simple situations, the quality of information may often only be assessed in general terms, 'on the basis of feeling'; The judgment is then based on an intuitive assessment of certain individual criteria, is only partially certain and cannot be justified in this scenario.

Other dimensions

In the literature, further distinctions can be found in connection with information quality:

  • Statements on the quality of information are possible as an evaluation of specific information, but also as a target for information that is potentially expected or to be produced - especially here the quality-determining criteria must be defined more precisely.
  • With a similar meaning, Nohr differentiates between constructive (= quality in the production of information) and receptive (= checking of externally obtained information) information quality .
  • With regard to the requirements on which the quality is based, a distinction can be made between user-specific and general requirements.

See also


  • Jürgen Bode: The concept of information in business administration . In: Schmalenbach's Journal for Business Research, Volume 49, No. 5, pp. 449–468, 1997.
  • Larry P. English: Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits . New York: John Wiley & Sons, 1999.
  • Martin Eppler: Managing Information Quality: Increasing the Value of Information in knowledge-intensive Products and Processes . 2nd revised and extended edition, New York / Berlin: Springer, 2006. ISBN 978-3540314080
  • Gernot Gräfe: Information quality in transactions on the Internet . Wiesbaden: Dt. Univ.-Verl., 2005.
  • Knut Hildebrand, Marcus Gebauer, Holger Hinrichs, Michael Mielke (eds.): Data and information quality: On the way to Information Excellence , Vieweg + Teubner Verlag, Wiesbaden 2008, ISBN 978-3-8348-0321-4
  • Holger Hinrichs: Data quality management in data warehouse systems , Oldenburg 2002 (online PDF )
  • Thomas C. Redman: Data Quality for the Information Age . Boston: Artech House, 1996.
  • Richard Wang, Diane Strong: Beyond Accuracy: What Data Quality Means to Data Consumers. In: Journal of Management Information Systems, Vol. 12, No. 4, pp. 5-33, 1996.
  • Holger Nohr , Information Quality Management, Knowledge Management Working Papers, University of Applied Sciences Stuttgart, No. 3/2001, ISSN  1616-5349 (Internet, PDF ), ISSN  1616-5330 (Print)
  • Carsten Kraus, address and customer databases for direct marketing , Verlag Business Village, Göttingen 2004, ISBN 3-934424-59-7
  • Volker Würthele, Data Quality Metrics for Information Processes, Department of Computer Sciences, Information Systems Department, Eidgenössische Technische Hochschule (ETH) Zurich, Verlag Books on Demand GmbH, Norderstedt 2003, ISBN 3833403454 , (online PDF )
  • Jan Rutenberg: The influence of the quality and quantity of information on mental convenience in purchasing decisions. Publishing house Dr. Kovač , Hamburg 2008, ISBN 978-3-8300-3696-8 .
  • Fehrenbacher, DD , & Helfert, M. (2012). Contextual factors influencing perceived importance and trade-offs of information qualityCommunications of the Association for Information Systems30 , 111-126.

Web links

Individual evidence

  1. a b c d e f Holger Nohr FH Stuttgart Management of information quality (PDF; 362 kB)
  2. a b c d Fehrenbacher, Dennis Dominique, Helfert, Markus: Contextual Factors Influencing Perceived Importance and Trade-offs of Information Quality . In: Communications of the Association for Information Systems . tape 30 , no. 1 , January 1, 2012, ISSN  1529-3181 ( [PDF; accessed February 28, 2017]).
  3. Fehrenbacher, DD (2013), Information Quality, Controlling 25 (2) pp. 125-126 
  4. MIT Total Data Quality Management (TDQM) . Retrieved September 1, 2014.
  5. German Society for Information and Data Quality - Graphic overview of the 15 IQ dimensions ( Memento from September 1, 2014 in the Internet Archive )
  6. David A. Garvin, What Does "Product Quality" Really Mean ?, In: Sloan Management Review, 1984, Volume 26, Issue 1, pp. 25-43
  7. Martin Bayer in Good data - bad data in Computerwoche Jan. 24, 2011 (PDF, 515 kB)
  8. Methodological Documents: Definition of Quality in Statistics. (No longer available online.) In: Working Group "Assessment of quality in Statistics", archived from the original on December 6, 2013 ; Retrieved December 3, 2013 . Info: The archive link was inserted automatically and has not yet been checked. Please check the original and archive link according to the instructions and then remove this notice. @1@ 2Template: Webachiv / IABot /
  9. Porter, Michael E. (1985). Competitive Advantage: Creating and Sustaining Superior Performance . New York .: Simon and Schuster.
  10. Dennis D. Fehrenbacher: Perceptions of information quality dimensions from the perspective of commodity theory . In: Behavior & Information Technology . tape 35 , no. 4 , February 15, 2016, p. 254-267 , doi : 10.1080 / 0144929x.2015.1128974 .
  11. ^ Crawford, Holly: Reference and information services: An introduction . Libraries Unlimited Englewood, CO, 2001, pp. 433-459.