Wang, R.Y., & Strong, D.M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5--34.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....poor decisions [22] It is clear that wrong data is likely to result in wrong decisions. 3. Data quality variables While there is no single definition of data quality, accuracy, timeliness, consistency, completeness, relevancy, and fitness for use are among the variables most frequently used [51]. Accuracy generally means that the recorded value conforms to the real world fact or value. Accuracy refers to lack of errors and is considered by consumers of data to be the most important characteristic of data quality [3] Timeliness implies that the recorded value is not out of date. Data ....

R.Y. Wang, D. Strong, Beyond accuracy: what data quality means to data consumers, Journal of Management Information Systems 12 (4), 1996, pp. 5-34.

....by two data sources: a thermometer and a tensiometer . These two data sources are distributed on the space of the physiological state of the patient. They are complementary on the same space, and het erogeneous. 2. 4 Qualified gain An empirical approach on data quality has been pro posed in [10]. This approach is based on the concept of data quality through a classification of modalities of quality. Despite this classification, the measure of data quality through comparison or data sources classifying stays a hard multi attributes decision problem [6] In order to simplify the problem of ....

R. Wang, D. Strong, and L. Guarascio. Beyond accuracy: What data quality means to data con- sumers. 1996.

....two main considerations are worth to be pointed out: There is no agreement on the set of the dimensions strictly characterizing data quality. As an example, though Redman It0] identifies more than twenty data quality dimensions, they do not include all of the fifteen dimensions proposed by Wang [8]. Even if some dimensions are universally considered as important, there is no agreement on their meaning. As an example, the timeliness dimension has a meaning related to the context in the Wang s proposal [8] that is, is the information in time with respect to specific requirements ) ....

....dimensions, they do not include all of the fifteen dimensions proposed by Wang [8] Even if some dimensions are universally considered as important, there is no agreement on their meaning. As an example, the timeliness dimension has a meaning related to the context in the Wang s proposal [8] (that is, is the information in time with respect to specific requirements ) conversely, in the Ballou s definition [6] timeliness is indicated as the degree at which a data item is up to date. One of the reasons for such a wide variety of dimensions is that it is very difficult to define a ....

Wang R.Y., Strong D.M.: Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, vol. 12, no. 4, 1996.

....is not an absolute concept, but it rather needs to be put in a context, since the received data might be used or interpreted in different ways by the receiving organizations operating in the process. The need for context dependent data quality dimensions is recog nized in the literature, e.g. in [46]. In e Applications, the context is the cooperative process and therefore the data quality dimensions are both related to the data creation and internal management phase on one side, and to their evolution during the process on the other one. In this section, we have chosen and adapted some ....

....process and therefore the data quality dimensions are both related to the data creation and internal management phase on one side, and to their evolution during the process on the other one. In this section, we have chosen and adapted some of the data quality dimensions proposed in [46] regarding quality parameters that are inherent to the context of data (timeliness and source reliability dimensions) In addition, we propose new dimensions related to data exchange in cooperative processes (importance and confidentiality dimensions) On the basis of such dimensions, the ....

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Wang, R. and D. Strong: 1996, 'Beyond Accuracy: What Data Quality Means to Data Consumers'. Journal of Management Information Systems 12(4).

....is essential to ensure high quality IP. In the TQM literature, the widely practiced Deming cycle for quality enhancement consists of: Plan, Do, Check, and Act. By adapting the Deming cycle [7] we develop the TDQM cycle. The definition component of the TDQM cycle identifies important IQ dimensions [11] and the corresponding IQ requirements. The measurement component produces IQ metrics. The analysis component identifies root causes for IQ problems and calculates the impacts of poor quality information. Finally, the improvement component provides techniques for improving IQ. They are ....

....and delivery of customer account data is an IP manager. Information Quality. Just as a material product has quality dimensions associated with it, an IP has IQ dimensions. IQ has been viewed as fitness for use by information consumers, with four IQ categories and fifteen dimensions identified [11]. As shown in Table 2, the intrinsic IQ captures the fact that information has quality in its own right. Accuracy is merely one of the four dimensions underlying this category. Contextual IQ highlights the requirement that information quality must be considered within the context of the task at ....

Wang, R.Y. and Strong, D.M. Beyond accuracy: What data quality means to data consumers. J. Manage. Info. Syst. 12, 4 (1996), 5--34.

....0.9 1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Weights Utility(Greedy) Utility(DP) Figure 5: Ratio of the utility of the plans given by ParPlan Greedy to that given by ParPlan DP for a spectrum of weights in the utility metric. X axis varies the weight used in the utility metric, and Y axis shows the ratio of utilities 0 10 20 30 40 50 60 70 80 90 100 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Weights Percentage Coverage Cost Figure 6: Comparing the Coverage and cost of the plans found by ParPlan DP by using different weights in Utility function, on queries of 4 subgoals. X axis varies the weights in the ....

Wang, R.Y. and D.M. Strong. Beyond accuracy: What data quality means to data consumers. Journal on Management of Information Systems. 12(4).

....assessing scores for certain aspects of information quality and aggregating these scores is easier than immediately finding a single global IQ score. 3.1 An IQ Measure Information quality is defined as a catalog of IQ criteria. Several research projects have put together such general catalogs [Bas90, CZW98, JV97, Red96, Wei99, WS96] or compiled multiple catalogs [NR00, EW00] These catalogs are proposals formulated in the most general way to allow for different interpretation depending on applications, data sources, and users. Many criteria are not independent and typically not all criteria should be used at the same time. ....

Richard Y. Wang and Diane M. Strong. Beyond accuracy: What data quality means to data consumers. Journal on Management of Information Systems, 12(4):5-34, 1996.

....2 Contact Author Marcus Maletic Utilizing Association Rules for the Identification of Errors in Data Technical Report CS 00 04 2 5 8 2000 1. Introduction The quality, correctness, consistency, completeness, and reliability of any large real world data set depend on a number of factors [Wang95, Wang96, English99]. But the source of the data is often times the crucial factor. Data entry and acquisition is inherently prone to errors both simple and complex. Much effort is typically given to this front end process, with respect to reduction in entry error, but the fact often remains that errors in a large ....

....within the merge purge problem is available in [Galhardas99] TDQM is an area of interest both within the research and business communities. The data quality issue and its integration in the entire information business process are tackled from various points of view in the literature (e.g. [Fox95, Fox94, Levitin95, Orr98, Pak93, Redman96, Redman98, Strong97, Svanks84, Wang96]) Other work refers to the same problem as the enterprise data quality management [Flanagan98] The most comprehensive survey of the research in this area is available in [Wang95] Unfortunately, none of the mentioned papers refer explicitly to the data cleansing problem. Some of the papers deal ....

Wang, R.; Strong, D.; Guarascio, L.: "Beyond Accuracy: What Data Quality Means to Data Consumers", in Journal of Management Information Systems, Vol. 12, No 4, Spring 1996, pp. 5-34

....Author Maletic Marcus Utilizing Association Rules for Automated Identification of Errors in Data Sets Technical Report CS 00 03 2 2 28 2000 1. Introduction The quality, correctness, consistency, completeness, and reliability of any large real world data set depend on a number of factors [Wang95, Wang96, English99]. But the source of the data is often times the crucial factor. Data entry and acquisition is inherently prone to errors both simple and complex. Much effort is typically given to this front end process, with respect to reduction in entry error, but the fact often remains that errors in a large ....

....within the merge purge problem is available in [Galhardas99] TDQM is an area of interest both within the research and business communities. The data quality issue and its integration in the entire information business process are tackled from various points of view in the literature (e.g. [Fox95, Fox94, Levitin95, Orr98, Pak93, Redman96, Redman98, Strong97, Svanks84, Wang96]) Other work refers to the same problem as the enterprise data quality management [Flanagan98] Probably the most comprehensive survey of the research in the area is available in [Wang95] Unfortunately, none of the mentioned papers refer explicitly to the data cleansing problem. Some of the ....

Wang, R.; Strong, D.; Guarascio, L.: "Beyond Accuracy: What Data Quality Means to Data Consumers", in Journal of Management Information Systems, Vol. 12, No 4, Spring 1996, pp. 5-34

....of a source and the quality of the documents it contains must be measured with more than one criterion or quality dimension. Source selection is thus a multi attribute decision making problem. 2 The Data We havechosen three distinct quality criteria from a collection empirically gathered byWang and Strong #1996# to de#ne information quality. To these quality criteria come two cost criteria, whichwe #nd to be the most important for WWW information retrieval. The data is not given in a common unit nor in a common range of values. It will be the task of the decision making methods to deal with these ....

Wang, R. Y. and D. M. Strong #1996#. Beyond accuracy: What data quality means to data consumers. Journal on Management of Information Systems 12, 4, 5#34.

....reviews and classification of IQ projects are summarized in Table 1. TDQM: Total Data Quality Management is a project aimed at providing an empirical foundation for data quality. Wang and Strong have empirically identified fifteen IQ criteria regarded by data consumers as the most important [WS96]. The authors classified their criteria into the classes intrinsic quality , accessibility , contextual quality , and representational quality . The classification is based on the semantic of the criteria. It is of use to describe the criteria but not to assess them. Thus, this classification ....

....survey: The survey in [NR99] compiles IQ criteria from all of the previously mentioned projects and finds a classification of its own. The classes are contentrelated, technical, intellectual, and instantiation related criteria, and thus, they are semantic oriented. Project Classification TDQM [WS96] Semantic oriented MBIS [NLF99] Processing oriented Weikum [Wei99] Processing oriented DWQ [JV97] Goal oriented SCOUG [Bas90] Goal oriented Chen et al. CZW98] Goal oriented Requirement survey [NR99] Semantic oriented Table1: IQ criterion sets with classifications The mentioned ....

Richard Y. Wang and Diane M. Strong. Beyond accuracy: What data quality means to data consumers.Journal on Management of Information Systems, 12(4):5-34, 1996.

....the use of such methods are given. The future research directions necessary to address the data cleansing problem are discussed. Keywords: data cleansing, data cleaning, data quality, error detection. 1. Introduction The quality of a large real world data set depends on a number of issues [9, 39, 40], but the source of the data is the crucial factor. Data entry and acquisition is inherently prone to errors both simple and complex. Much effort can be given to this front end process, with respect to reduction in entry error, but the fact often remains that errors in a large data set are common. ....

.... Marcus IQ2000 June 23, 2000 3 Total Data Quality Management (TDQM) is an area of interest both within the research and business communities. The data quality issue and its integration in the entire information business process are tackled from various points of view in the literature (e.g. [13, 14, 24, 28 31, 34, 35, 40]) Other work refers to the same problem as the enterprise data quality management [12] The most comprehensive survey of the research in this area is available in [39] Unfortunately, none of the mentioned papers refer explicitly to the data cleansing problem. Some of the papers deal strictly ....

Wang, R., Strong, D., and Guarascio, L., "Beyond Accuracy: What Data Quality Means to Data Consumers," Journal of Management Information Systems, vol. 12, no. 4, Spring 1996, pp. 5-34.

....provided to the end user. 2. QUALITY AND DATAWAREHOUSES Data quality has been defined as the fraction of performance over expectancy, or as the loss imparted to society from the time a product is shipped [ BBBB95] We believe, though, that the best definition is the one found in [TaBa98, Orr98, WaSG94] data quality is defined as fitness for use . The nature of this definition directly implies that the concept of data quality is relative. For example, data semantics (the interpretation of information) is different for each distinct user. As [Orr98] mentions the problem of data quality is ....

....ontological based [Wand89] WaWa96] WaWe90] approach has been proposed in order to identify the possible deficiencies that exist when mapping the real world into an information system. In [DeMc92] information theory is used as the basis for the foundation of data quality dimensions, whereas in [WaSG94] marketing research is used as the basis for the same cause. The second way to establish data quality dimensions is the use of pragmatic approaches. e.g. data quality dimensions can be thought as user defined. Another proposal [WaSF95] is the formation of a data quality standard technical ....

R.Y. Wang, D. Strong, L.M. Guarascio. Beyond Accuracy: What Data Quality Means to Data Consumers. Technical Report TDQM-94-10, Total Data Quality Management Research Program, MIT Sloan School of Management, Cambridge, Mass., (1994).

....reviews and classi cation of IQ projects are summarized in Table 1. TDQM: Total Data Quality Management is a project aimed at providing an empirical foundation for data quality. Wang and Strong have empirically identi ed fteen IQ criteria regarded by data consumers as the most important [WS96]. The authors classi ed their criteria into the classes intrinsic quality , accessibility, contextual quality , and representational quality . The classi cation is based on the semantic of the criteria. It is of use to describe the criteria but not to assess them. We call this classi cation ....

Richard Y. Wang and Diane M. Strong. Beyond accuracy: What data quality means to data consumers. Journal on Management of Information Systems, 12, 4:534, 1996.

....Source administrator DW Designer Fig. 1. A generic architecture for a DW Data quality has been defined as the fraction of performance over expectancy, or as the loss imparted to society from the time a product is shipped [3] We believe, though, that the best definition is the one found in [13,11,17,14]: data quality is defined as fitness for use . The nature of this definition directly implies that the concept of data quality is relative. For example, data semantics (the interpretation of information) is different for each distinct user. As [11] mentions the problem of data quality is ....

....affect them. The idea behind this is to enrich the meta data repository in order to ease the impact analysis of each evolution operator and its consequences on the quality factor measures. 5. Related Work There has ben much research on the definition and measurement of data quality dimensions [18,15,17,13]. A very good review is found in [16] The GQM methodology is best presented in [10,2] The TDQM methodology [14] follows the Total Quality Management approach, adapted for the evaluation of data quality in an information system (by assuming that each piece of produced information can be ....

R.Y. Wang, D. Strong, L.M. Guarascio. Beyond Accuracy: What Data Quality Means to Data Consumers. Technical Report TDQM-94-10, Total Data Quality Management Research Program, MIT Sloan School of Management, Cambridge, Mass., 1994.

....our goal: Enabling cooperation with the user. We regard density as an information quality criterion. Information quality considerations are not wide spread in the research community. Wang and Strong have empirically identified fifteen IQ criteria regarded by data consumers as the most important [WS96] Some research projects have focused on integrating all these criteria into their model, for instance the Data Warehouse Quality project [JQJ98] or [NLF99] Other projects have chosen only one or a few of these criteria for examination. Motro and Rakov, for instance, have studied the ....

....plans, i.e. plans that obtain values for all required attributes. But, in contrast to other query planning methods, if no complete plan exists they still produce valuable answers. 3 Density Density is the extent to which data are of sufficient breadth, depth, and scope for the task at hand [WS96] We interpret density as the fullness of the wrappers: Typical information sources have many missing values (null values) in the attributes they provide or put the other way, sources often provide attributes they do not completely cover. For instance, book information sites do not (and ....

Richard Y. Wang and Diane M. Strong. Beyond accuracy: What data quality means to data consumers. Journal on Management of Information Systems, 12, 4:5--34, 1996.

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Wang, R.Y., & Strong, D.M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5--34.

No context found.

Wang, R.Y. and Strong, D.M. Beyond accuracy: what data quality means to data consumers. Journal of Management Information Systems 12, 4 (1996), 5--34.

No context found.

Wang, R. Y. and Strong, D. M. (1996), "Beyond Accuracy: What Data Quality Means to Data Consumers," Journal of Management Information Systems, 12(4), 5-34.

No context found.

R. Y. Wang and D. M. Strong, "Beyond Accuracy: What Data Quality Means to Data Consumers," Journal of Management Information Systems, vol. 12, pp. 5-34, 1996.

No context found.

Wang, R.Y. and Strong, D.M. Beyond accuracy: what data quality means to data consumers. Journal of Management Information Systems 12, 4 (1996), 5--34.

....and use of data. DQ problems may arise anywhere in this larger IS context. Thus, we argue for a conceptualization of data quality that includes this context. Database research aims at ensuring the quality of data in databases. In the DQ area, existing research investigates DQ definitions [8, 11], modeling [1, 2] and control [6] With few exceptions, however, DQ is treated as an intrinsic concept, independent of the context in which data is produced and used. This focus on intrinsic DQ problems in stored data fails to solve complex organizational problems. We attribute this failure, in ....

....concerns about the ease of access and ease of understanding data. Contextual DQ includes concerns about how well data matches task contexts. This research adopts a data consumer perspective. The results confirm the importance of the quality categories and dimensions in our previous research [11]. They also enrich our understanding of how organizations experience DQ problems and which dimensions comprise these problems. For example, this research discovered that representational DQ dimensions are underlying causes of accessibility DQ problem patterns. Some might argue our research ....

Wang, R. Y. and Strong, D. M. Beyond accuracy: What data quality means to data consumers. J. Manage. Info. Syst. 12, 4 (1996), pp. 5--34.

.... or intentional data corruption (Mathieu and Khalil 1997; Strong, Lee and Wang 1997a) As a consequence, IS professionals must seek not only to improve data accuracy, but also to ensure information accessibility and relevance as it relates to the context of the information consumers tasks (Wang and Strong 1996; Strong, Lee and Wang 1997b) Few IS professionals, however, have received formal training in specific techniques to maintain and improve information quality. What techniques they are taught are often presented in an ad hoc way as part of technical IS courses. For example, they may learn about ....

....from that of other educational institutions. At such general conferences, only one panel or session on information quality may be included. # #. X #6410)X ## 0X # 2 01 59 Another approach for educators is to attend a conference on IQ such as the annual information quality conference (Wang 1996; Strong and Kahn 1997; Chengalur Smith and Pipino 1998) At this conference, all presentations focus on information quality research and practice. Educators can discuss information quality topics with presenters and participants and apply what is learned in their courses. For an educator whose ....

Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12 4, 5-34.

No context found.

R.Y. Wang and D.M. Strong, "Beyond Accuracy: What Data Quality Means to Data Consumers," J. Management Information Systems, Spring 1996, pp. 5-34.

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R.Y. Wang, D.M. Strong, Beyond accuracy: What data quality means to data consumers, Journal of Management Information Systems 12 (4) (1996).

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