A Framework for the Classification of Data and

8/7/2019 A Framework for the Classification of Data and

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M. Pamela Neely Business Research Consortium

April 17, 2010


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Agenday Introductiony Other D IQ Research Frameworksy DIQ PMOT Research Frameworky DIQ Research Topicsy Conclusions and Further Research


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Introductiony The quality of data and information is recognized as

an area of concern in both industry and academiay Emerging as an important area of research

y Journal of Data and Information Q uality y International Journal of Data Q uality y International Conference on Information Q uality y Other practitioner conferences dedicated to D IQ


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O ther D IQ Frameworksy A Framework for Analysis of Data Q uality Research

(Wang, Storey, Firth, 1995)y

Seven Elementsy Management Responsibilitiesy Operation and Assurance Costsy Research and Developmenty Productiony Distributiony Personnel Managementy Legal Function


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O ther D IQ Frameworksy A Product Perspective on Total Data Q uality

Management (Wang, 1998)y Extends the Information as Product analogy

y Information Manufacturing System ( ISM) producesy Information Products ( IP)y Which have some value to the consumer

y Four Roles Identifiedy Information suppliersy Information manufacturersy Information consumersy Information product managers


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O ther D IQ Frameworksy Reflections on Data Q uality Research: A Review of the

Literature since 1995 (Neely, 2005)y Further extends information as product adding fitness-for-

use characteristics identified by J.M. Juran (1998)y Who are the users of the product or service?y H ow are the users actually putting the product or service to use?y What specific determinants, from a user perspective, make a

product or service fit for use?y

What are the ec onomi c res ou rces available to the producer anduser?y Is there a possibility or probability of danger to human s af e ty ?

y Matrix is developed with Wang s dimensions as one axis andJuran s criteria as the other


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O ther D IQ Frameworksy A Framework to Assess Decision Q uality (Ge and

Helfert, 2007)y Three Categories for Classification

y Information quality assessmenty Metric layery Dimension layery Methodology layer

y

Information quality managementy Contextual information quality y 2x2 matrix developed for classification of papersy Several research questions are posed


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O ther D IQ Frameworksy Accounting Information Systems Research: Is it another

QWERTY? (Poston and Grabski, 2000)y Information Lifecycle perspective

y Database issuesy Data entry y Data storagey Data viewsy Overall database issues (entry, storage, and views)

y Data then used to make decisions and support action takeny Decisions with respect to what data should be captured and entered in

the A IS leads back to data entry y Overarching the whole thing is accountability of the management

of the A ISy Underpinning it is data verification, supported by internal controls

and auditing


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O ther D IQ Frameworksy Beyond Accuracy: What Data Q uality Means to Data

Consumers (Wang and Strong, 1996)y

Definitionaly Four categories of data quality

y Intrinsicy Contextualy Representationaly Accessibility

y Fitness-for-use becomes a primary focus to determine thequality of the data


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O ther D IQ Frameworksy An investigation of the impact of organization size on

data quality issues (Nord, Nord and Xu, 2005)y

Examine critical factors for D IQ in accounting informationsystems, defining stakeholdersy Information producersy Information custodiansy Information consumersy Data/database managersy Internal auditors


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O ther D IQ Frameworksy Overview and Framework for Data and Information

Q uality Research (Madnick, Wang, Lee, and Zhu,

2009)y Describe a framework for classification of D IQ literaturey Research topicsy Research methods

y Topics are drawn from the scope list of the Journal of Data andInformation Q uality

y Eleven research methods broad and narrow are identifiedy 2x2 matrix developed


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DIQ PM OT Research Frameworky None of the previously described frameworks

adequately captures the complexity of the D IQ

literaturey DIQ touches the entire organizationy DIQ draws on multiple disciplinesy DIQ crosses organizational boundariesy Context, or fitness-for-use, is a critical component in

determining the quality of data and information


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DIQ PM OT Research Frameworky Development of a research framework facilitates

classification of the body of literaturey

Provides researchers an opportunity to understandwhat exists and what still needs to be discoveredy Provides a foundation for the generation of research

questions


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DIQ PM OT Research Frameworky Extending previous research the elements of the

PMOT framework are:y

P eopley Processes

y M anagementy perational

y T echnology

y Three major research strands ( MOT ) with P eoplebeing an integral part of all three strands


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PM OT

Man


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DIQ Research Topics- Sample

Research Q uestionsPolicies & Procedures

What are upper management's roles and responsibilities in the creation, approval, and enforcement of D IQ -related policies,

procedures and systems? What policies and procedures should exist specifically to deal with human safety issues such as privacy,

accuracy and integrity which are important in fields like healthcare?

Legal & EthicalWhat legal and regulatory requirements exist that pertain to D IQ (e.g., H IPPA, S OX , & GLB)? What internal controls should exist

to ensure D IQ (e.g., H IPPA, S OX , & GLB)?

Personnel ManagementWhat training is necessary and why? What motivates employees to care about D IQ ? What is the economic impact when

personnel are improperly trained, resulting in poor D IQ ?

ProductionWhat interventions are available to improve the quality of data (e.g., training, managerial direction, incentives, outlier reports)?What are the cost categories and benefits of production, including data cleaning and integrity checks?

Distribution

What tools are available to document the processes and metrics of a distribution system (e.g., IDEF0, IQ Matrix, IASDO Model)?What processes and metrics are or should be a part of the distribution system?What metadata is needed? What processes exist to prevent misdirecting or distributing information to unauthorized people?

O perations & AssuranceWhat are the economic costs associated with D IQ in an information system, database or accounting system? What are the

metrics for measuring the operation and assurance costs? What are the most important security threats to D IQ ? What is the

return on investment of putting data quality systems in place?

Dimensions & MeasurementWhat data quality dimensions and their corresponding metrics exist or should be monitored? What procedures, surveys andmetrics must be in place to validate and verify D IQ ?

Systems & DesignWhat technologies and processes are available to manage data uncertainty (e.g., approximate, probabilistic, inexact, incomplete,

imprecise, fuzzy, inaccurate data)? What costs (e.g., poor decision making, increased legal costs, and bad public relations) are

associated with not incorporating D IQ into systems design?

Information Products ( IP)

What methods exist to measure IP quality? What are the factors that influence people's judgment about IQ (e.g., education,

experience, website domain, industry, firm size, cognitive authority)? What are the costs and benefits of improving IP? What

measures are used to show the impact of D IQ with respect to how IP affects human safety (e.g., percentage of loss on retirement

accounts, investment decisions)?


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C onclusionsy Several frameworks have been developed within the

fieldy

As the field matures it becomes increasingly clearerthat fitness-for-use is a critical component indetermining quality

y DIQ reaches across the organization and quality is in

the eye of the beholdery Understanding both the environment for D IQ and the

literature that has been conducted in critical tomoving towards answers


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Further Researchy Flesh out the list of questionsy Classify the body of literature along several metrics

y PMOT question(s) addressedy Industry y Reference Discipliney Research Methody Researcher Goalsy Audiencey Key Findings

A Framework for the Classification of Data and

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