ict & information quality: ‘eliminating the negatives’

Independent information management consultantsA business unit of UXC Limited

ICT & Information Quality: ‘Eliminating the Negatives’

Info-Qual for Structured Data• The SPoT principle: data entities and information

domains should bear a one-to-one relationship with their real-world equivalents independent of any repetition generated by their use in multiple systems or processes or through multiple views of the same entity or domain instance.

• Entity Integrity: The entity integrity rule states that for every instance of an entity, the value of the primary key must exist, be unique, and cannot be null. Without entity integrity, the primary key could not fulfill its role of uniquely identifying each instance of an entity.

• Referential Integrity: The referential integrity rule states that every foreign key value must match a primary key value in an associated table. Referential integrity ensures that we can correctly navigate between related entities.

• Data Rules: A data rule is a subset of business rules that deal with the data column of the Zachman Framework. They specify the criteria for maintaining the quality of the data architecture. Data rules are further subdivided into data integrity rules, data sourcing rules, data extraction rules, data transformation rules and data deployment rules.

Info-Qual for Unstructured DataSome progress in the ‘semi-structured’ information space – e.g.

DITA: The Darwin Information Typing Architecture (DITA) is an XML-based, end-to-end architecture for authoring, producing, and delivering technical information. This architecture consists of a set of design principles for creating "information-typed" modules at a topic level and for using that content in delivery modes such as online help and product support portals on the Web. RDF: Resource Description Framework (RDF) is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadata model but which has come to be used as a general method of modeling information, through a variety of syntax formats.The RDF metadata model is based upon the idea of making statements about resources in the form of subject-predicate-object expressions, called triples in RDF terminology.

ICT, Info, Productivity & Value

ICT

ICT Value

Productivity

ICT

Information Quality/Value

Productivity

ICT Value

ICT – eliminating the ‘negatives’ Some of the recognised ‘positives’ of ICT: Automation,

storage, access, time and space delimitation

Correlated ‘negatives’ of ICT - identified by an external respondent to the Opticon report on information quality:

• Increased information storage capacity leads to data ‘noise’ and requires data cleansing and archiving actions

• Automation of processes tends to lead to process ‘invisibility’ and inflexibility (lack of relation to the changing form, time and space of the ‘real world’)

• Greater accessibility (‘democratisation’) of information reduces centralised ‘control’ of information quality

Quote: Our respondent felt that an information quality ‘tool’ could help to remedy these effects, and that it should be used to “combat the negatives of ICT in order to fully realise the positives.”

The main challenge for IM consultants: overcome information ‘chaos’ and move towards the ‘single point of truth’ (SPoT)

Info-Qual in Unstructured Data (1)

• Several systematic models of information quality have been defined in the theoretical work of a core group of information scientists specialising in information quality: Richard Y. Wang (Boston University, MA), Diane M. Strong (Worcester Polytechnic Institute, Worcester, MA) and Beverly K. Kahn (Suffolk University, Boston, MA).

• These models have various characteristics in common, namely that they all attempt to arrive at a definitive ‘taxonomy’ of information quality categories and criteria belonging to each category through some theoretical or ad-hoc process of class-derivation.


• On the other hand, there is little agreement between these primary models of information quality in relation to conceptual structure: i.e. there is no ‘single view’ of how subjective and objective interpretations of information quality should be defined or evaluated, nor what model of internal/external validity should be applied in the context of information quality assessments.


• The internationally recognised research of Monash University’s Rosanne Price and Graeme Shanks (2005) takes a theoretical, rather than an ad-hoc, intuitive or methodological approach to defining an initial taxonomy of information quality based on semiotic (linguistics) theory.

• Price, R. and Shanks, G. ‘A semiotic information quality framework: development and comparative analysis’, Journal of Information Technology (Palgrave Macmillan, 2005) and http://www.ingentaconnect.com/content/pal/paljit/2005/00000020/00000002/art00003

• ‘InfoQual’ is demonstrably a current, innovative, rigorous and comprehensive model that is explicitly designed to lead into the development of ‘mechanisms’ for ‘information quality assessment’. It also attempts to resolve some of the limitations inherent in previous models.

• It does have some of its own limitations – still relies on ‘subjective’ taxonomies, theoretical basis not generally familiar to practitioners, role of IM/ICT standards unclear and categories are not associated with any system of quantitative evaluation of unstructured information quality.


Information Quality Framework Based on the Price & Shanks ‘InfoQual’ Model: Layer 1

Information Quality

Conformance to Purpose

Internal Validity

[Information is intelligible, usable and fit for purpose]

[Information is ‘correct’according to rules that

define and/or are applied to it]

External Integrity

[Information represents external phenomena,

the ‘real-world’, truthfully]

Information Quality Framework Based on the Price & Shanks ‘InfoQual’ Model: Layer 2

Information Quality

Internal Validity

External Integrity

ANZLIC

IDEF

UML

Dublin Core

ITILANZILF Conformance to Purpose

PMEST

ISO15489

DMAIC VOC

DOD 5015.2

OAISIDI-NTNU ‘Two-world’Modeling

SPoT

BRDF WAI

References

• Eppler, M.J.and Wittig, D. (2000) Conceptualizing Information Quality: a review of information quality frameworks from the last ten years, in Proceedings of the 2000 Conference on Information Quality. Eds Klein D. and Rossin D. F. IQ-2000 MIT Cambridge, Massachusetts, USA. pp 83-91

• Price, R. and Shanks, G. ‘A semiotic information quality framework: development and comparative analysis’, Journal of Information Technology (Palgrave Macmillan, 2005) and http://www.ingentaconnect.com/content/pal/paljit/2005/00000020/00000002/art00003

ict & information quality: ‘eliminating the negatives’

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