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Information and data quality

Information and data quality. Efrim Boritz Hans Verkruijsse. Information. To inform is derived from the Latin word Informare : in the sense of "to give form to the mind” Information contributes to the development of the mind of human beings Information quality research over the years.

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Information and data quality

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  1. Information and data quality EfrimBoritz Hans Verkruijsse

  2. Information • To inform is derivedfrom the Latin word Informare: in the sense of "to give form to the mind” • Information contributes to the development of the mind of human beings • Information quality research over the years

  3. Bovee et al. (2003)

  4. Reliability Relevance Useability INFORMATION QUALITY Boritz (2004) Information integrity

  5. Srivastava & Kogan (2010)

  6. Boritz and No (2011)

  7. Boritz (2004)

  8. Information Integrity Framework Boritz et al. (2011)

  9. Domains • Content: various types of information subject matter in information integrity • Process: consists of four key phases: input, process, output and storage that contribute to information integrity • Environment: assures an effective operating IS environment

  10. Domains of informationintegrity Boritz et al. (2011)

  11. Butwhatabout data • Gerald Trites et al (2010): A principaldifferencebetween the information and data is the needfor data to have context in order to beconsideredinformation. This is a hazydistinction, butneverthelessan important concept in principle. • Information is the result of processing data • Data are the building blocks of information

  12. Tagged Data • Two types of data: • raw data: data without content. • tagged data: raw data with content. • XBRL uses tagged data; it tags the content to the raw data in order that this content can move along with that raw data.

  13. From data to information

  14. Metadata,Key for data quality • Metadata is an important enabler of data quality. • Metadata is data that describes the content, context and structure of data. • Metadata contributes to the security, availability, understandability, consistency and verifiability of data.

  15. Content, Context, Structure • Content: identifies the nature of the data and its purpose (e.g., a stream of sensor data, a set of transactions, a list of accounts receivable). • Context : describes the process(es) to which the data relates, relevant parties to a transaction and the duration or instant in time that the data relates to. • Structure: describes the logical and physical organization of data, and the format of and relationships between its elements.

  16. Types of Metadata • Explicitmetadata attached to the content (e.g., XBRL taxonomies and linkbases). • Explicitmetadata that is a central part of the process but is not part of the reported content (e.g. who is accountable for the tagging). • Implicitmetadata that provides the context for understanding the content.

  17. Types of Metadata • There is almost no limit to the amount of data that can be captured about data. • The following slides are intended to be a set of concise but comprehensive items that can be used to manage data quality.

  18. Metadata elements • Description: nature of the data. • Purpose: primary use of the content. • Origin:source of the data (e.g. internal, external). • Owned by: who is the owner of the data. • Custodian: who maintains the data. • Classification for security/privacy: label assigned to the data (e.g. public, internal, confidential, etc). • Access privileges: requirement to access the data.

  19. Metadata elements • Location: the location that the data originated from. • Version: to enable version control. • Date/Timestamp: the date and time the data was generated and/or modified. • Retention/Disposal Requirement: the duration that the data is to be retained for. • Audit trail: to allow the tracing of the data back to the source. • Assurance: the level of verification that the data has undergone.

  20. Data Integrity Framework? Boritz et al. (2011)

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