1-mis-informationexcelence
TRANSCRIPT
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Ridi FerdianaVersion 1.0.0
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virtually every disaster, Information Systems and Information Quality failures alike,
there will be losses of Customers due to loss of confidence in the companies.
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PoorQualityInformationSystems
PoorQualityBusinessInformation
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Assuming that Enterprise is single systemManagement creates performancemeasures and reward systems that
compromise quality for speed and cost-reduction
not to go to find and fix the Root Causes ofthe broken processes
Management focuses on the needs of theinvestors over the needs of the end-Customers
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Falling into the techno-trap of believingtheir job was to put in place theinformation technology infrastructure
Caught up in the ever-acceleratingtechnology "innovation" spiral, they werenot able to reason through what is thepurpose
that application "solutions" sic. "systems" istheir product
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Never ignore potential waste
Inspect and Correct" model of dataprofiling and data "cleansing
Total Information Quality Management by
designing quality and error-proofing intothe processes.
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addresses a holistic approach to managingInformation Quality and that is based onproven Quality Management Systems(QMS).
E.g. Deming, Juran, Crosby, Imai (Kaizen),the Baldrige Criteria, and Six Sigma.
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to provide a means for organizations, whether in theprivate or public sector, whether large or small, toaccomplish their missions efficiently and effectively todelight their Customers by eliminating process failure,recovery from failure, Information Scrap and Rework or
waste of resources caused by poor quality information
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Assessment: Measure Information Qualitycapability and information processes to identifyprocess capability (degree of defects)
Betterment: Continually improve processes
Culture Transformation: Transform the culture ofthe Enterprise to value "Information Customers"and empower "Information Producers,"
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Quality of "Information Product Specification" data
(data definition, valid values, business rulespecifications, information model correctness, anddatabase design stability, flexibility, and reuse)
Quality of Information Content (accurate andcomplete)
Quality of Information Presentation (relevant,objective, intuitive)
Quality Culture transformation to sustain BusinessPerformance Excellence through InformationExcellence as a result of Continuous Information
Process Improvement
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Data Name: Represents an attribute or characteristic about a real-world object or eventthe enterprise needs to know.
Data Definition: A clear definition of the characteristic of the real-world object or event
Valid Value Set: The complete set of discrete values for the attribute
Data Value Format: The structure of the value, such as NNN-NN-NNNN for a U.S. SocialSecurity number
Business Rules: Clear and correct specification of rules that govern integrity of the validvalues
Derived Data Calculation Formula: The correct algorithm or formula used to calculate afact of derived data
Information Model: Represents the objects and events and the attributes or facts that the enterprise needs to know in order to be successful and accomplish its mission.
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Data Content Quality is equivalent to theQuality of Raw Materials in manufacturing.
Defects in the content cause defects in the
Information Presented to KnowledgeWorkers or in applications that use thedefects in their calculations or processing,causing them to fail
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relevant information must be accessible,available, and presented in a timely,
intuitive, and un-biased way, enablingKnowledge Workers to understand thesignificance in the Information, so they cantake the right action or make the right
decision.
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IQ is quality of data in databasesIQ is "data cleansing."
IQ is data profiling or data measurement
IQ is "fitness for use" or "fitness forpurpose."
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The Quality of Information is needed tobuild system and maintenace the business
TIQM is about quality of people. "A
company able to build quality into itspeople is already halfway towardproducing Quality [Information] Products
TIQM is Quality System that values the
information Customer in termpresentation, content, and data