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Database and Data Warehousing Design

One of the main functions of any business is to be able to use data to leverage a strategic competitive advantage. This feat hinges upon a company’s ability to transform data into quality information. The use of relational databases is a necessity for contemporary organizations; however, data warehousing has become a strategic priority due to the enormous amounts of data that must be analyzed along with the varying sources from which data comes. Since you are now the CIO of a data-collection company which gathers data by using Web analytics and operational systems, you must design a solution overview that incorporates data warehousing. The executive team needs to be clear about what data warehousing can provide the company.

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Finally,on theissue of contextual information quality they provide an overview ofwhichpublications that relate to different data application contexts, whichinclude:database, information manufacture system, accounting, marketing, datawarehouse, decision-making, healthcare, enterprise resource planning,customerrelationship management, ,e-business, WorldWide Web and supply chain management.

Transaction data describe the relevant events in a company, i.e.

In Task 1 (writing about a graph or visual data) you just report what you see.

Popular definitions of such terms have been made byDavenport and (1998), who define data as “discrete, objectivefacts about events” and information as data transformed bythe value-addingprocesses of contextualization, categorization, calculation, correctionandcondensation.

(2002),whodefine data as “providing a record of signs and observationscollected fromvarious sources” and information as when “data arepresented in a particularway in relation to a particular context of action”.

the percentage of data completeness.

Masterdata are defined as the basic characteristics of business entities,i.e.

Thecosts inflicted by poor quality data are for example faulty decisionsbased onpoor data quality, whether this is of operational or strategiccharacter.

Additionally, and identify 10 cost examples of assuring data quality,which are 1) information quality assessment or inspection costs, 2)informationquality process improvement and defect prevention costs, 3) preventinglowquality data, 4) detecting low quality data, 5) repairing low qualitydata, 6)costs of improving data format, 7) investment costs of improving datainfrastructures, 8) investment costs of improving data processes, 9)trainingcosts of improving data quality know-how and lastly 10) management andadministrative costs associated with ensuring data quality.

how toestablish thecausal links between poor data quality and monetary effects).
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The second and horizontal axis dealswith thequality of data.

The efforts have been directed to investigating the effects of dataerrors oncomputer-based models such as neural networks, linear regressionmodels,rule-based systems, etc.” and “In practice, lowquality data can bring monetarydamages to an organization in a variety of ways”.

the costs inflicted by poor quality data decreases exponentially.

Accordingto Kim (2002), thetypes of damage that low quality data can cause depend on the nature ofdata,the nature of the use of data, the types of responses (by the customersorcitizens) to the damages, etc.

Costs associatedwiththis are for example long lead times.

Tohelp determine the optimal data quality maintenance efforts, the paperprovides: (1) a definition of the optimal data maintenance effort; and(2) aclassification of costs inflicted by poor quality data.

Data Warehousing/Big Data Forum

On the otherhand, and (2004) arguethat although there is much literature that claims that the costs ofpoor dataquality are significant in many companies, only very few studiesdemonstratehow to identify, categorize and measure such costs (i.e.

Thesis Data Warehousing Phd - …

Next,Section 3 proposes a model to determine the optimal data maintenanceeffort anda classification of different types of costs inflicted by poor qualitydata.

Data Warehousing Research Papers Thesis

However, a deeper discussion about the meaning of themeaning of‘knowledge’ is beyond the scope of this paper,which, as mentioned, focuses ondata quality.

Master Thesis In Data Warehousing

As for the evaluation of the completeness of some data, thiscan alsobe done relatively straight forward, as long as the focus is on whetherthedata are complete or not in contrast to defining the level ofcompleteness,e.g.

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