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Competing with High Quality Data: Concepts, Tools, and Techniques for Building a Successful Approach to Data Quality by Rajesh Jugulum

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Index

A

  • Abnormal conditions:
    • direction of
    • “good” vs. “bad”
    • variable set for detecting
  • Accuracy (DQ dimension)
  • Adjoint of a square matrix
  • AlliedSignal
  • Analysis(-es):
    • association
    • correlation
    • current state
    • DQ capabilities gap
    • drill-down
    • measurement system
    • multiple regression
    • network
    • Pareto
    • principal component
    • regression
    • return on investment
    • root-cause
    • signal-to-noise ratios
  • Analysis of variance (ANOVA)
    • in data tracing
    • defined
    • elements of
    • nested
    • two-way
  • Analytical insights, in DQPC
  • Analytics ascendency model (Gartner)
  • Analytics management. See also Data analytics
  • Analytics vision
  • ANOVA, see Analysis of variance
  • Artificial neural networks (ANNs)
  • Assessment(s):
    • of critical data elements
    • of current state
    • in DAIC Assess phase
    • of data quality
    • skilled resources for
  • Assess phase (DAIC approach)
  • Assignable causes. See also Special cause variation
  • Association analysis:
    • defined
    • for discrete CDEs
    • in transaction pattern recognition
  • Attribute control charts
  • Attribute data
  • Attribute profiling
  • Average chart
  • Average-range ( -R) charts
  • Average-standard deviation ( -S) chart

B

  • Backpropagation algorithm, standard
  • Backpropagation (feed-forward) method
  • Bad data, see Poor-quality data
  • Bank of America
  • Basel II case study
    • CDE rationalization matrix
    • correlation and regression analysis

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