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
    • DQ rules
    • signal-to-noise ratios analysis
  • Baseline:
    • for critical data
    • for improvement
  • BIDVs (business impacting decision variables)
  • Big data:
    • advantages of
    • defined
  • Big data analytics
    • artificial neural networks
    • discrimination and classification method
    • examples of projects
    • feed-forward (backpropagation) method
    • Fisher's discriminant function
    • multiple process control
    • multiple regression analysis
    • operating model for
    • principal component analysis
    • standard backpropagation algorithm
    • stepwise regression
    • test of additional information (Rao's test)
    • types of
    • using Mahalanobis distance
  • Big data analytics operating model
  • Bramson, Brian, 13n.1, 23n.1
  • Business/function-level CDE rationalization matrix
  • Business impacting decision variables (BIDVs)
  • Business rules, see Data quality (DQ) rules
  • Business specification
  • Business use, defining

C

  • Capabilities gap analysis report
  • Cause-related analytics
  • c-charts
  • CDEs, see Critical data elements
  • CDE rationalization matrix
    • Basel II case study
    • business/function-level
    • enterprise
  • CDO, see Chief data office
  • Central limit theorem
  • Chance causes. See also Common cause variation
  • Change management processes, formalizing
  • Charter:
    • program
    • project
  • Chief data office (CDO):
    • components of
    • and other functions
  • Client experience improvement case study
  • Cofactors (matrices)
  • Common cause variation
    • behaviors of
    • uncontrollable variation as
  • Communications, program
  • Completeness (DQ dimension)
  • Confidence interval
  • Conformity (DQ dimension)
  • Consistency and synchronization (DQ dimension)
  • Continuous CDEs:
    • correlation analysis for
    • regression analysis for
  • Control:
    • in DAIC Control phase
    • in data integration
    • defining operating guidelines for
    • in DQOM
  • Control charts. See also Statistical process control (SPC)
    • attribute
    • average–standard deviation
    • to detect small shifts
    • for DQ scorecards
    • multivariate process
    • sample and sample parameters
    • types of data
    • value of
    • variable
  • Controllable variation. See also Special cause variation
  • Control limits
    • and stable vs. unstable process variation
    • upper and lower
  • Control phase (DAIC approach)
  • COPQD (cost of poor-quality data). See also Quantifying impact of DQ
  • Correlation analysis:
    • in Basel II case study
    • for continuous CDEs
  • Correlation coefficient
  • Correlation matrix
  • Cost implications
  • Cost of poor-quality data (COPQD). See also Quantifying impact of DQ
  • Cost waterfall
  • Critical data elements (CDEs)
    • assessment of
    • baseline for
    • CDE rationalization matrix
    • continuous, correlation analysis
    • continuous, regression analysis for
    • defined
    • defining
    • discrete, association analysis for
    • enterprise
    • identification of
    • impact of
    • prioritization of. See also Funnel approach
    • quality levels of
    • steady state for
  • Cumulative sum (CUSUM) control charts
  • Current CDE capability
  • Current state, documenting
  • Current-state analysis
  • Current state assessment, in DQOM
  • CUSUM (cumulative sum) control charts

D

  • DAIC approach, see Define, Improve, Analyze, and Control approach
  • Dashboards, determining set of
  • Data. See also Big data
    • as key resource
    • types of
    • variable
  • Data analytics
    • for big data
    • defined
    • in DQOM
    • executing
    • skilled resources for
    • types of
  • Data architecture, global financial crisis and
  • Data assessment, in DQOM
  • Data collection plan
  • Data consumer business processes
  • Data decay (DQ dimension)
  • Data dictionary
  • Data elements. See also Critical data elements (CDEs)
    • in data dictionary
    • defined
    • paths traveled through organization
  • Data governance
  • Data innovation
    • big data in. See also Big data
    • defined
  • Data integration, in DQOM
  • Data lineage, through data tracing
  • Data management function:
    • chief data office
    • increasing importance of
  • Data producer business processes
  • Data profiling:
    • in Assess phase
    • in DQOM
    • skilled resources for
    • types of
    • using DQ rules in
  • Data quality (DQ)
    • as chief data office component
    • and data management function
    • dimensions of
    • linkage between process quality and. See also Issues resolution [IR]
    • solution strategy for
  • Data quality analyst (DQA)
    • in assessment of data quality
    • role of
  • Data quality issues:
    • collecting list of
    • compounding of
    • implementing management process for
    • prioritization matrix for
    • resolving, see Issues resolution [IR]
    • root-cause analysis of
    • sources of
  • Data quality operating model (DQOM)
    • assessment of current state of DQ in
    • current-state analysis in
    • data assessment in
    • data integration in
    • data profiling and analytics in
    • discussion on establishing
    • DQ projects in
    • establishment of program in
    • foundational capabilities of
    • issues resolution in
    • methodology in
    • monitoring and control in
    • ongoing monitoring and effectiveness measurement in
    • skilled resources in
    • strategy and governance in
    • technology infrastructure and metadata in
  • Data quality practices center (DQPC)
  • Data quality (DQ) rules
    • building/executing
    • data profiling using
    • specifying
    • translating dimensions into
  • Data quality scores (DQ scores)
  • Data quality (DQ) scorecards
    • analytical framework for
    • ANOVA
    • application of framework
    • at CDE level
    • creation of
    • defined
    • determining set of
    • development of
    • in DQOM
    • heat maps
    • producing
    • SPC charts
    • thresholds
    • at various levels
  • Data standards
  • Data strategy:
    • in big data analytics operating model
    • as data management component
  • Data tracing
    • case study of
    • data lineage through
    • defined
    • methodology for
    • statistical sampling
  • Decay, data
  • Defaulting customers' behavior patterns case study
  • Defects
  • Defect-free processes/products
  • Defective units
  • Define, Improve, Analyze, and Control (DAIC) approach
    • Assess phase in
    • Control phase in
    • Define phase in
    • Improve phase in
    • in issues resolution
    • Six Sigma methodologies
  • Define, Measure, Analyze, Design, and Verify (DMADV)
  • Define, Measure, Analyze, Improve, and Control (DMAIC) methodology. See also Six Sigma
  • Define phase (DAIC approach)
  • Degrees of freedom (df)
  • Deming, W. Edwards
  • Dependency profiling
  • Descriptive analytics
  • Design for Six Sigma (DFSS) methodology
  • Determinant of matrix
  • df (degrees of freedom)
  • DFSS, see Design for Six Sigma methodology
  • Diagnostic analytics
  • Dimensions (data quality)
    • assessing data quality across
    • in DQ assessment
    • translating into DQ rules
  • Discrete CDEs, association analysis for
  • Discrete data
  • Discrimination and classification method
  • DMADV, see Define, Measure, Analyze, Design, and Verify
  • DMAIC (Define, Measure, Analyze, Improve, and Control) methodology. See also Six Sigma
  • Documentation, of current state
  • DQ, see Data quality
  • DQA, see Data quality analyst
  • DQOM, see Data quality operating model
  • DQPC (data quality practices center)
  • DQ projects, in DQOM
  • DQ rules, see Data quality rules
  • DQ scorecards, see Data quality scorecards
  • DQ (data quality) scores
  • Drill-down analysis, heat maps for
  • Duplicate avoidance (DQ dimension)
  • Dynamic signal-to-noise (S/N) ratios

E

  • Effectiveness, of DQ improvement
  • 80/20 rule
  • Enablers, in DQPC
  • Enterprise CDEs
  • Enterprise CDE rationalization matrix
  • Enterprise (data quality) scorecard. See also Data quality (DQ) scorecards
  • Error records
  • Establishment of DQ program
  • EWMA (exponentially weighted moving average) control charts
  • Executive sponsor
  • Expected variation. See also Common cause variation
  • Exponentially weighted moving average (EWMA) control charts

F

  • Feed-forward (backpropagation) method
  • Finance company case study
  • Financial crisis, inadequate information technology and
  • Fisher, R. A.
  • Fisher's discriminant function
  • F-ratio
  • Functional variability of process, losses due to
  • Funnel approach
    • association analysis for discrete CDEs
    • Basel II case study
    • correlation analysis for continuous CDEs
    • regression analysis for continuous CDEs
    • signal-to-noise ratios analysis

G

  • Gap analysis
  • Gartner analytics ascendency model
  • Gaussian distribution
  • Gear motor assembly case study
  • General Electric
  • Global financial crisis, inadequate information technology and
  • Governance:
    • around metadata management
    • in big data analytics operating model
    • charter for
    • data
    • in DQPC
    • of DQ program
    • of metadata
  • Gram-Schmidt process (GSP)
  • Granese, Bob, 43n.1

H

  • Hadoop
  • Harmful side effects, losses due to
  • Heat maps:
    • defined
    • for DQ scorecards

I

  • IBM
  • Improvement:
    • commissioning efforts for
    • in DAIC Improve phase
    • in DQPC
    • establishing baseline for
    • measuring effectiveness of
  • Improve phase (DAIC approach)
  • Individual–moving range (X-MR) charts
  • Information objects
  • Information system testing. See also Mahalanobis-Taguchi Strategy (MTS)
    • constructing combination tables
    • finance company case study
    • method of
    • MTS software testing
    • orthogonal arrays in
    • software company case study
    • study of two-factor combinations
    • typical arrangement for
  • Information technology (IT), global financial crisis and
  • Innovation, data, see Data innovation
  • Institute of International Finance
  • Integration, data
  • Integrity (DQ dimension)
  • Inverse matrix
  • Issues, data quality, see Data quality issues
  • Issues resolution (IR)
    • DAIC approach for
    • in DQOM
    • process of
    • process quality (Six Sigma) approach
    • reengineering of (case study)
    • skilled resources for
  • IT, global financial crisis and

J

  • Jeopardy, Watson on
  • Joyce, Ian
  • Juran, J. M.

L

  • Larger-the-better type S/N ratio
  • Levels (of factors)
  • Lineage of data
  • Linear discriminant function
  • Loss due to poor data quality
    • categories of
    • forms of
    • impact of. See also Quantifying impact of DQ
  • Loss function
  • Loss to society
  • Lost opportunities
  • Lower control limits

M

  • McKinsey & Company
  • Mahalanobis, P. C.
  • Mahalanobis distance (MD)
    • basic calculation of
    • in big data analytics
    • calculated with Gram-Schmidt process
    • purpose of
  • Mahalanobis space (MS)
  • Mahalanobis-Taguchi Strategy (MTS)
    • client experience improvement case study
    • defaulting customers' behavior patterns case study
    • direction of abnormals in
    • gear motor assembly case study
    • GSP calculation in
    • marketing case study
    • medical diagnosis example of
    • orthogonal arrays in
    • S/N ratios in
    • software testing with
    • stages in
    • variable set for detecting abnormal conditions in
  • Management:
    • analytics
    • in big data analytics operating model
    • change
    • data
    • metadata
  • MapReduce
  • Marketing case study
  • Marketing costs
  • Matrix(-ces):
    • adjoint of a square matrix
    • cofactors
    • correlation
    • defined
    • determinant of
    • inverse
    • nonsingular
    • singular
    • square
    • transpose of
  • Matrix theory
  • MD, see Mahalanobis distance
  • Mean square (MS)
  • Measurement:
    • in Control phase (DAIC approach)
    • with DQ scorecards
    • of effectiveness
    • in Six Sigma
  • Measurement system analysis. See also Information system testing
  • Metadata:
    • formalizing change management process for
    • means for gathering, managing, and updating
    • resources for defining, gathering, and managing
    • updating
  • Methodology (in DQOM)
    • assess current state of DQ
    • conducting current-state analysis
    • data quality projects
    • discussion on establishing DQ program
    • establishing DQ program
    • monitoring and measurement
  • Monitoring:
    • in Control phase (DAIC approach)
    • defining operating guidelines for
    • in DQOM
    • in DQPC
    • ongoing
    • relevance of SPC in
    • skilled resources for
  • Monitoring and reporting (M&R) function. See also Data quality (DQ) scorecards
  • Motorola
  • Moving range (MR)
  • M&R (monitoring and reporting) function. See also Data quality (DQ) scorecards
  • MS (Mahalanobis space)
  • MS (mean square)
  • MTS, see Mahalanobis-Taguchi Strategy
  • Multiple process control
  • Multiple regression analysis
  • Multivariate process control charts
  • Multivariate systems:
    • correlations in
    • defined
    • design and development of, see Mahalanobis-Taguchi Strategy [MTS]

N

  • Nested analysis of variance (nested ANOVA)
  • Network analysis
  • Nominal-the-best type S/N ratio
  • Nondynamic signal-to-noise (S/N) ratios
  • Nonsingular matrix
  • Nonuniformity
  • Normal distribution
  • Normal group (in MTS)
  • NoSQL databases
  • np-charts

O

  • OAs, see Orthogonal arrays
  • Ongoing monitoring:
    • control processes for
    • in DQOM
  • Operating model, see Data quality operating model (DQOM)
  • Operations leads
  • Orthogonal arrays (OAs)
    • in information system testing
    • for minimizing number of test combinations
    • in MTS
    • three-level
    • two-level
  • Oversampling

P

  • Pareto analysis
    • CDE prioritization through
    • in data tracing
  • Pareto diagram
  • Pareto principle
  • PCA (principal component analysis)
  • p-charts
  • Percent defective charts
  • Plan:
    • data collection
    • project
  • Poor-quality data:
    • cost of
    • loss due to
    • managing indicators of
    • quantifying impact of, see Quantifying impact of DQ
  • Predictive analytics
  • Preparatory analytics
  • Prescriptive analytics
  • Principal components
  • Principal component analysis (PCA)
  • Prioritization matrix:
    • CDE, using Pareto analysis
    • CDE rationalization matrix
    • for data quality issues
    • for reengineering IR process
  • Prioritization of CDEs, see Funnel approach
  • Process CDE potential index
  • Process quality:
    • linkage between data quality and. See also Issues resolution [IR]
    • measuring, see Six Sigma
  • Process variation, stable vs. unstable
  • Production environment, monitoring
  • Profiling, see Data profiling
  • Program charter
  • Program communications
  • Program management professions
  • Program strategy
  • Project charter
  • Project management professions
  • Project manager, role of
  • Project methodologies. See also individual methodologies
  • Project plan, creating
  • Project sponsor, role of
  • Proportion charts (p-charts)
  • p-value

Q

  • Quality loss function (QLF)
  • Quantifying impact of DQ
    • building quantification framework
    • cost waterfall
    • prioritization matrix
    • remediation and ROI
    • trading office example of

R

  • Random sample approach
  • Range chart
  • Rao, C. R.
  • Rao's test
  • Rationalization matrix, CDE, see CDE rationalization matrix
  • Rational subgrouping
  • Reengineering issues resolution case study
  • Reference group (in MTS)
  • Regression analysis:
    • in Basel II case study
    • for continuous CDEs
    • multiple regression
    • need for
    • purpose of
    • stepwise regression
  • Relationship profiling
  • Reliability-based analytics
  • Remediation:
    • commissioning efforts for
    • in quantification framework
  • Reports, determining set of
  • Reporting:
    • capabilities gap analysis report
    • monitoring and reporting function. See also Data quality [DQ] scorecards
    • relevance of SPC in
  • Resources, in DQOM
  • Return on investment (ROI):
    • in analysis of solutions
    • in quantification framework
  • Review:
    • in issues resolution
    • operational-level
  • Rework costs
  • Risk-weighted assets (RWAs)
  • ROI, see Return on investment
  • Role clarity
  • Root-cause analysis
  • Run charts
  • RWAs (risk-weighted assets)

S

  • Sample:
    • defined
    • selection of
  • Sample size:
    • in data tracing
    • defined
  • Sampling, statistical, see Statistical sampling
  • Sampling frequency
  • Sampling parameters
  • S-chart
  • Scope, defining
  • Scorecards. See also Data quality (DQ) scorecards
  • Service-level agreements, establishing
  • Shewhart, Walter
  • Shi, Chuan
  • Sigma
  • Sigma levels
  • Signal-to-noise (S/N) ratios
    • dynamic
    • equations for
  • Signal-to-noise (continued)
    • larger-the-better type
    • in MTS
    • nominal-the-best type
    • nondynamic
    • smaller-the-better type
  • Signal-to-noise ratios analysis
    • in Basel II case study
    • in MTS
  • Singular matrix
  • Six Sigma
    • Design for Six Sigma
    • development of methodologies
    • importance of measurement in
    • and issues resolution
    • magnitude of sigma levels in
  • Six Sigma processes
  • Skilled resources, in DQOM
  • Smaller-the-better type S/N ratio
  • Small shifts, control charts to detect
  • SMEs, see Subject-matter experts
  • S/N ratios, see Signal-to-noise ratios
  • Software testing:
    • case study
    • with MTS
  • Solution strategy (for DQ)
  • SPC, see Statistical process control
  • Special cause variation
  • Specification, business
  • SPM (statistical process monitoring)
  • Sponsors:
    • executive
    • project
  • Square matrix
  • SS (sum of squares)
  • Standards, data
  • Standardized distance
  • Standardized variables
  • State-transition model profiling
  • Statistical process control (SPC)
    • common and special cause variation in
    • control charts
    • in data tracing
    • in DQ monitoring and reporting
    • in establishing thresholds
    • goal of
  • Statistical process monitoring (SPM)
  • Statistical sampling:
    • in data tracing
    • in reducing number of CDEs. See also Funnel approach
  • Steady state:
    • implementation of
    • monitoring and control activities in
  • Stepwise regression
  • Strategy. See also Mahalanobis-Taguchi Strategy (MTS)
    • data
    • for data quality
    • of DQ program
    • technology
  • Subgroups
  • Subject-matter experts (SMEs):
    • in assessment of data quality
    • in cost assessment
    • in establishing thresholds
  • Sum of squares (SS)
  • System performance, see Information system testing

T

  • Taguchi, Genichi
  • Target
  • Target quality values
  • Technology and operations analyst, role of
  • Technology environments, configuring
  • Technology infrastructure, in DQOM
  • Technology leads
  • Technology strategy, in big data analytics operating model
  • Terminologies, for big data processing
  • Test of additional information (Rao's test)
  • Three-level orthogonal arrays
  • Thresholds:
    • defined
    • for DQ scorecards
  • Timeliness (DQ dimension)
  • Tracing, see Data tracing
  • Transpose of a matrix
  • Triage, in issues resolution
  • Twin charts. See also Variable control charts
  • Two-level orthogonal arrays
  • Two-way ANOVA

U

  • u-charts
  • Uncontrollable variation
  • Undersampling
  • Uniformity
  • Updating metadata
  • Upper control limits

V

  • Validity (DQ dimension)
  • Variables, standardized
  • Variable control charts
  • Variable data
  • Variation:
    • common vs. special cause
    • in Six Sigma
    • sources of
    • in statistical process control
  • Vision, for analytics

W

  • Waste
  • Watson (IBM computer)
  • Western Electric Run rules
  • Work streams:
    • conducting current-state analysis
    • data quality projects
    • establishing DQ program
    • monitoring and measurement

X

  • X-MR (individual–moving range) charts
  • -R (average-range) charts
  • -S (average-standard deviation) chart
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