HIS Lesson 8 Flashcards

1
Q

has become a major concern for
large companies especially in the areas of customer
relationship management data integration,
and regulation requirements.

A

Data quality

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2
Q

is the overall utility of a dataset(s) as
a function of its ability to be processed easily and
analyzed for a database, data warehouse, or data
analytics system.

A

Data quality

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3
Q

signifies the data’s appropriateness
to serve its purpose in a given context.

A

Data quality

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4
Q

Aspects of Data Quality

A
  • Accuracy
  • Completeness
  • Reliability
  • Relevance
  • Consistency
  • Presentability
  • Accesability
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5
Q

is a tool
that allows the use of small random samples to
distinguish between different groups of data elements (or
lots) with high and low data quality.

A

Lot Quality Assurance Sampling (LQAS)

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6
Q

methodology provides real-time planning and
management information. It uses small sample
sizes to classify health or administrative
geographical areas , to inform if these areas
have achieved or not a pre-determined target
for a given indicator.

A

Lot Quality Assurance Sampling (LQAS)

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7
Q

tool is a simplified version of the Data Quality Audit
(DQA) tool which allows programs and projects to
verify and assess the quality of their reported data.

A

Routine Data Quality Assessment (RDQA)

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8
Q

is a multipurpose tool that is most
effective when routinely used.

A

Routine Data Quality Assessment (RDQA)

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9
Q

is a project management
tool that illustrates how a project is expected to progress
at a high level.

A

Implementation plan

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10
Q

Address the question,
“What do you want to accomplish?”

A

Define Goals/Objectives

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11
Q

Outline the deadline and
timelines in the implementation phase.

A

Schedule Milestones

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12
Q

Determine whether you have
sufficient resources, and decide how you will
procure those missing.

A

Allocate Resources

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13
Q

Create a
general team plan with overall roles that each team
member will play.

A

Designate Team Member Responsibilities

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14
Q

How will you determine
if you have achieved your goal?

A

Define Metrics for Success

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15
Q

analyzes information and
identifies incomplete or incorrect data.

A

Data Quality Tool

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16
Q

is the process of
detecting and correcting corrupt or inaccurate
records from a record set, table, or database and
refers to identifying incomplete, incorrect,
inaccurate or irrelevant parts of the data and then
replacing, modifying, or deleting the dirty or
coarse data.

A

Data cleansing

17
Q

the process
enhances the reliability of the information being
used by an organization

A

maintaining data integrity

18
Q

is a technologyenabled discipline in which business and
information technology work together to ensure
the uniformity, accuracy, stewardship, semantic
consistency and accountability of the
enterprise’s official shared master data assets.

A

Master data management

19
Q

involves combining data
residing in different sources and providing users
with a unified view of them.

A

Data integration

20
Q

refers to the decomposition of fields into component parts and formatting the values into consistent layouts

A

Parsing and Standardization

21
Q

is the modification of data values to meet the domain restrictions, constraints on integrity

A

Generalized Cleansing

22
Q

is the identification merging of related entries within across data sets

A

Matching

23
Q

refers to the analysis of data to capture statistics or metadata to determine the quality of data and identify data quality issues

A

Profiling

23
Q

refers to the deployment of controls to ensure conformity of data to business rules set by the organization

A

Monitoring

24
Q

is the enhancement of the value of the data by using related attributes from external sources

A

Enrichment

25
Q

which allow the
optimization of the alimentation process

A

Extract, Transform, Load (ETL) tools

26
Q

which generally integrates profiling, parsing,
standardization, cleansing, and matching processes.

A

Data Quality Management

27
Q

is a problem solving
method that identifies the root causes of problems or
events instead of simply addressing the obvious
symptoms.

A

Root cause analysis

28
Q

s is among the core building
blocks in the continuous improvement efforts of an
organization in terms of its operation dynamics,
especially in the way it handles information.

A

Root cause analysis

29
Q

aims to find various modes for failure within a system

A

Failure Mode and Effects Analysis (FMEA)

30
Q
  • It is used when there are
    multiple potential causes to a problem.
  • uses the Pareto principle
    which states that 20 percent of the work creates 80
    percent of the results.
A

Pareto analysis

31
Q

is used in risk
and safety analysis. It uses of Boolean logic to
determine the root causes of an undesirable event.

A

Fault Tree Analysis

32
Q

is used when the root
causes of multiple problems need to be analyzed all
at once. The problems are listed down followed by
the potential cause for a problem

A

Current Reality Tree (CRT)

33
Q

The diagram looks like a
fishbone as it shows the categorized causes and subcauses of a problem. This diagramming technique is
useful in grouping causes into categories

A

Fishbone or Ishikawa or Cause-and-Effect Diagram

34
Q

breaks a problem
down to its root cause by assessing a situation using
priorities and orders of concern for specific issues.

A

Kepner-Tregoe Technique

35
Q
  • Another technique for root cause analysis.
  • which diagnoses the
    causes of recurrent problems
A

Rapid
Problem Resolution

36
Q

data gathering and analysis of the findings

A

Discover

37
Q

creation of a diagnostic plan and identification
of the root cause through careful analysis of the diagnostic
data

A

Investigate

38
Q

fix the problem and monitor to confirm and validate
that the correct root cause was identified.

A

Fix