The document outlines a training program in Addis Ababa, Ethiopia, focused on livestock data management and quality assessment, scheduled for two batches in November 2023 and January 2024. It emphasizes the importance of data collection, organization, presentation, analysis, and interpretation for informed decision-making in the livestock sector, discussing various data quality dimensions such as validity, integrity, precision, reliability, and timeliness. Additionally, it addresses the data lifecycle, including creation, storage, usage, archival, and destruction, while advocating for rigorous data management processes to enhance accountability and service delivery.

1. 1
National Livestock Data
Use Training
Addis Ababa, Ethiopia
1st
Batch November 7-9, 2023
2nd
Batch January 15-17, 2024

2. Data Quality
2
Data Quality Issues

3. 3
Introduction
1. Collection of Data
2. Organization of Data
3. Presentation of Data
4. Analysis of Data
5. Interpretation of Data/
Inference

4. DATA
What is it?
5,677.00 1,552.31 1,552.31 0.00 1,552.31 5,677.00 1,552.31 1,552.31 0.00 1,552.31
4,894.00 1,338.21 1,338.21 1,338.21 4,894.00 1,338.21 1,338.21 1,338.21
325.00 88.87 88.87 88.87 325.00 88.87 88.87 88.87
458.00 125.24 125.24 125.24 458.00 125.24 125.24 125.24
7,378.00 2,017.43 2,017.43 0.00 2,017.43 7,378.00 2,017.43 2,017.43 0.00 2,017.43
4,577.00 1,251.53 1,251.53 1,251.53 4,577.00 1,251.53 1,251.53 1,251.53
2,801.00 765.90 765.90 765.90 2,801.00 765.90 765.90 765.90
2,476.00 677.04 677.04 0.00 677.04 2,476.00 677.04 677.04 0.00 677.04
2,476.00 677.04 677.04 677.04 2,476.00 677.04 677.04 677.04
2,570.00 702.74 702.74 0.00 702.74 2,570.00 702.74 702.74 0.00 702.74
1,457.00 398.40 398.40 398.40 1,457.00 398.40 398.40 398.40
651.00 178.01 178.01 178.01 651.00 178.01 178.01 178.01
462.00 126.33 126.33 126.33 462.00 126.33 126.33 126.33
2,151.00 588.17 588.17 0.00 588.17 2,151.00 588.17 588.17 0.00 588.17
5,677.00 1,552.31 1,552.31 0.00 1,552.31 5,677.00 1,552.31 1,552.31 0.00 1,552.31
4,894.00 1,338.21 1,338.21 1,338.21 4,894.00 1,338.21 1,338.21 1,338.21
325.00 88.87 88.87 88.87 325.00 88.87 88.87 88.87
458.00 125.24 125.24 125.24 458.00 125.24 125.24 125.24
7,378.00 2,017.43 2,017.43 0.00 2,017.43 7,378.00 2,017.43 2,017.43 0.00 2,017.43
4,577.00 1,251.53 1,251.53 1,251.53 4,577.00 1,251.53 1,251.53 1,251.53
2,801.00 765.90 765.90 765.90 2,801.00 765.90 765.90 765.90
2,476.00 677.04 677.04 0.00 677.04 2,476.00 677.04 677.04 0.00 677.04
2,476.00 677.04 677.04 677.04 2,476.00 677.04 677.04 677.04
2,570.00 702.74 702.74 0.00 702.74 2,570.00 702.74 702.74 0.00 702.74
1,457.00 398.40 398.40 398.40 1,457.00 398.40 398.40 398.40
651.00 178.01 178.01 178.01 651.00 178.01 178.01 178.01
462.00 126.33 126.33 126.33 462.00 126.33 126.33 126.33
2,151.00 588.17 588.17 0.00 588.17 2,151.00 588.17 588.17 0.00 588.17
4

5. DATA
o Data are raw facts or other
findings which, by themselves,
are of limited values to decision
makers.
o Data refers to any kind of
information that can be collected,
stored, and processed.
Information
o Information is the result of organizing,
processing, and interpreting data in a
way that puts them into context,
uncovers patterns or problem areas,
and thus transforms data into facts
that are useful to decision makers.
Data Processing Information
Raw facts
100 participants
Aggregate
Average, trends
Human sensible,
concise, accurate,
timely

6. 6
Creation
• Manual data entry
• External Acquisition
• Capture from device
Storage
Usage
Archival
Destruction
• Security
• Backup and recovery
• Data viewing, processing, modification
and saving
• Data sharing
•Data archived & protected
•purging
1.Data Creation
•The first phase of the data lifecycle is the
creation/capture of data from d/t forms e.g. PDF,
image, Word, database.
The three types data capturing methods
•Data Acquisition: acquiring already existing data
which has been produced outside the organization
•Data Entry: manual entry of new data by
personnel within the organization
•Data Capture: capture of data generated by
devices used in various processes in the
organization
2.Storage
Once data has been created within the organization, it needs to be stored
and protected, with the appropriate level of security applied. A robust
backup and recovery process should also be implemented to ensure
retention of data during the lifecycle.
3. Usage
During the usage phase of the data lifecycle, data is used to support activities in the
organization. Data can be viewed, processed, modified and saved. An audit trail
should be maintained for all critical data to ensure that all modifications to data are
fully traceable.
4. Archival
Data Archival is the copying of data to an environment where it is stored in case it is needed again in an
active production environment, and the removal of this data from all active production environments.
A data archive is simply a place where data is stored, but where no maintenance or general usage
occurs. If necessary, the data can be restored to an environment where it can be used.
Data Lifecycle
5. Destruction
The volume of archived data inevitably grows, and while you may want to save all your data forever, that’s
not feasible. Storage cost and compliance issues exert pressure to destroy data you no longer need. Data
destruction or purging is the removal of every copy of a data item from an organization. It is typically done

7. Data quality refers to the worth/
accuracy of the data/ information
collected
What is Data Quality?

8. ● Interventions, programs in the sector is
“evidence-based”
● Data quality  Data use
● Accountability
Why Data Quality In Ethiopian livestock
sector ?

9. Conceptual Framework of Data
Quality
Service delivery points
(the 5 prioritized system)
Intermediate aggregation levels
(e.g. districts/ regions, etc.)
M&E Unit in the ministry, or
Country level
Data
management
and
reporting
system
Functional components of Data
Management Systems Needed to Ensure
Data Quality
M&E Structures, Roles and Responsibilities
Indicator definitions and reporting guidelines
Data collection and reporting forms/tools
Data management processes
Data quality mechanisms
M&E capacity and system feedback
Dimensions of Data Quality
Validity, Integrity, Precision, Reliability,
Timeliness,
Quality Data

10. Data Quality Dimensions (DQD)
Every organization needs to develop and document its means to check the quality of their
data, so it needs to define the data quality dimensions.
Data Quality Assessment involves checking data against five criteria: VIP-RT
Validity
Integrity
Precision
Reliability
Timeliness
DQ help us to determine areas of poor data quality & help point us to potential solutions.

11. Validity
Does the data adequately represent
performance & are thus valid.
● The key question for validity is –
does this data actually represent
what we think it does?
E.g., in the animal data recording,
the ear tag is 10 digit, 9 or 11 digit is
invalid data
Face Validity: Is there a solid, logical relation
between the activity or program and what is
being measured? Is the indicator direct?
Measurement Construct Validity: Are
measurement tools / procedures well designed,
and defensible or is there potential for:
 Non sampling errors
 Sampling / representation errors
 Memory errors
 Self-presentation bias
Transcription Validity: Are transcription / data
entry and collation procedures sound and is the
data entered/ tallied correctly?
Types of Validity error

12. Integrity
Are data free of ‘untruths’ (introduced by
either human or technical means, willfully
or unconsciously) and therefore have
integrity?
● Manipulation / Bias - Are mechanisms are
in place to reduce the possibility that data
are manipulated for political or personal
reasons?
● Unconscious integrity issues when
organizations offer positive or negative
incentives to encourage data collection
● Willful integrity issues - when a person or
organization purposefully provides false
data
Precision
● Does the data have an acceptable
margin of error and is thus precise?
● For the data set to be “precise”, we
need to ensure that data has an
acceptable margin of error
● The margin of error should be
acceptable given the likely management
decisions to be affected -consider the
consequences of the program or policy
decisions based on the data
E.g. recording age of animal using teeth…

13. Reliability Timeliness
Are data collected frequently and are they
current and thus timely?
Frequency
• Data are available on a frequent enough
basis to inform management decisions.
• A schedule of data collection, collation,
analysis and reporting is in place that meets
the country livestock needs?
Currency
• The data are reported in the most current
timeframe practically available
• The data are reported as soon as possible
after collection
• The date of collection is clearly identified in
reports
 Are data collection processes stable &
consistent over time and are thus
reliable.
 Consistency: Consistent data collection
processes are used from year to year,
location to location, data source to data
source
 Internal Quality Control: Procedures
are in place for periodic review of data
collection, maintenance, and
processing.
 Transparency: Data collection, cleaning,
analysis, reporting, & quality
assessment procedures are documented
in writing and Data quality problems
are clearly described in reports.

14. Reproducibility vs Validity
● Reproducibility
○ the degree to which a measurement provides the same result each time it is performed on a
given subject or specimen
Data standard document.
● Validity
○ from the Latin Validus - strong
○ the degree to which a measurement truly measures (represents) what it purports to measure
(represent)

15. Reproducibility vs Validity
● Reproducibility
Reliability, Repeatability, Precision, Variability, Dependability, Consistency, Stability
● Validity
○ Accuracy

16. Relationship Between Reproducibility and
Validity
Good Reproducibility
Poor Validity
Poor Reproducibility
Good Validity

17. Relationship Between Reproducibility and
Validity
Good Reproducibility
Good Validity
Poor Reproducibility
Poor Validity

18. A B C D E F G H I J
95 98 100 100 100 102 103 139 150 160
Outliers IMPACT the mean!
Mean (114.6) 85.43
Median (101) 100
Outliers IMPACT the mean!
Outlier detection
An outlier is an observation or data point that is significantly
different from the other observations in a dataset.

19. A B C D E F G H I J
0
20
40
60
80
100
120
20
40
45
40
30
100
35
25
33
60
LITTER OF MILK PER DAY PER CAW

20. Sources of Measurement Variability
● Observer
■ within-observer
■ between-observer
● Instrument
■ within-instrument
■ between-instrument
● Subject
■ within-subject

21. 21
Thank
you!
Questions?