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ยฉ 2026 Logic Lab LLC. All rights reserved.

How It Works

A structured approach to assessing data quality using internationally recognized dimensions.

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Score Each Dimension

Rate 15 criteria across five dimensions using rubric anchors that describe excellent, adequate, and poor performance.

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Review Your Results

See an overall score out of 75 with per-dimension breakdowns. Color-coded bars highlight strengths and weaknesses.

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Get Recommendations

Receive prioritized improvement recommendations based on your lowest-scoring dimensions.

Data Quality Assessment Scorecard

Score your data quality across five standard dimensions using rubric-based criteria.

Validity

The extent to which data measures what it is intended to measure.

Indicator definitions are clear and measurable

5All indicators have SMART definitions with specified numerators and denominators where applicable.
3Most indicators have written definitions but some lack specificity in measurement method.
1Indicator definitions are unclear, contradictory, or missing for key indicators.

Data collection tools are appropriate

5Tools have been pilot-tested, validated for the context, and directly aligned to each indicator.
3Tools are designed for the indicators but have not been recently validated or pilot-tested.
1Tools do not clearly measure the intended indicators or have known design issues.

Operational procedures ensure consistent measurement

5Detailed SOPs exist, all staff are trained, and regular calibration exercises are conducted.
3Basic procedures are documented but training is inconsistent or no calibration is done.
1Data collection is ad-hoc with no documented procedures or training.

Results

Overall Score

0/75

0/15 criteria scored

Validity0/15
Reliability0/15
Timeliness0/15
Precision0/15
Integrity0/15

The Five Data Quality Dimensions

1

Validity

The extent to which data measures what it is intended to measure.

2

Reliability

The consistency of data collection and the ability to produce the same results under the same conditions.

3

Timeliness

Whether data are collected, processed, and reported on schedule to support timely decisions.

4

Precision

Whether data have enough detail and accuracy to measure the intended changes.

5

Integrity

Whether data are protected from unauthorized access, manipulation, or bias.

Data Quality Resources

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Data Quality Guide

Learn about data quality management practices for M&E programs.

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SMART Indicator Checker

Ensure your indicators are well-defined before assessing data quality.

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Data Collection Guide

Strengthen data collection processes to improve quality at the source.

Need Help Improving Data Quality?

Our advisory services help M&E teams build data quality systems that meet donor requirements and produce trustworthy evidence.

See How We WorkTalk to an Expert