Data Quality Assessments for USAID projects

Data quality is a recurring issue of concern across our customer base. USAID offers useful guidance on how Implementing Partners can help the agency make better decisions by using the highest quality data available.


This short article describes how ExInsight can help IPs and other customers meet and exceed the five data quality standards that all data from performance monitoring indicators must meet.


1. Validity- Data clearly and adequately represent the intended result.


Asking quantitative questions in a structured way reduces uncertainty. A common example we see when consulting with clients is the use of free-text, open-ended questions which lead to subjective response.


An example question might be 'describe the health of livestock'. When asked this way, the response will be varied, difficult to compare, and not always valid.

An iteration of that question might be a true or false type question. For example, 'Are the livestock healthy?' which would be followed by yes or no responses. The answers might still lack validity, though as the question is subjective. A more developed question might make use of options, where the respondent is guided to make a choice based on a fixed number of options. This Pictorial Evaluation Tool for use in agricultural surveys is a good example of a question set that promotes validity.



2. Integrity- Data have safeguards to minimize the risk of transcription error or data manipulation.


The first step is to move away from paper-based surveys. The next is to use systems that reduce the margin for error. Avoiding free-text responses increases integrity by avoiding mistakes such as spelling errors, mis-use of acronyms, or duplication.


Finally, a permission based system means that data cannot be manipulated and changed without record. The geo-tagging and time-stamping of information also adds certainty to who said what, where and when. The ability to audit information via logs is of crucial importance in providing oversight of the data processed in ExInsight.



3. Precision- Data have sufficient level of detail to permit management decision making.


While a question and answer based system is useful, if structured correctly, there are occasions where the information manager needs to know more. In the worst cases, this can mean redeploying a team to a difficult environment to collect more data. Collecting imagery, video, and GPS data are examples where precision can be enhanced via ExInsight, and where the probability of having to re-collect data is reduced.


4. Reliability- Data reflect consistent collection processes and analysis methods over time.


A common question we are asked is 'how do we know if the respondent was there?' The answer is meta-data. ExInsight can record meta data such as GPS data, the phone's IMEI, time-stamping of responses, and more, with permission. This builds certainty that the data originated as expected.


5. Timeliness- Data are available at a useful frequency, are current, and timely enough to influence management decision making.


A networked, databased systems, built with the latest best practice facilitates the real-time sharing of information. ExInsight is developed with the latest technology stack to realise the timely transfer of information, even in areas of low or disturbed connectivity.


Further reading


USAID Guidance on Conducting Data Quality Assessment (DQA)



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