Why should data quality be important to you?

If you are going to use data for decision-making, the integrity of data is of utmost importance: you should trust the integrity of the data you receive. Data integrity – or quality – enables you to make educated and insightful decisions and build effective programs. But what is data quality? According to Wikipedia, there is a plurality of definitions of data quality. Still, generally, “data is considered high quality if it is fit for its intended use”.

SCOPEinsight has built a computerized system of data collection along with cloud-based storage to better manage data. Over our 11 years of existence, we have built procedures and checks to ensure that we address the following six MAIN data quality elements. These are 1. Validity, 2. Accuracy, 3. Integrity, 4. Completeness, 5. Consistency, and 6. Timeliness.

Validity: Validity demonstrates how accurately a method measures something. Our assessments measure professionalism. We have analyzed the correlation between the data and various outcomes (like profits, sustainability, market access), and this reinforced the notion that we measure the correct elements to make conclusions about professionalism. Our methodology (and questions) is built upon internationally accepted concepts and models, like the IWA29.
Accuracy: We have a rigorous training protocol to train assessors who collect the data. Aside from being acclimated to the assessment tool and questions, the assessors receive training in soft skills such as asking probing questions to obtain the correct data. Additionally, our system has automated quality metrics to check that all assessments and assessors are delivering a reliable and relevant data product to our clients. Furthermore, our quality control staff and procedures ensure that answers make sense in the context of the organization and value chain.
Integrity: Data integrity is how accurate the relationships are between data elements and data sets. To ensure the integrity of our data, we employ quality assessors who check each and every assessment. In addition to checking the assessments, the quality controllers rate each assessor on the quality of data they collect.
Completeness: Data completeness refers to whether there are any gaps in the data from what was expected to be collected, and what actually was collected. The SCOPE App was designed to not only facilitate the data collection but also to ensure the completeness of the data. For example, one must fill out all the answers in the questionnaire to proceed to upload the information.
Consistency: Data consistency means that the measurement of variables in each assessment meets the same, standards for quality, accuracy, completeness & validity. SCOPEinsight has dynamic benchmarking against 1000s of assessments helps our system identify outliers, data-entry errors, and exceptional cases.
Timeliness: Traditionally, field research would take months in the development sector. Because our data is used for decision making, we ensure that clients receive their reports within a month of conducting an assessment.

Finally, we approach each assessment as a partnership between our clients and ourselves. The quality of the data is a joint effort and shared responsibility. We have built into the assessment process several quality checks and data integrity mitigation factors. At the beginning of our assessment journey with each client, we provide the requirements for hiring/engaging assessors. During the assessor training, our quality control validates that the assessors meet this requirement.

So, when you receive your assessment results from SCOPEinsight, you can be confident that the data is of the highest quality. Reliability of the data will enable you to make decisions about providing technical assistance to agribusinesses, or about the suppliers with whom you’d like to work or to make financing decisions.

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