Why you should care about data quality?
And how we achieve it at SCOPEinsight.
Imagine this: your doctor prescribes diet and exercise to help you get fit. You’re motivated and decide to make some lifestyle changes. However, like most of us, sometimes things like holidays get in the way and you indulge a bit. That shouldn’t be such a problem, though. At the next doctor’s visit, the doctor tells you that you’ve gained weight! Initially, you’re shocked because the scale at home indicates that you’re 10 kilos lighter than the doctor’s scale shows, and you’ve added 10,000 steps a day. The doctor looks at your pedometer and tells you that its calibrated incorrectly, and you’ve only been doing 2,000 steps a day. Without the proper data, you couldn’t course correct.
If you are going to use data for decision making, the integrity of data is of utmost importance: you should be able to trust the integrity of the data you receive. Data integrity – or quality – enables you to make educated and insightful decisions and allows you to build effective programs. But what is data quality? According to Wikipedia, there is a plurality of definitions of data quality, but generally “data is considered high quality if it is fit for its intended use.”
At SCOPEinsight, the quality of data we give you is important to us. The quality of data we provide is characterized by the following four MAIN elements: 1. Accuracy, 2. Completeness, 3. Validity, and 4. Consistency.
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.
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.
Validity: Validity demonstrates how accurately a method measures something. Our assessments were designed to 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 are measuring the right elements to make conclusions about professionalism. We also based our methodology (and questions) on internationally accepted concepts and models.
Consistency: Data consistency means that the measurement of variables in each assessment meets the same, standards for quality, accuracy, completeness & validity. At SCOPEinsight, we employ both quality control staff and automated quality metrics to check that all assessments and assessors are delivering a reliable and relevant data product to our clients. Our quality control staff and procedures ensure that answers make sense in the context of the organization and value chain. Dynamic benchmarking against 1000s of assessments helps our system identify outliers, data-entry errors, and exceptional cases.
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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