As we are super data nerds, you may be surprised that our answer to the question "Is data ever useless?" is a resounding YES. Organizations sometimes proudly flaunt their data, secure in the sense that ‘having data’ was trending, totally unaware that their data may in fact be of little value.
We know that data collection can be time-consuming and resource-intensive, so it is heart-breaking for us when we see these three common mistakes that seriously undermine the value of data.
1. No (or limited) attention to data quality
Data quality speaks to the state of completeness, consistency, timeliness, and accuracy of data that renders it useful. Data quality affects the efficiency and effectiveness of making decisions about a programme. Poor data quality can have major consequences including wastage of resources, loss of stakeholder confidence and support, and missed opportunities to identify areas of strength or areas for increased reach and impact.
2. No data management system
And to be clear, by ‘system’ we mean a data management process which may or may not include a fancy software programme with automatic dashboard updates (although having one does help so long as it is well thought out and managed properly but more on that below).
A lack of a data management process is intricately linked to data quality. A data management system includes the processes that transform data into information that can be used to guide decision-making. It requires you to be very intentional with all the steps from data collection through to analysis. Of critical importance is the identification of the responsible persons and dedicated times to facilitate the organization of your data. This ensures that your data is retrievable, secure, timely, and able to be updated as applicable. Data quality assurance is a key component of this process.
3. Data does not align to its intended purpose
Often, data collected is not able to answer the question it needs to. For example, comparisons of pre- and post-test scores for training will not necessarily tell you which areas of the training were less understood or what aspects worked well. Thinking through what the data will be used for prior to collection will guide your data management process and help you avoid this issue later.
However, do not throw away your piles of data just yet and give up on your journey to results-based management. The good news is that useless data can be improved with a little attention and know-how. Contact us for help with improving your data today so that you can actually use it.
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