This is a phrase we hear quite often from our clients when we jointly determine the scope of a source. Often this is a sign that it is time to pay close attention and share our experience. Because although it seems tempting to store as much data as possible for potential future analyses, this does not yield the most optimal result. Below I set out the most important considerations.
Quantity over quality?
When collecting data, organisations can prioritise quantity over quality, leading to arbitrary data collection practices. This approach can diminish the relevance and usability of the collected data, making it harder to extract meaningful insights. As organisations collect more and more data, they can quickly reach a point where the volume of information becomes overwhelming: "Where was this again?" "What was the idea behind this?".
This abundance can lead to overload, making it harder to identify relevant insights and subsequently act on them properly.
Imagine you may choose 3 countries for a world trip, and I give you the choice from 50 or from 5 countries. Which list would you have ready sooner?
Diminishing returns
Although collecting data can initially lead to valuable insights, there comes a point of diminishing returns. This also applies to the question:
"Can we unlock the data at a smaller interval?"
From a certain moment, additional data no longer contributes significantly to gaining new insights or improving decision-making processes. Additionally, there is the risk that analysis points towards a solution that is not realistic. For example, the data might indicate that during certain quarter-hours of the day you have almost no customers in the shop. Hiring a security guard during those quarter-hours might not be necessary according to the data. A nice insight, but with little impact, because that security guard is certainly not going to cycle home and back during that quarter of an hour. This way you spend time on a solution that yields nothing.
Costs and resources
Managing and storing large volumes of data requires not only time and money but also other things, such as good infrastructure, sufficient storage space and personnel. The costs associated with collecting, storing and processing data can easily outweigh the benefits, particularly if you do not use the data effectively. As the volume of data grows and you gradually lose oversight, the efficiency of data use also decreases. Without a predetermined plan, or well-formulated problem you want to solve, you are sucked into an endless rabbit hole. If you then have to shape the problem or intended analysis along the way, you are guaranteed to lose valuable time. Is that analysis still as valuable, if you have already squandered half of the potential gains because you needed so much time to arrive at this insight? Additionally, there is the risk of wasting time and energy on an analysis simply because we have already invested so much time and energy in it (sunk cost fallacy).