5 Data Granularity Mistakes That May Cost You | 7wData

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In the age of big data, the challenge is no longer accessing enough data; the challenge is figuring out the right data to use. In a past article, I focused on the value of alternative data, which is a vital business asset. Even with the benefits of alternative data, however, the wrong data granularity can undermine the ROI of data-driven management.

So how closely should you be looking at your data? Because the wrong data granularity could cost you more than you realize.

Simply put, data granularity refers to the level of detail of our data. The more granular your data, the more information contained in a particular data point. Measuring yearly transactions across all stores in a country would have low granularity, as you would know very little about when and where customers make those purchases. Measuring individual stores’ transactions by the second, on the other hand, would have incredibly high granularity.

The ideal data granularity depends on the kind of analysis you are doing. If you are looking for patterns in consumer behavior across decades, low granularity is probably fine. To automate store replenishment, however, you’d need much more granular data.

When you choose the wrong granularity for your analysis, you end up with less accurate and less useful intelligence. Think about how messy weekly store replenishment based only on yearly systemwide data would be! You’d continuously experience both excess stock and stockouts, amassing huge costs and high levels of waste in the process. In any analysis, the wrong data granularity can have similarly severe consequences for your efficiency and bottom line.

So are you using the correct data granularity for your business intelligence? Here are five common — and costly — data granularity mistakes.

Business intelligence needs to be clear and straightforward to be actionable, but sometimes in an attempt to achieve simplicity, people don’t dive deep enough into the data. That’s a shame because you will miss out on valuable insights. When data granularity is too low, you only see large patterns that arise to the surface. You may miss critical data.

In far too many cases, not looking closely enough at your data leads to compressing disparate trends into a single result. Businesses making this mistake end up with uneven results. They are more likely to have unpredictable and extreme outliers that don’t fit the overall pattern — because that pattern doesn’t reflect reality.

This is a common problem in many traditional supply chain forecasting systems. They can’t handle the level of granularity necessary to predict SKU-level demand in individual stores, which means that a single store may be dealing with both overstocks and stockouts at the same time. Automated systems powered by AI can handle the complexity required to properly segment data, which is one reason these improve supply chain efficiency. Sufficient data granularity is critical to more accurate business intelligence.

Have you ever accidentally zoomed way too far into a map online? It’s so frustrating! You can’t make out any useful information because there’s no context. That happens in data, too.

If your data is too granular, you get lost; you can’t focus enough to find a useful pattern within all the extraneous data. It’s tempting to feel like more detail is always better when it comes to data, but too much detail can make your data virtually useless. Many executives faced with so much data find themselves frozen with analysis paralysis. You end up with unreliable recommendations, a lack of business context, and unnecessary confusion.

Too granular data is a particularly costly mistake when it comes to AI forecasting. The data may trick the algorithm into indicating that it has enough data to make assumptions about the future that is not possible with today’s technology. In my supply chain work at Evo, for example, it’s still impossible to forecast daily sales per SKU.