The risks of incorrect data

Being data-driven has become the default ambition for most organisations. But there is a conversation that does not happen often enough: what happens when the data is wrong?

It is more than 50 years since humans first walked on the moon. Before re-entry into Earth’s atmosphere, NASA’s engineers calculated the precise trajectory for the spacecraft’s return. A few degrees off could have had enormous consequences – the craft could have missed Earth entirely.

The same logic applies to data. A decimal point in the wrong place might seem trivial, but a small error introduced early in a process follows that process all the way through to the decision it informs. Three of the most common data problems are:

1. Data is missing

2. Data is out of date

3. Data is simply incorrect

Multiple data sources make quality control harder

There are many ways incorrect data ends up in reports and systems. In property management, data typically comes from multiple sources – a building portfolio contains many different components, and each tends to be measured differently. Temperature readings come in from one source, manual meter readings from another, and the supplier of the EV chargers in the car park manages their data in a third way entirely. Each stream is often analysed separately, in its own silo, and the results are then used as the basis for decisions – but they are difficult to compare or combine into a coherent picture.

The difficulty of consolidating data from different sources also means it is less likely to be scrutinised carefully. Small errors are harder to spot when no one has the full picture. Siloed processes allow incorrect data to go unnoticed.

Why the property industry has the same problem as NASA

Whether incorrect data is used to forecast future energy costs or to optimise heating across a building portfolio, the result is the same: the data cannot be trusted as a basis for decisions. If an error is introduced early – the trajectory calculation is a few degrees off – it will carry through every subsequent step, from the decision to the outcome.

To make matters worse, many property companies still rely on spreadsheets to manage critical data. Multiple spreadsheets, spread across different people and projects, make it close to impossible to ensure that reported figures are correct. The problem is not unique to any one market.

Quality-check your data

Data collection in property management is not straightforward. At Metry, we address the problem by collecting values from all types of data sources and structuring and validating that data in one place – no silos. A property company can then connect its own energy management system or analytics tools directly to that structured, validated dataset.

The advantage is that the data is already quality-checked before it reaches any downstream system, which removes the need for manual reconciliation and reduces the risk of errors carrying through to decisions.

In short: we reduce the risk of the spacecraft missing Earth on its way back through the atmosphere.