4 phases within Data-Driven Facility Management

All organizations generate data. Much of that data comes from the facilities chain. However, the extent to which companies benefit from this data varies widely. What opportunities are there in using this data? And how do you make full use of these opportunities as a facilities company?

All companies generate data, but not all companies actually do anything with the available data. With this data, a lot can change in contract management as we know it today. Instead of relying on a vague estimate or a gut feeling, you can make decisions based on hard data; that is where the opportunities lie for clients, suppliers and intermediaries.

When we talk about the use of data, I see four phases in which a facilities company can find itself. At one end of the spectrum I place the organizations that have no access to digital data at all. Companies that base decisions entirely on data and have established a tight data structure are completely on the other side of the spectrum. Below I describe these four phases.


In this phase, no usable digital data is collected at all. There may be hard copy data, ie data on paper, but this form of data is much less useful and effective when used.

In this phase, therefore, no data structure has been set up. For the facility manager it is first important to get answers to the following questions: what kind of data do I want to collect, what kind of data can I collect and how am I going to do that?

Take the following example: a foreman carries out a monthly cleaning check on the work floor. The cleaning employee does this with a form that he or she fills in step by step. So a hard copy result is available. The inspector then scans this paper and sends it to the client.

In this situation, scanning has turned a hardcopy check into a digital check. However, nothing can be done with the data because it is unstructured. In this case, unstructured data means that no proper comparison can be made with previous measurements. For example, a client can only find out manually where the problems of cleaning are located (for example traffic areas) and how this translated during previous measurements.

Do you want to take the next step? Then digitize the entire measurement and work with structured data as much as possible (no open questions, but closed answers). You then need to be able to export this digital, structured data in order to extract all kinds of insights. This detailed data can be useful in the future, because you can compare the different measurements.


In the second phase, we see companies that are already generating data, but are sometimes completely unaware of this. In any case, all structure is missing and opportunities are therefore missed. The data can be generated by the company itself, but other parties can also do this.

For example, a school that has the cleaning carried out by a supplier. The facilities department has an external audit carried out four times a year to see whether the cleaning contract is being maintained. The supplier may provide DKS reports and an external auditor will perform an inspection using a different, certified, measurement method.

There are therefore different types of reports, but in fact this concerns technical cleaning quality. This involves looking at different room categories (eg sanitary facilities, traffic areas, etc.) and elements within a category.

In this example, most reports are sent by email and then viewed during a snapshot. A real comparison over a period of time is much more difficult to make. The reports will probably go into the archive. A missed opportunity.

If the school wants to take the next step, the facility services must first see how the data is available. In the future, the data will have to be collected in such a way that they can be compared with each other. In this way, the school can see what the results were in the past period per room category and per element. This provides much more insight than just comparing report figures.


In the third phase are the companies that have been consciously working with data for a longer period of time. This data is generated (internally or externally), collected and made transparent, whereby the trend lines of the own organization can be followed. The KPIs that are agreed between supplier and client are easily monitored.

An example: a company with branches all over the country where there are different clusters with different suppliers. This company uses online contract management software where calculations and work programs are stored and can be managed (by means of changes), invoices are checked and quotations are assessed.

A lot of data comes out of this online contract management software. When this data ends up in a dashboard, it becomes easy to track and assess the data.

In this way, the facilities manager receives answers to questions such as: how do the financial developments compare to the delivered (and agreed) quality? Should new actions be taken based on the answer, and in what areas?

To go further in data-driven facility management you will have to start comparing, in other words benchmarking. How do you perform in relation to other (comparable) organizations in the market? We have now arrived at the fourth phase.


In this phase, the organization already makes data-driven decisions internally. The structure of the data is good, the collection is (semi) automatic and good overviews are displayed, so that KPIs and trendlines can be followed (and shared) within the organization in a pleasant way.

Within this phase, this data will be benchmarked. Benchmarking compares the performance of anonymous (comparable) organizations. In this way, companies can learn from each other and make even more data-driven decisions.

For example, it may turn out that a school with a certain surface area spends much more on floor maintenance than other schools, while inspections of floor maintenance show few deviations. By delving deeper into the data, for example, it can become clear that the school much more often has a floor sprayed instead of immediately preserving it. Another possibility is that the other schools have different types of floor, which ultimately saves costs. In short, a benchmark provides valuable information that can ultimately save a lot of costs.


Although almost all organizations generate more and more data, not all of them benefit from this. By collecting and benchmarking this data in a structured way, new insights are created. These can ensure that clients, suppliers and intermediaries within facility management work together even better. In practice, this will lead to an excellent quality/price ratio that satisfies all parties.

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