198 West 21th Street, Suite 721
New York, NY 10010
[email protected]
+88 (0) 101 0000 000
Follow Us

+31 (0) 85 0604270   | +32 (0) 26 229614   |    [email protected]

  • Nederlands
  • Français
  • Deutsch

4 phases within Data-Driven Facility Management

4 phases within Data-Driven Facility Management

All organizations generate data. Much of that data comes from the facility 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 fully utilize these options as a facility 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 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 facility company can find itself. On one side of the spectrum I place the organizations that have no digital data at all. Companies that base decisions entirely on data and have established a strict data structure are at the other end of the spectrum. Below I describe these four phases.


In this phase no useful digital data is actually collected at all. There may be hardcopy data, so data on paper, but this form of data is much less useful and effective when used.

No data structure has therefore been set up in this phase. For the facility manager it is first important to get an answer to the following questions: what kind of data do I want to collect, what kind of data can I collect and how will I do that?

Take the following example: a worker performs a monthly cleaning check on the shop floor. The cleaning employee does this with a form that he or she completes step by step. So a hardcopy result is available. The auditor then scans this paper and sends it to the client.

By scanning, a hard copy check has been made into a digital check in this situation. However, nothing can be done with the data, because it is unstructured. In this case, unstructured data means that a good comparison cannot be made with earlier measurements. For example, a client can only find out manually where the problems of the cleaning are (such as traffic areas) and how this was translated during previous measurements.

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


In the second phase we see companies that are already generating data, but are sometimes not aware of this at all. In any case, all the structure is lacking and opportunities are therefore missed. The data can be generated by the company itself, but other parties can also do this.

Think of a school that has the cleaning done by a supplier. The facility service has an external check carried out four times a year to see whether the cleaning contract is being enforced. The supplier may provide DKS reports and an external auditor performs a check with a different, certified, measurement method.

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

In this example, most reports are sent by e-mail and then viewed during a snapshot. A real comparison over a certain period is much harder to make. The reports will probably go to the archive. A missed opportunity.

If the school wants to take the next step, the facility service must first look at how the data is available. In the future, the data will have to be collected in such a way that it can be compared. In this way the school can see what the results were in the past period per room category and per element. That gives much more insight than just comparing report numbers.


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

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 mutations), invoices are checked and quotations are assessed.

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

The facility manager thus receives answers to questions such as: how do financial developments view the delivered (and agreed) quality? Should new actions be taken based on the answer, and in which areas?

To continue 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 are then in the fourth phase.


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

This data will be benchmarked within this phase. 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 area spends much more on floor maintenance than other schools, while checks on floor maintenance show few deviations. By delving deeper into the data it can become clear, for example, that the school will have a floor sprayed more often instead of directly preserving it. Another possibility is that the other schools have different types of floor types, 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, they do not all benefit from this. By collecting and benchmarking this data in a structured way, new insights are created. They 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, in which all parties are satisfied.