Facility and maintenance data is critical to your overall success. With the right data, you can see the big picture while at the same time find all the small spots where you could be running more efficiently, wasting less time, and spending less money.
Data is also how you set and track goals. It’s hard to overstate the value of good data. But it’s also easy to fall into the trap of wanting too much of a good thing — especially when you’re working with large data sets from across multiple sources. So, how can you make sure you’re getting all the data you need but none of what you don’t?
Facility and maintenance data: Working definitions
It might feel a bit circular, but you can define facility and maintenance data as the data a facility manager and maintenance team needs to keep the assets and equipment up and running.
It’s also the data produced in the process of keeping the facility going, including records of preventive maintenance schedules and on-demand works orders. Plus, there’s the results of that work, related KPIs, and reports.
Another way to categorize data is as more or less static. Schematics for the HVAC system are static while the inventory levels for AC replacement parts are more dynamic since they fluctuate with demand.
Facility and maintenance data can be as low-tech as a paper log of readings from a pressure gauge to as high-tech as large data sets from a digital twin being fed a constant stream of sensor readings.
Big data sets: Cutting the noise to boost the signal
When trying to find the best ways to work with big sets of data, it’s important to remember you already work with tons of data all the time. Every day, when you use the Internet, you’re working with massive amounts of data. What are your current best practices?
You use a search engine to find what you need, the “signal.” There’s also a lot of data there you don’t want or need, call that “noise,” so to get past it to what you want, you need a good search engine. If you’re good thinking of the right keywords, the system works well enough.
But with your facility and maintenance data, you don’t have the luxury of being able to just ignore useless data. Remember, it’s costing you resources to capture and maintain your data, so you only want what you can use. Here, it’s better to boost the signal by simply cutting out all the noise. Notice the difference between cutting through and cutting out.
If you’re careful about what you add, you don’t have to worry about making your way through a lot of useless data later.
So, how do you do that? How do you ensure you’re capturing and keeping useful data only?
Data insights: Work backwards from metrics and key performance indicators
When deciding which data you should capture and maintain, think about why you need it in the first place. What are you going to do with the data? It’s the same as when you make a shopping list of ingredients. How do you know what to put on the list? You start with the dishes you want to prepare and work backwards. No one ever thinks, “I need flour.” Instead, you think, “I need flour to bake a cake.”
Depending on your specific facility, industry, and maintenance requirements, you can focus on different maintenance metrics and key performance indicators (KPIs). SpaceIQ by Eptura’s blog post on maintenance data suggests tracking:
- Work order response times
- Planned vs. reactive maintenance
- Cost per repair
- Energy use and audits
- Space occupancy levels
A good general, all-purpose one is planned maintenance percentage (PMP), which helps you compare how much of your maintenance is scheduled versus on-demand. To get the PMP, you would need reliable data on how much time the team spent on work orders from the preventive maintenance schedule and how much time they spent on all work orders combined.
Another good KPI is mean time between failures (MTBF) which helps you better understand a repairable asset’s reliability. You can also use it to compare systems and designs. Here, you need data on uptime and the number of times the maintenance team had to perform unscheduled maintenance.
The number of possible examples is nearly limitless, but the logic remains the same: Once you know what you want to track, you can work backwards to determine which data you need.
Data “destiny”: Work backwards from key roles and responsibilities
Another way you can focus on the right data is to start by thinking carefully about the different people on the team who need to use it. Chuck Mies, LEED A.P., Assoc. AIA of Autodesk, and a pioneer in leveraging building information modeling (BIM) data for more effective, efficient facility management, says you need to start by looking at the end user.
In an Eptura podcast, Leverage BIM to Unlock Facility and Asset Data, Mies presents this critical rhetorical question: “If we don’t understand who the final consumers are, what problem are we trying to solve?”
Once you have a list of who’s going to use the data, you can then think about how they’re going to use it. Mies encourages organizations to create user personas to help at this step. A persona is a sort of digital twin, but not for an asset or facility.
It’s a virtual archetype of a data user who fills a role within the organization. So, it’s not tied to any one specific person on the team. Instead, it’s tied to a specific role, and so it shares many of the important characteristics of the people who have that role, such as
- Educational background
When creating a persona, ask:
- Who are they and what do they do?
- What are their main, overall goals?
- What is preventing them from reaching these goals?
Once you have a sense of what different roles require to succeed, you can see what data they need.
Data criticality: Go after the most important data first
It’s important to remember that capturing and managing data can take time. In fact, in a recent Eptura podcast, Andy Burg from Messer Construction explains how “ultimately, this isn’t something that’s going to be done really fast. This is a long journey, to get to where you want to go… A lot of our facility owners have multiple systems, you’re dealing with multiple departments, and they all like to do things their way. And to unify all this, to make it work, takes time.”
So, it can be helpful to look at data criticality. Once you know what data you need, you can start to prioritize it. Eventually, you’ll have it all, but in the meantime, what do you need right away?
If you already have a good working list of the KPIs you want to track and the personas who are going to track them, you likely already know which data to go after first. Keep in mind, though, that it’s not a perfect process that runs straight from A through Z either. In some cases, you can go after the low-hanging fruit.
Modern facility and maintenance management involves a lot of data, so you need to establish reliable in-house best practices for working with large data sets. One of the most effective ways to boost the signal is to reduce the noise.
Basically, make sure you’re only adding in the data you need. You can start by working backwards. So, if you know the metrics and KPIs you want to track, you can easily see what types of data you need to have. It’s the same as when you go shopping; once you know the dish you want to cook, it’s easy to determine the ingredients.
You can also look at who is going to use the data and how. Once you create personas for different roles in the organization, you can determine what data they need to succeed.