Part of being a successful facilities manager is using and applying data in a meaningful way. Data analysis has become so relevant that companies will hire data scientists to help them make sense of the numbers.
Why data analysis matters
Data analytics is important because it helps companies optimize operations and performance. At its very core, data tells managers and employees what has happened, what will happen, and what to do next. Prioritizing data can help facilities managers reduce costs by:
- exposing inefficiencies and problematic processes, and
- identifying opportunities and possibilities
Managers can also use data analytics to make better decisions that will have a positive impact on the companies that use their facilities, and the vendors they work with.
Most experts believe the companies that will thrive will be the ones that realize how crucial data is to their success. That being said, every investment in data must be made strategically; managers are not encouraged to track numbers just for the sake of having them.
Data-driven facilities managers will have the best results if they are using a platform that helps them make sense of all that information. Generally, this will be some form of integrated workplace management system (IWMS).
Different types of data
Data analytics can be segmented into four basic categories.
Descriptive analytics – this data describes what has happened. It is rooted in concrete facts, and can provide background information as well as tell stories about performance metrics. Data aggregation, mining, and summary statistics all serve to provide this category of analytics. It is often used as a starting point for building an analytics strategy.
Diagnostic analytics – this category focuses more on why something happened. It looks for cause and effect to determine an outcome or result. Finding an answer isn’t always easy, but these stats can provide context for probability, likelihood or outcome.
Diagnostic analytics can provide guidance to facilities managers by highlighting outliers, isolating patterns or uncovering relationships.
Predictive analytics – predictive analytics looks at what is likely going to happen in the future. It uses the descriptive and diagnostic analytics and applies those numbers to create actionable insights for decision-making by forecasting potential outcomes. Predictive analytics goes beyond whether something will or won’t happen. Rather, it tries to determine what will happen if specific conditions are met. This category of analytics may require the involvement of a data scientist.
Prescriptive analytics – the last category tries to suggest a course of action. Prescriptive analytics builds on predictive analytics by helping determine the best actions to take based on predicted outcomes. Prescriptive analytics models are dynamic and are constantly learning through feedback mechanisms so that they can provide the optimal solution based on the latest information. By simulating a solution, prescriptive analytics can consider all key performance criteria to ensure the outcome will achieve the correct metric goals before anything is implemented. Artificial intelligence, machine learning and neural network algorithms are employed to support prescriptive analytics.
When collecting data, many companies make the mistake of putting numbers into isolated silos. For example, the operations side of a company can see some data, and has answers to questions around inventory, manufacturing and supply chain. The finance department has its own set of data and has answers to questions relating to its department. But, if the company were to bring all of the data together onto one platform, the entire company would be able to get more answers, and better answers.
There’s also a cost savings aspect to having data on one platform. Instead of paying for multiple independent silos (including storage, software and processing costs), key numbers are stored in one spot, and can be combined and studied in less time.
In order to break silos down, managers are advised to start small and focus on high-priority datasets first. Then, develop a process for prioritizing and gathering data, and let it grow incrementally.
Data for facilities managers
Data is fantastic because it can be analyzed and manipulated in so many different ways. But perhaps the biggest challenge is there is a lot of it. Facilities managers could attempt to track dozens of metrics, but they simply don’t have the time to study everything. Below, we’ve identified some key metrics that they should be tracking.
Requests and response times
Facilities managers should be aware of the types of requests they receive, as well as how long it takes to satisfy a request. If the manager finds that it’s taking a week to change a light, they can figure out what’s causing the delay and create a process to reduce response time.
Managers can use this data to look for trends not only by request type, but requester and department. Tracking and observing average resolution times can help managers identify busy periods and make a proactive plan for handling them before requests come flooding in.
Planned and unscheduled maintenance
By tracking maintenance, facilities managers can see if there are assets that require more care than others, and decide if something needs to be replaced or just repaired at an earlier date. By getting ahead of a printer failure or A/C shutdown, you also boost employee satisfaction and productivity.
Cost per repair
Tracking money spend on repairs can help managers make a budget if they don’t have one, or help them understand if they’ve spent more or less than they planned to if a budget already exists. Facilities managers can also use this information to compare vendor prices if they are looking to bring in a new repair professional.
Energy is a big cost for most companies. Managers can track energy usage in specific areas of their buildings to pinpoint opportunities to reduce consumption. Having this information can also help facilities managers work with environmental requirements or work on their own green initiatives.
Based on the results, facilities managers may decide to invest in energy-efficient appliances or implement energy-saving policies, like shutting down computers and printers at the end of each workday.
Use desk booking data or information from tenants to get a clear idea of existing space utilization, and how to make adjustments. This same data can help managers identify when it will be time for a tenant to move to a larger workplace. Having accurate, up-to-date data about desk and space occupancy is critical, especially since the pandemic. Overcrowding a space can have major consequences.
Facilities managers work hard to serve their company and their clients, but no one is perfect. Most managers appreciate feedback from tenants, but how many actually quantify the information they receive from the people they’re working to help? Tracking complaints can help facilities managers realize where office procedures can be modified or streamlined for optimal productivity. Similarly, if everyone complains about the state of the restrooms, this lets them know that this issue is a top priority that needs to be addressed as soon as possible.
Data can give facilities managers the competitive edge they are looking for. While analyzing numbers might sound like a time-consuming or complex process, it can be very manageable if you start small. Select a system that allows makes the process easier. For example, use a platform that automatically saves data for you. Most managers don’t have the time to input numbers manually, but they can create, study and share reports if the information is already there.