Using numerous examples from a typical office facility, Abhijeet Roy, Associate Director – Administration & Corporate Real Estate, Fractal demonstrates how data makes all the difference in facility management.
Data offers a real, unbiased, objective and truthful perspective, providing tangible information that can be acted upon. It offers endless benefits, and I believe any sort of refined data is beneficial.
To simplify whether I should initiate data collection in a given situation, I follow the following decision matrix:
- Effort vs Benefit: What positive change can this data bring? Does that help me in improving human behaviour or comfort? Can this data help me in optimising spends or energy?
- Is this data relevant at this point? Does the cost of gathering this data justify the changes at this time?
- Is this data redundant? Can we gather similar data from an existing resource or data feed?
Data analysis, application and dash boarding can be done in more than one way. For example, when it comes to attendance, a facility manager can use very simple data to estimate the quantity of food to be ordered, set temperatures of the HVAC system, regulate the number of housekeeping cycles…the list is endless.
The more the data feed, the more accurate your predictions will be. Using multiple data feeds for better decision making is recommended. For example, you can use the same attendance data and club it with hotdesking data to understand if you really need to have as many square feet made available or as many HVACs operational, thus providing both financial and environmental savings.
Data collection and analysis has had a huge impact in helping my organisation achieve a good bottom line. Here are some examples:
Costs: Patterns of attendance observed over an extended time were useful in saving 25-30% on real estate spends. This has also helped us save on cascading costs such as the number of housekeeping resources required.
Better human behaviour and comfort: What food is being consumed the most, what HVAC ambient temperature is least complained about or most liked, which format of meeting rooms is most booked and so on. Human comfort and behaviour are often misunderstood or difficult to quantify in a short timeframe, but over a long period of time, one can analyse data such as average longevity of an employee, net performer score etc as indicators of FM.
In a recent project, we were able to count the number of people in our cafeteria and this feedback was published in real time to all employees in the facility. This empowered each employee to decide whether they should go to the cafeteria and wait in queues or go a bit later when the occupancy was lower. This reduced their frustration of waiting in queues, and in the long run, will reduce the need for additional real estate for a cafeteria.
Responsibility towards the globe: The purpose of data is not just to reduce costs but also make best use of resources. As an FM, I am responsible for the ethical use and disposal of the material I use for the organization. Through a combination of many data feeds, we have been able to set many internal targets that allow us to reduce costs and also act responsibly.
One such example is tracking how much energy we are using per square feet, per head and also as a function of our revenue. Such data has helped us use products and services that contribute minimally to the globally increasing greenhouse gas emissions.
Myths about data
The FM community is becoming increasingly aware of the importance of data analytics, but a small set of people still needs to understand this. Some common misconceptions are:
- Gathering data is expensive: If one can use existing resources to gather data, there is a high probability of achieving savings. E.g. Use time of day attendance i.e where your footfall is the highest, to reduce housekeeping cycles.
- Data analytics is complex: There are many free courses that will teach you easy ways to learn basic models.
- Data needs to be checked only once or non periodically: This is a huge blunder. You will need to categorise the refresh window of each dataset and then check in periodically. E.g. Food waste needs to be checked weekly, but air quality should be checked daily. You will need to categorise very clearly which data is needed in what cycles.
- Data is only via Excel: It can also be experience based, but you will need to be conscious to eliminate any biases while you present it.
Future of data analysis
- Occupancy data: Both macro occupancy i.e how many people are there in the office, which can help you reduce your overall spends on housekeeping/energy, can be used in emergencies, and micro occupancy i.e which areas in an office are most used, can help you repurpose your existing office areas and help reduce real estate spends in the long run.
- Air quality monitoring: The long term benefits are reduction in HVAC costs, no molds in the office (thus reducing spends on repairs), better employee longevity etc.
- Waste tracking: You can check this both manually and digitally by use of automation. Tracking of waste will help you set your targets and adhere to them. While this may now be looked upon as a voluntary action, soon almost every FM will have to adhere to this as governments will make it a mandate.