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How to Make Data-Driven Decisions for Your Facilities Management Program

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January 12, 2024

People make decisions every day that impact their lives, choices, wallets, etc.  Whether personal or professional, virtually every action taken is based on a choice of various, albeit sometimes conflicting alternatives.  Over time, as we make decisions, particularly within the same domain, we’re ideally getting smarter.  For much of our lives, you’ve made such decisions on gut feel like:

  • Should I take a taxi, Uber or public transportation to get to my destination?
  • Is this the cheapest I can pay for this product or service?
  • Does it make sense to buy this product now?
  • Is this a ‘good’ proposal from a painter that wants to paint my house?

While it’s not always readily apparent, there are lots of variables that go into these types of decisions.  Timing, pricing, quality, experience and speed are just a few.

How do you go about answering these types of questions? Most likely you’ve answered these questions enough times that you can infer from your past experiences the best choice to make in that occurrence. Facilities managers would often have to trust their gut, but now thanks to the power of machine learning, the decision-making process had become a whole lot simpler. 

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Facilities Management Decision Making

How does this apply in the facilities management world? Well, facilities managers make important decisions on a daily basis that have cost and revenue implications in their organizations such as:

  • Approving or rejecting a service provider’s proposal
  • Approving or rejecting an invoice
  • Choosing a service provider

Today, most facilities managers make decisions based on remembering past decisions over a period of time. But as we all know, human memory degrades over time and is not the most reliable. This can then lead to bad decisions, increasing cost, delaying results and lowering quality.

How can we minimize making bad decisions? What can we rely on? The answer is…data.

Smarter Decision Making with Machine Learning

In recent years, technology advances have driven processor and memory cost down so that data can be stored and processed much faster and cheaper than ever before. This has enabled areas like artificial intelligence and machine learning to start being incorporated and utilized in the enterprise to give both business users (B2B) and consumers (B2C) greater confidence in making decisions. Kayak price predictions

Take for instance, It uses historical customer, price and flight data to provide a recommendation on a flight you are trying to book as shown here:

But how do these type of recommendations get made? To see how Kayak (in this particular case) and others offer these type of ‘smart’ decision-focused applications, we need to explore the world of machine learning…

Let’s Learn a Little About Machine Learning

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed what to do. It usually starts with a good amount of available data; then you define an algorithm to apply it against that dataset.

It is assumed in that algorithm you are highlighting elements in that dataset that are important to you for it to consider (or weigh) more than other elements. You generate a model or a function from that which you apply new data to get some predictive result from it. In a nutshell, you are teaching a computer “to do stuff” to give you a recommendation based on a set of data and constraints.

What are some other real world applications of machine learning?

  • Google’s self-driving car
  • Amazon, Spotify, Netflix recommendations
  • Spam filtering
  • Facial and image recognition
  • Speech recognition
  • Fraud detection
  • Automatic language translation
  • Assistive and medical technology

How is the business world reacting to this? Recently, reported that a Russian investment firm launched a $100 million fund to invest in businesses that are machine learning and AI-based.

Tractica, a leading research group in market intelligence, forecasts annual worldwide AI revenue will grow from $643 million in 2016 to $36 billion by 2025 as shown below: AI projected revenue

What Does Machine Learning Mean for Facilities Management?

With technologies like machine learning incorporated into facilities management software, we expect a transformation in the space where we will see better decisions being made and efficiencies in operations that is driven by data.  Facilities managers will improve decision speed, quality and consistency and importantly, reduce costs.

Like other applications of this technology, decisions will no longer be based on gut-feel but be data-driven.  This will be another way that big data and analytics is disrupting the FM industry.  In future posts, we’ll highlight the ways that ServiceChannel is driving real quantitative benefits for facilities professionals with machine learning technology.

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