Pandemics and Predictive Analytics in Construction

As a consultant, one of your biggest assets is the knowledge bank of tools, code, white papers, lessons-learned and contacts that are amassed over the span of your career. As a society, pandemics bring with them insight into collective knowledge (or exposure to the lack thereof), living standards, microeconomics and more.

In the process of sifting through my archives for a reporting sample last week, I ran across a draft introduction to predictive analytics for the construction industry written in 2015. To the credit of the CFMA, a large portion of the construction story below was provided by a KPI (Key Performance Indicator) piece originally published in 2013.

A flood of memories came back. I recalled how popular predictive analytics was (and still is). The phenomenon of “big data” was trending through industries such as retail, banking, hospitality, medicine and insurance. I recalled the premonition that the construction industry (in many ways due to the nature of the business) would be destined to be one of, if not the, last to embed the concept into daily practice. Lastly, I recalled why I did not complete the piece and publish it amongst my peers… even though Predictive Analytics had made it to the “for Dummies” series and tech giants like Apple, Amazon and Google were owning the space and taking it mainstream, it still felt lofty. I did not want to join the growing crowd of intellectuals stating that the investment was a necessity without a roadmap to tangible results.

Over half a decade (and facing a global pandemic) later we find the model maturing in some sectors and slowly gaining traction in construction. To illustrate the problem, consider the following:

Imagine you are riding down the road in the back of a pickup truck and could not face forward. You would use several indicators to determine how the trip is going:

1)  It is very windy, which indicates you are moving fast. This is not necessarily a bad thing.

2)  You see the road trailing off behind you, so you appear to be on track.

3)  There is constant loud noise (not unlike your office during the rush of the day).

4)  There are a number of cars falling behind, so you must be doing well compared to the competition.

You arrive at your destination, measure your gas mileage, and determine that everything seems to have gone well. However, because you were looking back, you were unable to spot a number of KPIs:

1)  The temperature gauge was steadily rising, endangering the engine.

2)  There were several forks in the road; better navigation may have allowed you to arrive at your destination faster.

3)  The driver failed to shift despite the roaring engine and red-lined tachometer, resulting in wear and tear on the engine.

4)  You didn’t notice the cars that started ahead of you and increased their lead. You thought you were ahead, but you were actually behind and losing ground.

Likewise, while the typical construction KPIs of historical profit, revenue, and costs are an essential part of measuring business performance, they don’t represent a complete picture. Predictive KPIs are the equivalent of a temperature gauge or tachometer – they are the predictive measures that provide insight into future trends, challenges, and opportunities that allow you to make proactive decisions.

Every company must define the indicators that fit its business culture and environment. There is not a one-size fits all solution for construction practices.

Like the pandemics of the past, there are positive paradigm shifts that can be drawn out of the present scenario we are facing. The Bubonic Plague was also a turning point in the intensification of European prejudice and bigotry against minorities, particularly Jews and Roma. Smallpox, however, paved the way for the colonization of the Americas and changed how money was valued, both crucial points in the development of modern capitalism.

Currently, there is a divide (classist or not; stated or not) amongst those that want to re-open the economy at an accelerated pace and those who want to play it safe. Generally, those that are pushing to re-open have hard, tangible reasons for taking the stance (and the risk). They are unemployed or own/work for small businesses on the brink of closing or otherwise require the benefits of a stable economy to live. Those that are more cautious were economically pre-dispositioned to weather the storm. They are essential business owners or otherwise financially stable to take on a slower, more cautious approach to recovery.

One way to bridge the Coronavirus divide within the construction community is to regain the knowledge capital of the uninsulated population through the development and refinement of predictive analytics strategies. We live in a connected world. Construction Industry and broader market sectors are more connected than originally perceived. The chances are high that there are impacted individuals that could provide the big data metrics relevant to building out the analytics vision for your company.

Shameless plug: If you have made it this far, I hope that you have enjoyed the read. If you want to know more, or need help implementing your vision for predictive analytics feel free to reach out to me!

Open questions: What have you done to leverage big data and/or predictive analytics within your enterprise? How are the results distributed? Who has been involved in your analytics planning?