It would be hard to argue that our industry is falling down when it comes to gathering information. From incident reports and progress images to worker hours and safety observations, we’ve got mountains of data.
The trouble is using it.
Nitesh Alagh, the health and safety lead for UK-based Galliford Try, put it well: “Initially the problem was that there wasn’t enough safety data. Now there’s too much data, with cabinets and hard disks just saturated with it, but we were not revisiting or using it in the way we’d like. There was no organizational learning, so you wonder, ‘Why do we bother collecting it in the first place?’”
He’s got a good point. Why spend so much time gathering information if it’s just going to sit on a hard drive doing nothing for the organization? Everyone wants to become a data-driven organization today, but it’s not always obvious how to navigate the journey from collecting data to using it to make key decisions.
So, let’s look at what it means to be a data-driven organization and what the benefits are for an organization that does it right.
In most organizations, while the safety team and other management will look back on incident reports and other historical data to identify ways they could improve, for the most part decisions are made based on best practices, lagging indicators, personal experience, and gut feelings.
A data-driven organization doesn’t make decisions based on gut feelings, but lets the data guide their actions. Raw data alone, however, won’t do the trick. The key is to have an analytics layer that can combine disparate data sets to provide you with actionable trends and insights.
Too often, this is difficult to do because, while the organization may be collecting the right data, different data sets often reside in disconnected silos. Either the data must be aggregated, or the silos must be connected before analytics can access it. Data such as images may contain additional valuable information that AI could unlock.
Collecting and analyzing data in a rigorous way can provide an organization with the ability to track KPIs such as identifying the highest risk projects so safety teams can act. And if these data points are shared widely throughout the company, it won’t just be the safety team that’s making decisions according to the data, but everyone.
Becoming a truly data-driven company, however, is not an overnight affair. It’s a journey, and that journey begins with collecting good data.
There’s no doubt that construction is collecting a lot of data, but that data needs to be collected and stored so that it can easily be accessed and analyzed. It’s well worth the time and effort to ensure that site photos, manpower, project data, and even weather information is easily accessible—preferably in the cloud.
By automating much of the process, construction organizations can take a lot of the guesswork out of data collection and ensure it is stored in the right location and format. For example, if employees on the jobsite use a smartphone to take photos for a safety observation, intuitive forms can make it simple to tag it with descriptors and rate it for severity and frequency. The system also can make sure the data is routed to a central location where AI can access it.
Data collection also can occur using the data a construction company already has. For instance, our construction-trained AI, Vinnie, can analyze the thousands of progress photos an organization already has to identify more than 100 different types of safety hazards and essentially become a virtual safety inspector to make observations.
Finally, data collection needs to be accurate. Bad data will produce bad insights that lead to bad results. In safety, for example, if people feel they’re going to get their colleagues in trouble by providing information, they’ll be unlikely to do so, which will result in inaccurate data.
Data analysis is critical, but if your people don’t trust or act on the insights that analysis produces, you may as well not collect or analyze the data at all. The first step is to get your people on board, like Top ENR contractor JE Dunn was able to do.
One critical step is to celebrate data. This means that you cannot use it to punish or berate people when the data shows there are safety issues. If people know they may get nailed or get other people in trouble for reporting and analyzing data, not only will data quality suffer, but few will trust or act on it.
Organizations also need to create a process for acting on the intelligence that data produces. Create a “data-use plan” to ensure that insights are reviewed, communicated, and acted upon. This plan should include who will receive the relevant metrics, how often they receive it, and how it will be shared. And don’t sit on this plan—incorporate it into leadership review meetings to evaluate how well it’s working so you can continuously improve upon it.
Now that your data is accurate and easily accessible, your people are prepared to celebrate instead of fear data, and you’ve got a plan for acting on data insights, you’re ready to begin analysis to unearth the insights that will enable you to become a data-driven organization. AI, of course, is the first technology that will come to mind for many, but it’s important to recognize that different company’s AI are experts in different fields, just like human beings.
And unless you’re equipped to train the AI yourself—which we do not recommend—you’ll want to choose one that’s already trained on construction-specific data. But even if you’re not using AI, analytics can still provide a great deal of value.
For example, in the most advanced form of data-driven safety, AI analyzes site photos, safety observations, incident reports, FTEs on site, weather data, and a host of other information to create predictive models that can identify which 20% of projects will have 80% of all incidents.
Acting as an early warning system, AI can provide prescriptive actions to take to prevent incidents before they happen. The more access supervisors and other managers in the field have to data and insights, the better they can act on them to improve safety.
And this isn’t just us saying this. There are plenty of examples of construction companies that have seen dramatic results from taking a data-driven approach to safety. Suffolk, for example, cut its recordable incidents by 28% and lost time by 35% in just 12 months after implementing a data-driven safety program they called RiskX.
Then, there are insurance benefits. Increasingly, large insurers are coming to recognize that truly data-driven safety programs have the power to substantially reduce risk, which puts construction companies who are successfully doing so in an excellent position to negotiate better rates.
Warfel, for example, was able not only to secure superior insurance as a result of its strong results with data-driven safety, but it was able to get a rate that typically requires seven to 10 years of experience with the insurer to obtain.
But these are far from the only benefits. A better safety record strengthens a company’s brand and positions it to win more business. Construction companies can make more efficient use of their limited safety resources, deploying them where they will have the largest impact on reducing accidents.
In fact, our analysis of the impact of taking a predictive, data-driven approach to safety shows that for every dollar spent on the underlying technology, a construction company can expect $5 to $20 in savings, which is a pretty staggering ROI.
It pays to be a data-driven business.
Learn how developing a data-driven safety program with Oracle Construction and Engineering has the power to substantially reduce your organization’s risk.
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