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A data analytics evolution is well underway, and those who have cracked the analytics code are reaping its rewards. That’s the conclusion of Deloitte’s recently published report “The Analytics Advantage: We’re just getting started.”But the report makes no mention of data analytics being applied to safety. It mainly focuses on business functions such as finance, marketing, customer relationship management and sales. Yet, some firms are drawing the connection between data analytics and safety.

One Fortune 200 company with nearly $20 billion in revenue has two goals for implementing predictive analytics in safety. The firm’s vice president of health and safety not only expects to reduce incident rates and costs by predicting and preventing injuries, but also ignite a more active analytics culture across the entire company. Although his C-suite has been talking about analytics, the company hasn’t really taken any tangible actions. He plans for his safety team to lead the charge.

This strategy for success is specifically called out in the Deloitte report: “Smart analytics leaders are gaining advocates by taking on small, focused pilot projects that are yielding tangible results.”

There is still time to start or advance a safety data analytics strategy. According to Deloitte’s findings, many companies are still early in their data analytics evolution and “the organizations that plan for this evolution today will be the analytical competitors of the future.”

What is Data Analytics?

Data analytics is a broad term with lots of meanings. Tom Davenport’s seminal book, “Competing on Analytics,” can help work through the complexity of the term.

Davenport suggests that once data is collected, it can be used to answer various business questions. By using basic data access and reporting techniques, companies can answer “what happened” and “where, when and how often?” These backward-facing questions usually are answered using lagging indicators, but most savvy safety professionals are trying to move their organizations toward leading indicators. To do so requires moving up the analytics pyramid and employing advanced analytics.

Davenport suggests companies can go even further by employing predictive modeling to answer the penultimate question of “what will happen next?” In the world of safety, this translates into predicting workplace injuries, usually using machine-learning predictive models.

A research group at Carnegie Mellon University built an injury prediction model with accuracy rates exceeding 90 percent. Companies that have employed this model in their safety programs have seen significant declines in their incident rates because they are able to predict and prevent injuries from ever happening. These companies have moved to the top of the analytics pyramid. They are seeing similar results to one respondent from Deloitte’s survey who said that this type of “quantitative analysis enables us to anticipate the future rather than having to react after the event. And that can have a tremendous impact on our financial performance.”

It may sound easy, but clearly there are challenges, including:

  • the data itself;
  • the technology tools to collect, analyze and report on the data;
  • the skills needed to perform the analytics; and
  • the support from senior leaders to accept the results of the analytics and to use them to help drive change.
All four items listed above are among the top leading barriers to analytics use, according to Deloitte’s report.

Overcoming the Challenges

Most change initiatives require senior leadership support. The leaders who will engage with an analytics program need to be identified, as do the actions they need to take based on the results of the analytics.

One effective way to do this is with a data use plan, which maps out the following:

  • each specific leader to be engaged;
  • the type of information to engage them with;
  • the frequency of that engagement; and
  • the actions leaders are expected to take with the information.
For example, when working through a data use plan for a safety observation program within a construction company, the following levels of leadership engagement might be identified: foreman, supervisor, operations director and general manager.

For the foreman, it’s important to share very comprehensive and detailed information, but in a way that is specific to his own work crew. It might be best to give him this information at the end of every day so he has an acute understanding of the safety hazards his team is facing.

A different approach should be taken with the general manager, who can’t be inundated with the same level of data as the foreman and be expected to do something effective with it.

The operations manager’s job is much more expansive, so the information and frequency of communication should be commensurate with that expanded scope; i.e, monthly rather than daily. For instance, the operations manager might want to receive a rolled-up synopsis—in the form of a leading indicators report or even the results of an injury prediction model—of the safety hazards across all work crews. The operations manager uses this information to inform his decision-making on resource allocation, risk management and even investments that will lead to a safer and more productive operation.

Data use plans are an effective way of driving leadership engagement. They are easy to build, easy to engage with, and provide a roadmap to action using analytics as the driver. As Deloitte suggests, start small and get some early wins; then expand, start scaling the analytics pyramid and go for a true analytics competitive advantage.

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