Safety professionals everywhere often wonder what education or training they need to further their career. The answer: data-driven decision-making (DDDM). It is the systematic collection and analysis of various types of data — including input, process, outcome and satisfaction data — to support the decision-making process. It is making decisions based primarily on data and analysis rather than the often used experience and intuition. DDDM is, for all intents and purposes, a quality management philosophy that can yield great results when applied to the safety field.
Safety is both an “art” and a “science.” For years, the major focus has been on the “art” of safety. In fact, many safety professionals will openly admit to shying away from anything to do with numerical analysis or any other related jobs or tasks where a calculator is involved.
The current standard for safety certification in Canada is the Canadian Registered Safety Professional (CRSP) designation awarded by the Board of Canadian Registered Safety Professionals (BCRSP). Although the requirements for certification have changed over the years, the major focus of the exam has been on the art of safety. In its current format a candidate can pass an exam without ever having to use a calculator and without having to demonstrate any numerical competency. This is all about to change.
Effective in 2015, under the Applied Safety Fundamentals domain, competency 3 (ASF 3), a candidate will be required to demonstrate an understanding of statistical analysis. Although this requirement is pretty basic and is just the beginning of DDDM, it’s a start.
A couple of common DDDM tools include Six Sigma and Pareto analysis. Six Sigma was originally a set of practices aimed at improving manufacturing processes and reducing the number of defects. It has since evolved to be applicable to all business processes. The particulars of the Six Sigma methodology were devised in 1986 by Bill Smith at Motorola and include “a clear commitment to making decisions on the basis of verifiable data and statistical methods, rather than assumptions and guesswork.”
Pareto analysis is a technique used to prioritize problems of any type, for example, quality, absenteeism, incidents, production variations, duration of a process or resource allocation.
What’s really required — and what the most successful companies do very well — is to ensure data is turned into information that is used to generate knowledge which results in intelligence.
OK, so what is data? Data are values of qualitative or quantitative variables, typically the result of measurements or observation. Here is an example: A health and safety officer in a manufacturing operation may collect data on incidents including date, time, location, severity, lost time, cause, age, gender and work experience of an operator or injured party.
Some common data sets used to understand safety throughout an organization include near misses, hazard tracking, industrial hygiene monitoring, job hazard analysis, safety behaviour, safety culture, training data, incident tracking, inspections and action item follow-up.
In simple terms, data is transformed into information by organizing and processing it. The use of charts and graphs is helpful in this regard. Other ways of processing data include the calculation of the average of a data set or calculation of various other measures of variability in the data.
When collecting data for any purpose, be sure the following questions are addressed:
• How should record-keeping be managed?
• What method of backing up data should be used and how often?
• Who is responsible for the integrity of the data?
• How is the integrity of the data established and verified?
Once collected, where do you start? Many people will dive right into a bunch of data and try to build reports and data analysis. A better approach is to start with a problem or an improvement area then look for the right data that will help you understand the problem or monitor the results. To determine what improvements matter most, perform a departmental purpose analysis to determine where you can best apply your DDDM talents. Or, better yet, review your organization’s latest strategic plan and look for stated strategic goals and key performance indicators (KPIs). Determine which KPIs are within your sphere of influence and focus on those.
When you’re ready to analyze the data, ask some simple questions, such as “Is the data trending up or down?” Then move into tougher questions, including “Is the variable that’s trending up or down made up of more than one component?” or “Is one trending up while another is static or trending down?” Finally ask, the really important questions: “Why?” and “What can be done about it?”
Going forward, the next generation of safety professionals will possess some capacity for DDDM, and future safety management systems have to include DDDM. If this sounds like a novel idea to you, then the next training you should be looking for is foundational education in DDDM.
Safety is both an “art” and a “science.” For years, the major focus has been on the “art” of safety. In fact, many safety professionals will openly admit to shying away from anything to do with numerical analysis or any other related jobs or tasks where a calculator is involved.
The current standard for safety certification in Canada is the Canadian Registered Safety Professional (CRSP) designation awarded by the Board of Canadian Registered Safety Professionals (BCRSP). Although the requirements for certification have changed over the years, the major focus of the exam has been on the art of safety. In its current format a candidate can pass an exam without ever having to use a calculator and without having to demonstrate any numerical competency. This is all about to change.
Effective in 2015, under the Applied Safety Fundamentals domain, competency 3 (ASF 3), a candidate will be required to demonstrate an understanding of statistical analysis. Although this requirement is pretty basic and is just the beginning of DDDM, it’s a start.
A couple of common DDDM tools include Six Sigma and Pareto analysis. Six Sigma was originally a set of practices aimed at improving manufacturing processes and reducing the number of defects. It has since evolved to be applicable to all business processes. The particulars of the Six Sigma methodology were devised in 1986 by Bill Smith at Motorola and include “a clear commitment to making decisions on the basis of verifiable data and statistical methods, rather than assumptions and guesswork.”
Pareto analysis is a technique used to prioritize problems of any type, for example, quality, absenteeism, incidents, production variations, duration of a process or resource allocation.
What’s really required — and what the most successful companies do very well — is to ensure data is turned into information that is used to generate knowledge which results in intelligence.
OK, so what is data? Data are values of qualitative or quantitative variables, typically the result of measurements or observation. Here is an example: A health and safety officer in a manufacturing operation may collect data on incidents including date, time, location, severity, lost time, cause, age, gender and work experience of an operator or injured party.
Some common data sets used to understand safety throughout an organization include near misses, hazard tracking, industrial hygiene monitoring, job hazard analysis, safety behaviour, safety culture, training data, incident tracking, inspections and action item follow-up.
In simple terms, data is transformed into information by organizing and processing it. The use of charts and graphs is helpful in this regard. Other ways of processing data include the calculation of the average of a data set or calculation of various other measures of variability in the data.
When collecting data for any purpose, be sure the following questions are addressed:
• How should record-keeping be managed?
• What method of backing up data should be used and how often?
• Who is responsible for the integrity of the data?
• How is the integrity of the data established and verified?
Once collected, where do you start? Many people will dive right into a bunch of data and try to build reports and data analysis. A better approach is to start with a problem or an improvement area then look for the right data that will help you understand the problem or monitor the results. To determine what improvements matter most, perform a departmental purpose analysis to determine where you can best apply your DDDM talents. Or, better yet, review your organization’s latest strategic plan and look for stated strategic goals and key performance indicators (KPIs). Determine which KPIs are within your sphere of influence and focus on those.
When you’re ready to analyze the data, ask some simple questions, such as “Is the data trending up or down?” Then move into tougher questions, including “Is the variable that’s trending up or down made up of more than one component?” or “Is one trending up while another is static or trending down?” Finally ask, the really important questions: “Why?” and “What can be done about it?”
Going forward, the next generation of safety professionals will possess some capacity for DDDM, and future safety management systems have to include DDDM. If this sounds like a novel idea to you, then the next training you should be looking for is foundational education in DDDM.
