I love to hike. It is so nice to get out and walk, see amazing views, and experience new sights and sounds.

When I was in Washington, D.C., we would try and explore a new hike each month. One Saturday, several of us from the office decided to take a trip and hike at a new location. It was a gorgeous day. When we finally reached the top, I headed over to the edge to get better views. I am not the best when it comes to heights, but I can tolerate some adventure.

All of a sudden I heard a guy screaming at his kid from behind: “Son, stop right there, don’t get too close. You’re going to fall over the edge and die.” At first, I thought he was a bit crazy and dramatic — especially as he was yelling at his adult son. But then I started thinking about how many times we hear about people going a little too far and ... it doesn’t end well.

We all have different comfort zones and risk tolerances when it comes to heights. But how about when it comes to food safety? Are we all on the same page? I gave a talk at a seafood conference a few weeks ago and discussed "variation." Whenever I discuss this topic, I like to use the example my friend shared with me: Variation defines how close to the cliff you want to get. Some people want to stay as far away as possible while others don’t mind taking some risks.

I think in food safety, it’s a balance. It’s a calculated risk. I was safe standing close to the edge of the cliff but probably would not have felt as safe if I had ventured out onto the teetering jutted rock ledge.

Our industry often talks about industry averages as a way to compare and contrast food safety and quality systems. I think averages are important, but more important are the variations we see around those averages. Those variations represent how close to the cliff we want to get. Large variations will take us closer to the cliff, maybe even closer than we want. High variation can mean sometimes we may even fall off the cliff — and no one wants to see that happen. Falling off means unsafe food or, worse, an outbreak with illnesses and maybe even death.

On the flip side, controlled variation can help us determine how close we want to be to the edge of disaster.

How do we control variation? First is knowing what your variation is; second is targeting a multi-hurdle, multi-technology approach to reducing that variation; third is collecting appropriate data to monitor variation; and fourth is to really investigate corrective actions when things don’t go as planned. I think if we shift focus to add "variation" instead of just averages, we may see the data a little differently and we may not get close enough to the cliff to have concern.

How close to the cliff do you want to be?

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