Pareto was right. The world operates at 80/20.

Striving for perfection is the opposite of operating perfectly. Getting to 100% has a cost disproportionate to the added value – 80% is usually more than enough.

Look around at what you do. How often does something new get released that can answer every single question around it with perfection? There are always grey areas that can’t be known before you hit the start button. Trying to know everything before you hit go only adds time and doesn’t really reduce risk much.

Most people try to get to perfect in order to mitigate their concept of risk. Surely the more they know, the less risk they have. The problem is that this assumes you can know everything you need to know. It’s simply not possible. Experience in the real world always leads to new and unexpected knowledge.

The reality is, even Pareto saw this trend of 80/20 in populations, decisions, and groups of highly generalized things. 20% of your time generates 80% of your profits. 80% of your time is occupied by only 20% of the things you need to work on.

Are you spending your time right?

It’s the circle of risk management!

With the election of Donald Trump, I have seen an associated uptick in the number of consultants talking about the increasing need for risk management. Everywhere I look I’m seeing “risk management.”

But talking about risk now is like trying to sell fire insurance after your house has burned down. It may be on top of people’s minds but the right time to talk about risk management is always. But especially prior to surprising events like Brexit and the election of President Trump. Risk management should be part of the everyday playbook in how problems get solved.

Consultants, as a group, are very good about selling for the situation in front of us today or that occurred yesterday. Like clockwork:

  • If the stock market takes a hit you will see all kinds of mentions about aligning strategy with EPS and EBITDA.
  • If something unexpected happens in the world they will bring up Risk Management.
  • If unemployment starts going up they will talk about aligning your workforce to the next three years.
  • If a treaty is about to be signed addressing climate change or the cost of carbon you will see them going hard after sustainable initiatives.

Anyone focused on yesterday or today is not really looking out for your best interests – as they say on Wall Street, buy low/sell high! When unemployment is going up they should be talking to you about how you can leverage the opportunity to get new customers. When your earnings are strong, you should be paying even closer attention to aligning strategy with financials.

When everyone thinks the same and acts the same you end up with these recurring cycles that strike everywhere at once. Groupthink is very pervasive and can occur even when you don’t know you are part of the group.

Being skeptical of statistics is rational. Rejecting all statistics is irrational.

Statistics is a tricky subject. To some, it seems like the ultimate in waffling – why can’t you just say what the right answer is after all? To others, it’s the ultimate in risk mitigation – but what if these 15 things were to happen. With the recent election forecasting, renewed debate over climate change data, economic modeling and any number of difficult topics, statistics has been taking a very public beating.

Here’s the thing about statistics and data modeling: it’s simply a tool. Like a gun, it can be used for good, evil or any range of grey areas in between. Like people holding guns, some know to use the safety while others wave it loaded around in the air like it’s a toy. The problem with statistics is you typically only see the outcome of a process and not what went into getting it. Essentially you get the target with holes in it but don’t see how the shooter put them there.

To carry the analogy forward, sometimes shooters get lucky and cluster 3 shots right in the middle of a target even though they would never be able to do it again. Sometimes a previously used target is placed up and the shooter only pretends to have accomplished the feat. Sometimes you have a skilled shooter that actually knows what they are doing but has a bad day and misses. Things happen.

Judging performance of statistics in hindsight is useful to be able to understand the tolerances and error associated with a given model. But even then, past performance is not always an indicator of future performance. It’s important to understand the role that statistics and data modeling should play in your decision making process but throwing them out completely is taking a very powerful tool (often perfect for the task) and choosing to never use it because it wasn’t used properly by amateurs in the past or a customer was unhappy even though the product was technically correct.

All tools that you used should be evaluated regularly to understand their best roles and uses, where they work and where they don’t, what can go right and what can go wrong. Statistics is powerful but with that power comes inherent risks that you should always keep in mind.

Trust but verify. It’s even true with math.

The quality of a decision is directly related to the degree risk is accounted for in it.

Risk mitigation and decision making are two topics that go hand-in-hand. You can’t make good decisions without understanding the risks that come with the various choices.

Risk is not always a negative consequence. A lot of time risk goes by another name: opportunity cost. Risk can often be best described as the downside difference of potential outcomes. The greater the best to worst potential outcomes (even if all outcomes are good), the greater the risk.

Sometimes we get thrown off by the word Risk. It has a negative connotation to it. Maybe it comes from all the awful hours spent playing the painful board game of the same name. Maybe it is simply that the association of risk in our minds is tied to the idea of losing money. Regardless, risk is something that exists in all decisions.

There is risk to every post I write. If I publish as-is am I leaving readers out that wanted something shorter? If I don’t spend more time editing do I hurt my credibility through the use of bad grammar? Is this really a topic that is worth posting here? These are all risks – every, ordinary, daily risks.

Thinking through the downside is how good decisions are made – even when the downside is still a good outcome. Maximizing upside is always worthwhile.

In the real world there are no such things as straight lines.

I remember in my first year out of college I was working with our racking vendor to put together a future state plan for a fully built out interior.  Step one was to go column to column and measure the actual distance between each because a 1 to 3 inch variance could throw the whole thing off.  This surprised me at the time because the spec was for all columns to be exactly 50′ by 50′ apart.   But nothing ever happens that perfectly or cleanly.

The real world is not full of straight lines, it’s full of lines that look straight but are actually slightly curved.  It’s full of lines that actually have angles in them part way through.  The straightest path is actually a large arc.  Lines that appear to be going straight today shift over time as pieces of this world expand and contract due to temperature changes.  Planning on straight lines will never win.

Sometimes a line that appears straight at 50,000 feet looks like a wave pattern at 2 inches.  Sometimes a straight line at 2 inches is a clear curve at 50,000 feet.  Perspective will always play tricks on us and usually not in the direction that we expected it to happen in.  Don’t just measure twice, cut once.  Measure with multiple devices from multiple angles and double check that you are measuring the right thing to begin with.  That straight line may not be what you think it is to begin with.

The problem with predictions based on data.

Prediction is the art of taking information about the past and applying it to future circumstances to understand what is likely to happen.  It is premised on the fact that the future will follow the same or similar rules as the past.  Behavior is expected to remain largely consistent over time.

For many applications this works really well.  Purchasing and retail wouldn’t work well at all without prediction forecasting.  Population changes by geography are largely predictable.

But often knowledge of the past changes the future.  Knowing that they are losing the youth population to larger cities a smaller community may undertake initiatives to retain or attract population by offering incentives for businesses to locate there.  Or knowing competitor trends a company revises their business strategy to attract new customers.  The past changes the future in unpredictable ways.

The future also can vary from predictions because new information becomes available.  Who could have predicted the impact of the iPhone in 2005 before it was released?  It completely changed the course of several industries within 3 years let alone a full decade.

Predictions using data (especially the buzzwordy “Big Data”) sometimes feel like they are more certain than qualitative predictions.  This is often untrue.  No data set can completely encapsulate a scenario or situation regardless of how large or unique the data set is.

Risk and the new.  Those may not be the centerpiece of prediction but they sure better be included if the prediction is going to have any worth at all.

The safe choice is rarely the same as the good choice.

In life we face many, many choices.  Daily we ask ourselves what should we wear?  To fix a sandwich or go our for a quick meal?

Questions can quickly get more complicated.  Within our own lives our decisions impact ourselves and maybe those directly around us.  In business the questions can impact quite a broader range of people.  From our employees, associates, customers, vendors, partners, etc.

Often the stress of a decision can cause us to minimize risk and go with a safe choice.  We choose the bigger vendor because everyone else does and they can’t all be wrong.  We choose to not go outside the box because change is difficult.  We choose to do nothing simply because the need can be kicked down the road.  Safe choices that no one can say are absolutely wrong.  But these choices rarely give us upside.

The biggest vendor has no reason to risk their reputation or processes on something different to better suit you.  The inside-the-box solution will not give you a competitive edge in the market.  Doing things like everyone else is a race to the bottom.  Doing nothing today reduces your most precious commodity:  time.