Recipes don’t always work

recipes dont work

How many times have you followed a recipe while cooking something in the kitchen and had the result turn out not to be quite what you expected?     If you’re like me, more times than not. Sure, the result is edible (usually) but isn’t quite what you expected or what was described in the recipe.    Recipes don’t always work.

Recipes are great starting points for cooking great food, but a good cook will tweak that recipe to create something that is perfect for them / their family. They tweak whatever recipe they have to match their own tastes and their own inclinations.

The same is true of good leaders.

A good leader isn’t going to take something they read in a book like Good to Great and implement the idea(s) without tweaking the ‘recipe’ for their own organization, people and culture.  Just because something worked for one organization doesn’t mean it will work for another without changes. Just because Jim Collins provides insight into how a company was successful in the paste doesn’t mean that the thing that company did will work for your organization today.

Stephen Covey made a lot of money by telling people there are seven habits that successful people follow but I guarantee there are more than seven habits that successful people can follow and plenty of successful people that do things other than his seven habits.  The same is true of systems for time and task management. Sure, the systems work but you have to ask whether they will work for you. If you don’t sit down and actually look at your task list, that task management system isn’t going to work at all for you.

I hear people say that you’ve got to write 1000 words a day (or 500 words a day…or X words a day) in order to ‘be creative.  I hear people say you have to meditate or get up at 4AM or get 10 hours of sleep or exercise every day.  I hear people say all sorts of things that have worked for them but that same process may or may not work for you and/or your company.

Recipes are great starting points and they can help create starting points for what you’re trying to do. That said, they aren’t the ‘end-all-be-all’.  When you’re working with people, recipes generally don’t work as they are written down. They require some tweaking and some changes to fit into your personal approach and/or your organization’s culture.

Next time you’re trying to do something and you think some ‘recipe’ you’ve found is the way to go, just remember – recipes don’t always work.

Incrementally Better

Sunrise from Yavapai Point, Grand Canyon

Recently, I spent a week at the Grand Canyon National Park in Arizona. While visiting, I stayed in Tusayan Arizona, just a few miles outside the park’s entrance.

When I visit places like this, I don’t have high expectations when it comes to dining. I’m not a food-a-phile (or whatever you call them) but I do like good food and I like to have a selection of choices, especially since I’ve recently been trying to eat cleaner in recent months.

I noticed that in the park and in Tusayan that there were really no highly rated places to eat. When looking at places like tripadvisor and other ‘rating’ sites, there were very few places rated more than a ‘4’, which is an anomaly, even for a small area like Tusayan.   Granted, the area is a tourist destination so ratings might be skewed because of that, but I’ve been many other places that were tourist destinations and there were always plenty of places to eat that were very highly rated.

I tried a few places in Tusayan that were highest on the list (rated around 4 on tripadvisor) and I have to say I was wholly unimpressed, and actually quite disgusted by some of the food I received.   I did have a good breakfast at Bright Angel Lodge and a  good lunch at the El Tovar dining room in inside the park but everything I had in Tusayan was very disappointing.

No, I’m not just complaining.

OK…maybe I am complaining, but I thought this was a good ‘teachable moment’, even if the people running the Tusayan restaurants will never read this. Tusayan is a very small town (something like 500 full-time residents) and is focused on grabbing money from tourists rather than providing value for money. The places I ate were over-priced and very (very) underwhelming in terms of service and value.  While traveling to a place like this, you know you are going to be over-charged for what you get, but you at least expect to have a decent meal for the price you pay, but that’s not the case in this small tourist town.

I’m not here to complain about tourist traps though. I’m here to talk about how people (and companies) fall into the trap of thinking they don’t have to provide good service (or good food or good value, etc) to their customers because they have a ‘monopoly’ on what they do.   How many times have you run across a company that is the ‘industry leader’ and found that they just really didn’t care that much about servicing their customers in any meaningful way? How many times have you purchased something and felt completely underwhelmed by the service and/or the product received?

Just because you have a monopoly on something, doesn’t give you the right to stop trying.

There’s a very well known company within the financial space that has had a near-monopoly on financial data, news and content for many many years (they were one of the first – if not the first – in this particular space).  This company charges an enormous amount of money for their product and have stopped innovating over the last few years. They’ve sat back and rested and expect that their ‘monopoly’ will carry them through…and they may be right. They may have such a lead on the market that nobody will ever replace them but there are plenty of companies trying to replace them.

It wouldn’t take much for someone to come along and blow up the monopoly that this financial company has. It would just take some money and some marketing and they’d be in a bad spot – and they may already be in said bad spot…but it might take years for them to figure that out.

Just like this financial company, the restaurants in Tusayan think they have a monopoly on their industry. There’s not a lot of room to build in the town (its surrounded by Navajo lands and National Forest), but all it will take is for one of these places to go out of business and some enterprising chef to come in and create something incrementally better than what exists today.

It’s not always about creating the next ‘unicorn’ to make a billion (or million) dollars. Sometimes, you just need to create something that does things just a little better than others do. Don’t get me wrong – I’m not saying to do more than the other person (because more isn’t always better)…just find a way to do things better.  Maybe, just find a way to do things better than you did yesterday.

Do something incrementally better and see what happens.

Foto Friday – Pika Rocky Mountain National Park

Pika Rocky Mountain National Park

While in Rocky Mountain National Park last year, I stumbled upon an area perfect for Pikas. I sat myself down next to some rocks and waited. After about 15 minutes I started hearing the ‘squeeks’ that you’d hear from these cute little animals. Quickly thereafter, I started seeing the scurrying around and spend about an hour grabbing photos of them.   The below is one of those cute Pikas.

About Pikas:

A key characteristic of the American pika is its temperature sensitivity; death can occur after brief exposures to ambient temperatures greater than 77.9 °F.  Therefore, the range of the species progressively increases with elevation in the southern extents of its distribution.  In Canada, populations occur from sea level to 9,842 feet, but in New Mexico, Nevada, and southern California, populations rarely exist below 8,202 feet.

You can learn more about this great little animals here.

See more photos at my dedicated Photography website. If you like my photography, feel free to support my addiction habit by purchasing a copy for your wall and/or visiting Amazon (affiliate link) to purchase new or used photographic gear.


Pika Rocky Mountain National Park – Buy a copy for your wall

Pika - Rocky Mountain National Park
A closeup of a Pika that I found in Rocky Mountain National Park. Captured with Sony a9 with Sony 100-400 GM + 1.4x extender

Do you need machine learning? Maybe. Maybe Not.

Do you need machine learning? Maybe. Maybe Not.

I’ve recently written about the risks of machine learning (ML), but with this post I wanted to take a step back and talk about ML and general. I want to talk about the ‘why’ of machine learning and whether you and/or your company should be investigating machine learning.  Do you need machine learning?  Maybe. Maybe not.

The first question you have to ask yourself (and then answer) is this:  Why do you want to be involved with machine learning? What problem(s) are you really trying to solve?  Are you trying to forecast revenue for next quarter? You can probably do just fine with standard time series modeling techniques.  Are you trying to predict house prices in cities/neighborhoods around the world? Machine learning is probably a good idea.

I use this rule of thumb when talking to clients about machine learning:

  • If you are trying to forecast something with a small number of values / features – start with standard forecasting / modeling techniques.  You can always move on to machine learning after working through the standard approaches.
  • If you need to combine multiple data sets to create new knowledge and actionable insights, you probably don’t need machine learning.
  • If you have a complex model / algorithm with many features, then machine learning is something to consider.

The key here is ‘complex’.

Sure, machine learning can be applied to simple problems but there’s plenty of other approaches that might be just as good. Take the forecasting revenue example – there are multitudes of time series forecasting techniques you can use to create these forecasts.  Even if you have hundreds of product lines, you are most likely using a few ‘features’ to forecast one outcome which can easily be handled by Holt-Winters, ARIMA and other time-series forecasting techniques. You could throw this same problem at a ML algorithm / method and possibly get slightly better (or worse) results but the amount of time and effort to implement an ML approach may be wasted.

Where you get the most value from machine learning is when you have a problem that really vexes you. The problem is so complex that you just don’t know where to start. THAT is when you reach for machine learning.

Do you really need machine learning?

There are a LOT of people that will immediately tell you ‘yes!’ when asked if you should be investigating ML.  They are also the people that are trying to sell you ML / AI services and/or platforms. They are the people that have jumped on the band wagon and are chasing the latest buzzwords in the marketplace.  In 2 years, those same people will be jumping up and down telling you need to implement whatever is at the top of the buzzword queue at the time.  They are the same people that were telling you that you needed to implement a data warehouse and business intelligence platforms in the past.  Don’t get me wrong – data warehouses and business intelligence have their places but they weren’t right for every organization and/or every problem.

Do you need machine learning? Maybe.

Do you have complex stream of data that you need to process and turn into knowledge and actionable intelligence?  Definitely look into machine learning.

Do you need machine learning? Maybe not.

If you want to ‘do’ machine learning because everyone else is, feel free to investigate it and start building up your skills but don’t throw an enormous budget at it until you know beyond a shadow of a doubt that you need machine learning.

Or you could call me. I can help you figure out if you really need machine learning.

Photo by marc liu on Unsplash

Foto Friday – Rocky Mountain National Park

Late last year I had the opportunity to spend a week in Rocky Mountain National Park (RMNP).  Strangely, I’d never actually been to RMNP although I’ve been just about everywhere else around RMNP.

The trip was part of a trip to nearby Denver for a conference so I didn’t get as much time to spend in the park and surrounding areas as I’d wanted to, but I did spend every morning in the park for sunrise – and loved every second that I had there. I’d had a few pre-planned locations found during trip research and got a couple of really good sunrise shots but didn’t get as many opportunity for Elk that I wanted.  That said, I did get surprising access to multiple Moose during the trip as well as a few Pika.

Before we get into the trip photos, let me share the gear I used on the trip. If you want to know more about the gear, let me know and I can share my thoughts.

Now, onto the photos. If you would like to purchase a copy (or copies) of any of these photos, check out my portfolio site.

Sunrise and Fall Colors
Sunrise over Sprague Lake in Rocky Mountain National Park.
Red Sunrise
Sometimes, a quick ‘snap’ of the camera turns into something special. While I was walking around the lake after sunrise, I grabbed this quick snap, which turned out much better than expected.
Black & White Lake
Sprague Lake in Rocky Mountain National Park with a black and white treatment
Rocky Mountain Pika
While in Rocky Mountain National Park, I knew I wanted to find some Pikas. I was lucky and found a perfect habitat for them without much hiking. This is the outcome of my first visit.
The colors of sunrise
While wondering around Rocky Mountain National Pakr (RMNP) I found this spot and thought it’d be a good place for a sunrise photo. There wasn’t a lot of clouds that morning but I did get some fog that rolled in while the sun was rising. The fog plus the few clouds with color add some interest to this photograph.
Moose in the Morning
While at Rocky Mountain National Park, I had the chance to photograph a few moose. While walking down the road toward where a lot of folks said some moose had been spotted, I noticed this Bull Moose standing in the trees perfectly lit by the sunlight.
Moon over the Rockies
Went out to Sprague Lake in RMNP to capture sunrise hoping that the clouds would stick around. While setting up, I took a couple shots while the moon was out….and turned out the moon shots were so much better than the sunrise photos (the clouds disappeared before the sun came up).

See more of my photography here.

The Data Mining Trap

a photo of a lobster trap by the see

In a post titled Data Mining – A Cautionary Tale, I share the idea that data mining can be dangerous by sharing the story of Cornell’s Brian Wansink, who has had multiple papers retracted due to various data mining methods that aren’t quite ethical (or even correct).

Recently, Gary Smith over at Wired wrote an article called The Exaggerated Promise of so-called Unbiased Data Mining with another good example of the danger of data mining.

In the article, Gary writes of a time that noted physicist and Nobel Laureate Richard Feynman gave his class an exercise to determine the probability of seeing a specific license plate int he parking lot on the way into class (he gave them a specific example of a license plate).  The students worked on the problem and determine that the probability was less than 1 in 17 million that Feynman would see a specific license plate.

According to Smith, what Feynman didn’t tell the students was that he had seen the specific license plate that morning in the parking lot before coming to class, so the probability was actually 1. Smith calls this the ‘Feynman Trap.’

Whether this story is true – I don’t recall ever reading it from Feynman directly – (although he does have a quote about license plates), its a very good description one of the dangers of data mining — knowing what the answer will be before starting the work. In other words, bias.

Bias is everywhere in data science. Some say there are 8 types of bias (not sure I completely agree with 8 as the number, but its as good a place to start as anywhere else). The key is knowing that bias exists, how it exists and how to manage that bias. You have to manage your own bias as well as any bias that might be inherent in the data that you are analyzing. Bias is hard to overcome but knowing it exists makes it easier to manage.

The Data Mining Trap

The ‘Feynman Trap’ (i.e., bias) is a really good thing to keep in mind whenever you do any data analysis.  Thinking back to the story shared in Data Mining – A Cautionary Tale about Dr.Wansink, he was absolutely biased in just about everything he did in the research that was retracted. He had an answer that he wanted to find and then found the data to support that answer.

There’s the trap. Rather than going into data analysis with questions and looking for data to help you find answers, you go into it with answers and try to find patterns to support your answer.

Don’t fall into the data mining trap. Keep an open mind, manage your bias and look for the answers. Also, there’s nothing wrong with finding other questions (and answers) while data mining but keep that bias in check and you’ll be on the right path to avoiding the data mining trap.

Photo by James & Carol Lee on Unsplash