Something New: Rant – Experts Suck
It seems like every time I turn on the news or scroll through my social media feed, there is another “expert” spouting off their supposedly well-informed opinions on a variety of topics. These experts range from scientists and doctors to politicians and business leaders, and they are often held up as the ultimate authorities on their respective fields. But as someone who has spent a fair amount of time studying and researching various topics, I can confidently say that many of these so-called experts do not actually know what they are talking about.
For starters, it is important to recognize that expertise is not a blanket term that can be applied to all individuals within a particular field. Just because someone has a degree or a fancy job title does not automatically make them an expert in their field. In fact, many of the individuals who are hailed as experts are often just parroting the opinions and theories of others, without actually understanding the underlying concepts or having any firsthand experience or knowledge.
Furthermore, many experts are biased in their viewpoints and are more interested in advancing their own agendas or careers rather than presenting accurate and unbiased information. This is especially prevalent in fields such as politics and business, where experts often have vested interests in promoting certain policies or products. In these cases, the expert’s opinions are often influenced by their own personal or financial motivations rather than by the pursuit of truth.
Another issue is the fact that many experts rely on outdated or flawed information. The world of science and research is constantly evolving, and what was once considered true can often be proven false with new discoveries. However, many experts are resistant to change and continue to hold onto outdated beliefs, even when there is overwhelming evidence to the contrary.
This is particularly concerning when it comes to fields such as medicine, where experts can have a direct impact on people’s health and well-being. I have seen numerous cases where experts have refused to acknowledge new research or treatments, leading to people suffering or even dying because they were not given the most effective care.
In addition, many experts have a narrow focus and are unable to see the bigger picture. They are so focused on their specific area of expertise that they are unable to consider other factors that may be relevant to the issue at hand. This can lead to a lack of understanding and a failure to address the root causes of problems.
For example, I have often heard experts discussing environmental issues without considering the economic and social implications of their proposed solutions. These experts may have a deep understanding of the science behind climate change, but they often fail to consider the impact their solutions would have on communities and industries.
Furthermore, experts often present their opinions as facts, even when there is a lack of consensus or evidence to support their claims. This is especially prevalent in the media, where experts are frequently called upon to provide their “expert” opinions on various topics. However, these experts are often just voicing their personal beliefs and are not necessarily presenting facts or evidence-based information.
This can lead to confusion and misinformation, as the public is often unable to distinguish between expert opinions and actual facts. It also undermines the credibility of actual experts and their research, as the public may become skeptical of all experts due to the misinformation being presented as fact.
Finally, it is important to recognize that no one person or group can be an expert on everything. It is impossible for any one individual to have a comprehensive understanding of all topics, and it is dangerous to blindly trust any one expert or group of experts without questioning and verifying their information.
In conclusion, while there are certainly experts out there who are knowledgeable and well-informed, it is important to be cautious when it comes to accepting their opinions as fact.
Something New: Rant – Gaussian Distribution
The Gaussian distribution, also known as the normal distribution or bell curve, has long been heralded as the go-to model for understanding and predicting statistical phenomena. But as it turns out, this seemingly reliable model has a multitude of problems that make it far from a perfect fit for many real-world situations.
First and foremost, the assumption of a Gaussian distribution often relies on the assumption of a large sample size. This means that the distribution is only expected to hold true when there are a significant number of observations, typically around 30 or more. However, in many cases, data sets may not be large enough to accurately fit a Gaussian distribution. This can lead to flawed conclusions and predictions based on an inaccurate model.
Even when a Gaussian distribution does seem to fit a data set, it is often the result of oversimplification. Many real-world phenomena do not fit neatly into a single distribution, and instead may have multiple underlying distributions that contribute to the overall shape of the data. The Gaussian distribution is often used as a default model, despite not necessarily being the most accurate representation of the data.
One problem with the Gaussian distribution is its reliance on mean and standard deviation as the primary measures of central tendency and dispersion. While these measures can be useful in certain situations, they can also be misleading in others. For example, if a data set has a few extreme outliers, the mean can be heavily influenced by these values and may not accurately represent the majority of the data. In these cases, the median or mode may be more representative measures of central tendency.
Another issue with the Gaussian distribution is its assumption of symmetry. While many data sets do tend to be symmetrical, this is not always the case. In fact, many real-world phenomena exhibit skewed distributions, where the data is not evenly distributed around the mean. The Gaussian distribution cannot accurately model these types of data sets, leading to flawed conclusions and predictions.
The Gaussian distribution also assumes that the data is continuous, meaning that there are an infinite number of possible values within a given range. However, many data sets are discrete, meaning that there are a finite number of possible values. The Gaussian distribution cannot accurately model these types of data sets, leading to flawed conclusions and predictions.
One of the biggest problems with the Gaussian distribution is its over-reliance on statistical tests that assume a Gaussian distribution. These tests, such as t-tests and ANOVA, are often used to compare means and determine statistical significance. However, if the data does not fit a Gaussian distribution, these tests may produce inaccurate results. This can lead to incorrect conclusions being drawn and potentially harmful decisions being made based on flawed data.
The Gaussian distribution also has a problem with extrapolation, or using the model to make predictions beyond the range of the observed data. While the Gaussian distribution can be accurate within the range of the observed data, it may not hold true outside of this range. This can lead to flawed predictions and incorrect conclusions.
Finally, the Gaussian distribution often suffers from a lack of interpretability. While the mean and standard deviation can provide useful information about a data set, they do not tell the whole story. Other measures, such as skewness and kurtosis, can provide additional insight into the shape and distribution of the data. However, these measures are often overlooked in favor of the Gaussian distribution, leading to a limited understanding of the data.
In conclusion, the Gaussian distribution is far from a perfect model for understanding and predicting statistical phenomena. Its reliance on large sample sizes, oversimplification of data, assumption of symmetry and continuity, and over-reliance on statistical tests can lead to false conclusions.
Something New: Rant – Product Management Best Practices
Product management best practices are guidelines and techniques that are widely accepted as effective ways to manage the development and marketing of a product. However, despite their popularity and widespread use, there are several problems with product management best practices that can hinder the success of a product.
One issue is that best practices can often become overused and formulaic. When product managers follow best practices too closely, they risk losing their creativity and uniqueness. For example, many best practices recommend using market research to gather customer insights and inform product decisions. While market research is certainly important, relying solely on it can lead to a product that is too generic and doesn’t stand out in the market.
Another problem with best practices is that they can be overly rigid and inflexible. Product managers may feel pressure to follow best practices to the letter, even if the specific circumstances of their product or market don’t warrant it. This can lead to a lack of agility and the inability to adapt to changing market conditions.
Another issue is that best practices can be influenced by the biases and personal experiences of those who develop them. For example, best practices that are developed by successful Silicon Valley startups may not be applicable to smaller companies or businesses in other industries. This can create a one-size-fits-all approach that doesn’t take into account the unique needs and challenges of different products and markets.
Additionally, best practices can be time-consuming and resource-intensive to implement. Product managers may find themselves spending a disproportionate amount of time and resources on following best practices, rather than focusing on the needs of the product and the customers. This can lead to a lack of focus and a dilution of resources that could be better spent elsewhere.
Another problem with best practices is that they can create a false sense of security. Product managers may feel that as long as they are following best practices, they are doing everything they can to ensure the success of their product. However, this can be a dangerous mindset as it can lead to complacency and a lack of innovation.
Best practices can also lead to a lack of accountability and ownership. Product managers may feel that as long as they are following best practices, they are absolved of any responsibility for the success or failure of their product. This can lead to a lack of ownership and accountability, which is essential for driving results and ensuring that the product is meeting the needs of the customers.
Overall, while product management best practices can be useful guidelines, they can also create problems if they are blindly followed or not tailored to the specific needs of the product and market. It is important for product managers to use best practices as a starting point, but to also be flexible and open to adapting and innovating in order to achieve the best results for their product.
Data, Optimization, and Growth – The 3 Sides of the Product Management Pyramid
Its been a while since I last wrote for this blog, and in that time I have moved away from straight optimization into a more central Growth role, focused on data, optimization, and product managing core systems. The one thing that unifies all these disciplines is the one core mission statement:
“Use data and added information to improve the efficiency of resources towards the bottom line for the company.”
Or to put even more simply:
“Make the organization work smarter, not harder.”
The issue of course is that you have a multitude of people in an organization, all of which think their work is perfect and everyone else is the problem. Fingers get pointed all the time, some real, most not, due to cognitive dissonance and the stories that we convince ourselves of to justify our efforts. The truth is that most people are little more then blind moles digging through the preverbal dirt, and when they manage to not get crushed by a rock we tell ourselves that we are masters of our domain.
So, when someone comes in and tries to say, hey, maybe don’t do what you are doing or tackle a problem a different way, they react with all the grace and thoughtfulness of a rabid dog cornered by a pack of small cats. This is especially true in the product management world where so much is made of stories about users, or success, or trying to come up with some giant master idea that will solve all the problems in the world.
There are no magical ideas.
There are no silver bullets.
Yet that group, along with Design, seem so focused on ideas that they miss the forest for the trees. Success and failure isn’t based on a specific idea. Yes, there are better ideas then others, but the situation that those ideas are born into have so much more to do with overall success then the idea. The best idea in the world acted on poorly is going to have worse outcomes (See every redesign ever done), then the most mediocre idea acted on correctly.
With that in mind I wanted to take a moment and discuss what actually matters from a Product focused view of the business world, and how data can and should be a part of that conversation. Instead of just listing all the things to not do, and there are many, many, many books (easiest first read is The Halo Effect) on that subject, I want to instead focus on 3 core disciplines that will make any organization or Product group effective.
Scope trumps Ideas
The ability to properly scope and phase a project is the single most important part of any new initiative. If you put too much into an idea and you don’t have proper measures in place, then you are just wasting resources and become even more invested in its outcome. Under scope a project and you may see a change in behavior but no idea what, if any, of it is noise versus real outcomes.
Being able to properly create an MVP, being able to properly measure and be disciplined in going forward with that first or any phase, determines both the efficiency of the resources as well as the long-term success of a project or idea.
Scoping is the Product Managers super power. Failure to do it and you are a 4year old child trying to lift a car. Do it properly and you are the Hulk lifting a mountain.
So why then do so many product managers suck at this skill? Part of it is that most product managers start out in other disciplines, all of which focus on small pieces of the big picture. You may come from sales (scope is a completed sale), engineering (did it get done, yet or no), design (does it look pretty, yes or no), or a thousand other places. Scoping is a skill that is unique to the practice that most people have never spent more then a few seconds thinking about. On top of that you are always under pressure to sell an idea or to show progress, which means checking off boxes becomes important even when getting results should matter.
The other key part here is that scoping properly ensures that you have proper tracking towards a bottom line and a clear measure of success. Any phase of a project that doesn’t have a clear start and stop and a clear specific measure of bottom line (Not Texas Sharpshooter based) success is doomed. Its that simple. Every part of the human psyche and the inherent reward system in most organizations is designed to make the people involved believe or tell others there is success, even when there is not.
Being able to identify what is the MVP, what is the natural small phases, what the go to market and what the decision points are going into a project are what is going to give it success. It also allows you to plan to resources and not try to plan resources to projects. Knowing what you are working with and what the decision point is allows you complete control to ensure you can make proper decisions.
To steal a line from one of the most overused “thought leaders” of our time:
“Its not the 1 thing you say yes to, it’s the thousand things you say no to”
We live in a world with finite resources and unlimited expectations of results. In that setting you need to make sure that you are maximizing the use of your resources and making sure that the right things get finished. The fastest way to stop that is to start pulling resources left, right and away from every project whenever a passing fancy gets into anyone’s head.
And to be clear, it doesn’t matter if that idea comes from the janitor, the designer, the engineer, or the CEO, all ideas must be prioritized and acted on correctly. You are going to have way more success attacking 3 projects properly scoped then 15 projects done on a whim. Even when you factor in the higher chances of success from fragility and high beta, the fact that things are not focused and not meeting minimum viable status means that even when something randomly does work, you wont be able to exploit that information due to all of the problems you have added to the system.
A list of completed tasks is nice, except the number of tasks has nothing to do with the value or outcomes of those tasks. I could do 500 tasks in a week and accomplish nothing. I could not finish a single task in that week and still be highly impactful to the company (though you probably need to break up your task lists). Utilization is a measure of man hours, not of value.
And to be clear, no one person should own the complete specifics of what passes and what doesn’t, that should be up to a smaller very highly informed group that represent different POVs throughout the organization. What that one person can own however is stopping all ideas and being a gate keeper until they have been properly vetted and scoped and until everyone agrees on priority.
Of course this is easier said then done, as not only do people, especially those that are attempting to climb a corporate ladder, have inverse correlation from outcomes to pleasing their bosses, but all ideas can be made to sound good, no matter how foolish they may actually be. Not only that but even if you have 20 great ideas, if you are not managing resources and balancing the needs of the business, then you may go too far down a specific rabbit hole with no way to recover.
Making your boss happy helps you, but not scrambling and not giving in to every whim, no matter how much you want to, gets you results. Like all else, what matters most to you, personal success vs. company success is upt
Balance of Vision is Critical
I was lucky enough to meet and talk with Marissa Mayer way back in the day (Pre-Yahoo) and she gave me the single best piece of advice for a product manager. The job is to equally meet 3 goals. Where you want the product to go, where the market wants the product to go, and where the market needs the product to go. If you go too far down one side (only respond to pressure, only go where you sales teams want to go, only following your own vision) you will fail in the long run.
That advice still holds true very much today. You need to balance the core infrastructure, the need for short term growth, and the need for long term growth. No matter what pressure comes your way, no matter what resources you have, no matter who is screaming or how much your sales people are saying you need feature X or to build product Y, the same discipline and focus is needed.
Don’t focus on your infrastructure, and cost and maintenance will break everything. This means you need to know where in your scoping to make the infrastructure from MVP to robust. You have to build in plans and direct measurements of where and how you are going to move to a more robust infrastructure and make sure you stay disciplined enough to not go beyond that point. Technical debt is a bitch to fix.
Focus too much on adding features for short term success (like what your sales people may ask for) and your product will lose its actual value and will lose focus on what made it successful in the first place. Measure and make sure that you get short term wins, but adding technical debt or losing focus on core drive means you may end up with a fancy looking glob of uselessness.
Focus too much on your own vision, and you won’t have the correct pieces in place or be able to adapt to market trends. Add decision points at every step to make sure to validate and try to disprove your own vision.
Each of those steps have different measurement methods and goals. Each of those must be measured correctly to make sure you are not just doing work to do work. Each one of those need people trying to disprove the common thought process to be successful. None of them work if you are just using data to validate someone’s ego.
So you have to know how to scope and where to spend resources. You need to be able to stop reacting to everything and to make sure that you have focus and the correct measurement to succeed. You also need to make sure you have the right people fulfilling the correct roles. You need to have an independent analysis team that is not beholden to your own drive and ego. You need to make sure you have a sales team trying to get people to have the best short-term success. You need a technical team who isn’t trying to please everyone and who is trying to build a robust infrastructure. You must have a team that can act fast and test assumptions. You must have a project management team who is not just filling out forms but holding people accountable to scope, phases and timelines.
These things can and often do break. Product Management is the mastering and managing of all of them. There are no magic bullets and the specifics of each of these core focuses is dependent on the situation, but its focusing and trying to master each one that will determine your long term success. Its not some shiny idea or pleasing your boss or the CEO, its about doing the small ugly things well, consistently, and with laser like focus that will determine long term success or failure of the product and team.
It’s the discipline of balancing all these visions and moving people together. Data is just the tool to add a sanity check at each phase.