Gender equality in the workplace

Skellig Blog Creativity

It’s remarkable in 2019 that something as fundamental as gender equality at work is still a thing.  We need to work harder to be more inclusive.  It’s good business to have diverse opinions and perspectives woven into the DNA of a company and the teams within.  Yet, few of us really know how to practially do anything about this. The shift, like anything, begins with awareness of the issue.  While few remain in the modern workplace that would argue we don’t need to consciously be more inclusive, its useful to take stock of progress so far.  Consider the following depressing quote;

“The World Economic Forum predicts that the gender gap – measured in health, education, economic opportunity and political empowerment – won’t close until 2186. That’s 167 years from now. In the same time span, humankind went from the steam engine to Cassini’s trip to Saturn, and from carrier pigeons to the Internet. I’d like to think that we could achieve universal gender equality much faster than that.” – Richard Brandson

Diversity sounds to some like a nice to have. 

The vibe in our workplaces is often ‘Once we get this project done we can focus on nice shit like diversity and inclusion, but until then…’

In a technology driven creative field such as engineering, I firmly believe it’s a competitive BUSINESS advantage to have a diverse team of men and women. 

Better yet, men and women from diverse backgrounds. 

Better again, men and women from diverse backgrounds and a diverse age group.

Business and especially engineering is so boring when we make decisions because “that’s how we have always done it around here”.  It’s also so much less effective. Leaders typically feel more comfortable surrounding themselves with people who think like them… That usually means people from the same background and gender. This is a basic human survival mechanism. People from the same background will think like you, talk like you, protect you as their own.  From a leader’s perspective, it makes sense to have people to validate your opinion. It’s also easier in the short term to get everyone rowing in the same direction.

This might even be a good move for the individual leader in terms of their career longevity at the top.

However, it’s not typically going to be a good decision for the company or team as a whole in the long term.

What are we all supposed to do about it?

All we can do as individuals is try to reach out to colleagues and potential colleagues who are not like us.  Next time you are waiting for that meeting to start, choose someone different to make small talk with.  Make people feel more welcome. 

(If you take away one thing from this post please make it this) Consciously consider who’s ideas you are listening to, and who’s you aren’t.

The biggest challenges need multiple perspectives.

I would argue that diverse teams are more likely to constantly reexamine facts and remain objective.

I would argue that diverse teams encourage greater scrutiny of each other’s actions.

I would argue that members of diverse teams are more aware of their own biases and their own entrenched ways of thinking.

I would argue that you will see better performance overall from teams that have a diverse member group.

Diversity is good for the engineering design process.  Diversity is good business.  Proceed accordingly!

How to understand what kind of group culture you work in


What is the culture like where you work?
How do you know how to articulate the kind of culture you work in?
In the book ,”Tribal Leadership,” by Dave Logan, Halee Fischer-Wright, and John King, they broke it out into 5 categories. I read this book over 10 years ago, and it has had a profound impact on my approach.
All people who gather in groups can be defined as tribes. The group of people you work with everyday has a culture that should fit into one of these 5 stages. It has been a great asset to me to be able to identify and have awareness of the type of group I’m in when I go to a client. Most importantly, its helped me try to be conscious about the kind of culture I help encourage at Skellig.
Stage 1
The group motto here is “life sucks”. This is usually the kind of culture you find in gangs and prison. Hopefully you aren’t working in a group that belongs to this stage!
Stage 2
The group motto here is “My life sucks”. This is usually a place where people are just trying to get by. Nobody cares about the work or has any passion for what they do. People blame coworkers and management for their woes. Although the DMV (Where Patty and Selma work in the Simpsons) I go to is great, a lot of people site this type of environment as stage 2.
Stage 3
This group motto is “I’m great.. and you’re not”. Most businesses and teams are here. People here feel good about themselves and their individual contribution and they complain about the people around them not being as smart as they are. People feel deserving of special individual financial recognition.
Stage 4
This group motto is “we are great”. In these groups people work together toward a common goal, they share common values and work against a common competitor. Instead of individual recognition, its all about group recognition.
Stage 5
This group motto is “life is great”. In these groups there is no focus on competitors, only a just cause. These groups are focused on impact. One example given in the book of a Stage 5 Culture was Genentech in the early days (70’s and 80’s). Nowadays, most pharma/biotech companies have lined their halls with pictures of people whose lives they’ve positively impacted. Genentech was the first. Genentech employees would list cancer as their competitor not another pharma company.
The take away is not many groups or companies make it to Stage 4. This is something you can impact if you have the power to influence or build a team. Choose people who share values, preferably your values and energize them around a common cause.
Being aware of the culture of your group is very important for your own career development.

Preparing for AI and Machine Learning

As Henry Ford said, “If I’d asked customers what they wanted they would have said a faster horse.” Something similar is brewing in terms of machine learning in our industry.

Machine learning and AI could be great for pharma and biotech but our technology choices over the past 30 years are a bit of a problem. In industrial pharma automation, there was no apple computer to make some tough decisions on behalf of the customers. Instead, we have all sorts of half-decent “I want!” technology choices.

Back to Monopolies?

The big suppliers have taken the “give them what they ask for” approach too far. Clients are presented with a plethora of similar spec options that still need to be custom integrated. We start projects in a way too open-ended manner.

The issue here is the platforms have failed this industry in making hard calls. Nobody wants a faster horse.

Instead of focusing on being flexible enough to support every field bus ever invented, we should pick a bus technology and focus on making it really great.

It’s 2018 and we don’t have plug-and-play figured out. We can’t unplug a piece of equipment easily and plug it back in without a custom integrated, “kinda works” solution. Plug-and-play was figured out by the PC industry in the mid ’90s.

Our industry is led by big suppliers with a disease. This is the disease of give the customer more choice. It’s not working. We need focus. We need better choices, not more choices.

There are rumors the big guys are working on plug-and-play. But it goes to show how far behind we really are. Are we going to see an ad from one of the big guys in 2019 announcing a plug-and-play solution and have any reaction other than, “This should have been delivered 20 years ago”?

Moving Ahead with Machine Learning

We have widely adopted virtualization, so there is hope for us yet.

Now we see that the next big thing is machine learning and AI. Its promise is huge. However, we can’t take advantage of this until we decide on a few things. We don’t want to start integration projects with a blank slate. Instead of class-based control modules for devices, we need class-based equipment like bioreactors. We need these standard designs so that machine learning and AI can look at a wider dataset and learn. It can’t be applied in a useful way when everything is designed so custom and complex.

The big platform companies should have simplified their offerings before now. They haven’t. Now the ultimate power is with the customer. When they say “Enough is enough, we don’t want your platform only, instead we want your solution”.

The solution is more standard, less custom. Then machine learning and AI can compare bioreactor to bioreactor and we can see useful patterns. Hopefully we won’t have to wait till 2048 to see this happen.