Why do we call them “Solution” Architects? – Part 1
When I was young(er), I had a professor who liked to remind people that it takes a unique mindset to find solutions. At first, I didn’t understand what he meant, but it eventually dawned on me that he was getting at something very potent and relevant to everyday life. Most people spend far too much time finding problems, which is far easier than imagining solutions. For example, when walking down the street, most of us will notice a crack in the sidewalk and wonder: why doesn’t someone fix that?
In this blog post, I will challenge you to embrace a paradigm shift – instead of simply identifying problems, why not try to create solutions? Unlike most of the technically focused blogs from my BigBear.ai compatriots, I like to start with the fundamentals. This post marks the first of a series of blogs I’ll pen to help people understand the mindset that makes someone good at solving the challenging problems that face people across business, the government, and the world. But it all starts with the simplest of things – frame of mind.
During my research career, I spent countless hours working (both on my projects and those for other students and postdocs) not to challenge people with the problems impeding progress but to challenge them to see the solutions present. As an example, let’s say you own a bicycle shop. One day, a customer walks in seeking to repair a broken shifter cable. In this scenario, the customer has presented a problem.
The question then becomes, how do you, as the shop owner, respond? Does your head burst into flames as you run around, flailing arms and all? Do you calmly tell the customer you know there is no way to fix the shifter cable? Do you look it over and promptly give up – after all, this is far too challenging to solve for a simple store owner?
I would start by taking the shifter cable they brought in and try to understand how it works. Maybe the issue is simple, and it simply requires an adjustment. But, instead of waving the white flag and simply replacing the cable, perhaps there’s another alternative that will lead to a less expensive repair for the customer. While you may lose a short-term sale, your ability to solve the core problem may create a loyal relationship with the customer that will pay dividends for years to come.
These two processes, one thought-based and one action-based, are the core mentality needed to start down the road of the solution architect! If we view the issue as inherently solvable, we then focus on how to solve it. Such a paradigm shift is a far more practical and effective approach to almost any problem, whether personal, professional, or technical.
Beyond that, we need to start with the simple answers first. Note for those mystery buffs – this is not the Sherlock Holmes approach – even if it might seem so. We can eliminate obvious quick answers and focus on the more challenging and time-consuming possibilities by considering quick solutions first. This methodical approach has served me well over my career and often allows me to address complicated problems down into simple ones. Moreover, by viewing the issue in more easily digestible components, even if the totality of the problem is extraordinarily complex, I save myself considerable mental anguish.
In my next post, we’ll continue to look at how someone can use the philosophy of a solution architect in everyday life to help you approach daunting problems from a perspective that makes them seem far more manageable. But for now, let’s reiterate that to start as a solution architect, you will be best served by approaching problems with the following three things in mind:
- Feel confident that there is always a solution, even if you may not be the one to find it.
- Focus on thinking about possible solutions instead of getting hung up on the overarching problem.
- Start simple – not every problem will be simple, but they can always be broken down into uncomplicated components – you just need to find them.
About the Author
Matthew Jacobsen is the Vice President of Technology and Analytics and a Principal Data Scientist at BigBear.ai. He has more than 20 years of experience conducting a broad range of data manipulation, AI/ML, and insight generation tasks.