The Jigsaw Puzzle Master’s Guide to Solution Engineering

By Eddie Oz
Sunday, June 14, 2020

From complex technical integration with third-party systems to solving everyday operational problems, it all boils down to doing a puzzle. 
Here’s how.

Jigsaw puzzles have always been a favorite hobby for me. Ever since I was a little kid, I’ve relished the satisfaction of piecing together an image with the power of logic, visual acuity and speed. 
Over the years, my interest in puzzles evolved into more complex riddles, and further expanded to an ardent love of math.

So it’s no wonder that devising solutions is what I do for a living. 
I’ve realized that the methodology applied in the professional realm is no different than how jigsaw puzzles are assembled. 
I’ve put down this step-by-step guide as a walk through solution-engineering processes. Here goes:

Step 1: Mindset — the puzzle can and will be solved

A jigsaw puzzle has an inherent premise: it can be solved. 
It’s not an open ended, theoretical question that entails years of research, which may or may not be fruitful. However complex and however many pieces it contains, a jigsaw puzzle makes for a riddle that is concrete and clear. 
It may be time consuming, but there’s no doubt that it is possible.

A can-do approach is key for effective problem solving. 
Lack of faith in the feasibility of a solution is a death sentence for the project. This is the way I approach the challenges I face: they can and will be solved. 
Just like a jigsaw puzzle.

Step 2: Understanding the full picture

Assembling a jigsaw puzzle begins with a careful view of the visual on the box. Solution engineering is no different: for an effective and efficient solution, you have to have a good grasp of the entire picture: is the challenge purely technical? Does it require some educational mitigation? Is the right person dealing with the problem? Has the problem been correctly identified? Have we already encountered a similar challenge in the past? Do we have the resources in the company to solve this? If not⁠ — is there a third-party, off-the-shelf solution available on the market? What’s the cost? What’s the impact? The more accurate the view, the better your solution will be. 
Here’s an example: Natural Intelligence, the company I work with, runs comparison marketplaces that serve to match consumers and brands, closely tracks both web and conversational journeys in and outside of its websites. Hence, a while ago we were tasked with finding a solid call-tracking system. We quickly realized that there are dozens of possible solutions on the market. Which one is suitable to our needs? We started off with mapping what had been broken that made a solution necessary in the first place. 
Once the caveats were identified, we began scouting for fitting services. Through a somewhat arduous process, involving many implementations and designs, we were finally able to grow our accuracy level of call-based transactions from 30% to 90%

Identifying a recommended service is only a portion of the challenge⁠ — you have to identify and then answer quite a few questions en route to final integration. What are the requirements? What tools should it have? 
Do we need support?

Step 3: Set up the frame

Pull out all the straight-edged pieces in the puzzle. Composing the outer edge of the puzzle, these pieces make an accessible entry into the solution, and once put together in sequential order, they make for the boundaries of your puzzle. 
In the real-life technical problems that I solve, the frame marks the essence of the challenge as well as its boundaries.

Here’s a real-life example: when we implemented the phone call tracking solution, one of our partners, legitimately protective of their clients’ personal data, refused to share clients’ complete full numbers. However, they were still very much inclined that we track the performance of call-based transactions, so we had to solve this riddle: how to both track the performance of calls while being blind to the phone number? In this case, this partner’s limitation was the line that we couldn’t cross⁠ — the frame of the jigsaw puzzle. 
We ultimately provided this partner with alternate solutions that wouldn’t compromise the data.

As a large portion of my work involves production-sensitive challenges⁠ — loose cogs in what must be a well-oiled machine that supports the operational business of my company⁠ — I must establish a clear and agreed framing for the problem, so we can deliver an effective solution in a timely fashion.

Step 4: Color matching

The vast majority of jigsaw puzzles are made up of pieces in varying colors: the blue pieces make up the sea, the green illustrates the trees, the red delineate flowers, and so forth. Separate the colors into groups, putting all the blues together, apart from the greens, separate from the reds. 
Once broken into color-coded piles, clarity begins to form.

Similarly, while engineering a solution, the act of breaking apart the indecipherable jumble into its elements is key for the effective fixer. 
Our projects many times begin with a hodgepodge: technical problems in our systems are tangled with communication problems between the teams. 
Throw in a random, unrelated, bug that fumbles with the data (there’s always a bug that skews the attention), and it all becomes impossible. Or not.

If you look closely, you can classify the elements: technical, communicational, legal, or data-oriented. When the elements come into view, you can see what requires the majority of your attention (that is, the blue pieces are in fact many hues ranging from turquoise to dark navy), and what is in fact a breeze (i.e, the red flowers are made up of just 4 pieces).

Step 5: Start assembling

Your frame is ready. Your piles are all set. Now the fun part begins⁠ — the intricate work of putting the pieces together by forming connections with some trial and error.

This is the least structured process: every person has their own way of forming connections and making sense of the jigsaw puzzle. 
Granted, some trial and error will likely be part of this. And obviously, speed and accuracy come with experience. This is also the most creative part of the process: cutting through some obstacles with innovation for speedier delivery, or bringing an off-the-shelf solution to a technical obstacle that may otherwise take years and gargantuan costs if developed in-house.

Here’s a real life example. Natural Intelligence, processes large volumes of revenue data received from partners and clients. The data streams in from various sources. When we’re lucky, we receive structured, clean data sets that can be automatically processed through our systems, informing our business reps, product managers, and other stakeholders as they perform their tasks and run the business. But in other cases, revenue data is sent over the age-old… email! Email is a fantastic communication tool, but it’s less suitable for presenting and structuring data.

What do we do?

The simple solution is to get someone to do the manual work of pulling out the information, recording it in Excel, and running it through our systems. Simple? Yes. Efficient? Far from it. Error-prone? You bet. Fortunately, we’re not the first to encounter this challenge. We found an off-the-shelf solution that parses out information sent over email to Excel. 
The cost was almost negligible.

Here’s another story: one of our partners needed an integration solution with Salesforce⁠ — the platform we use to manage our sales data. 
The client from a non-tech industry found this solution overwhelmingly complex. For us, on the other hand, it was essential that they complete this integration, which would render this relationship much more beneficial to both parties. We solved the challenge by putting together an instruction manual for the client’s team⁠ — we found a simple plug-in that would work with their systems and even shared the cost of this integration. 
In this example, the problem started off with a technical glitch (lack of integration with a shared system), but a closer inspection revealed a need for education and budgeting.

Step 6: Congratulations! Puzzle solved

To conclude: a good problem solver has a lucid view of the picture, breaks it apart into pieces, and proceeds to tackle them one by one. 
While forging connections between pieces, the savvy engineer will not stop at effective shortcuts and innovative solutions to bridge technical and managerial problems alike. 
The experienced solution engineer knows that time is of the essence. 
As long as quality isn’t compromised, the speedier, the better⁠ — particularly in production-sensitive areas. 

So, if you have a minute to celebrate the beauty of an assembled jigsaw puzzle⁠ — make the most of it.

Usually, you’ll be on the next one in no time.

The Jigsaw Puzzle Master's Guide to Solution Engineering
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