I couldn’t find a matching pair of socks this morning….

🧦🧦

Sharing a room with a sleeping two month old, I often need to get ready in the dark. Most of my socks are dark blue, dark brown, or black and they all look the same. I’m regretting not taking the time to organize them yesterday.

Why post that on LinkedIn, the professional network? Because each day I go into work to help my colleagues and clients organize our socks. Data and really good analytics depend on a foundation of mundane work to prepare for the impact. As we work, we can look around for the right tool, data point, visualization, etc and stumble around in the dark. Or we can do the work up front to have it ready to go when it really matters: a key meeting with a client, the right fact or figure for the boss, or that time saving shortcut for a peer.

Go sort your socks.

All models are wrong, but some are useful” – applies to project planning as much as it does statistics. Especially when determining a plan for doing innovative new work, determining how long it will take can easily become divorced from reality. 

I find that the “Rule of Three” is usually a good model to start planning projects. For any given piece of work, it is helpful to estimate three equal-sized portions for planning, building, and testing.

When projects fail, it tends to happen due to the under-estimation of the plan or test phase. Just as the circumference of a circle divided by its diameter will always be pi, the time it will take to do a thing will usually be half the time it takes to plan and test that thing. Skip planning or testing to speed the thing up, and it will probably fail. It will fail by being the wrong thing. Or it will wail by being a thing that doesn’t work as it should. Ultimately the time spent fixing the thing will end up longer than build times three.

Many feel that building is the fun part where we do what we do best – be it coding, constructing, cooking, performing, or writing. Planning and testing inject purpose into the work and confirm the outcome meets the purpose. They require creativity and skepticism. They also slow us down. It is tempting to cut corners on these activities which aren’t as visible as the actual doing of the thing, but short cuts come at a cost.  

How can we be agile and move fast if everything we build takes 3x as long? Fortunately, while the rule dictates proportion, it does not determine order. The agile approach to the rule is to shrink everything down – faster planning, smaller build, and quicker QA. These iterations satisfy the rule of three while also shortening the time to delivery. Additionally, if small chunks of work are common, the entire plan, build, test cycle reduces once the team has experience knowing what to do. Finally, small chunks of effort can be done in parallel, further shrinking the time (although increasing the resource budget).

There are many ways to apply the rule and variations that apply beyond the fields of software engineering or data science. When you catch something that is in exception to the rule it is worth considering – have planning or testing been under-done? Maybe it will save you a lot of time and difficulty when the estimate models all turn out wrong.

I’m excited to share a bit about the greatest neural network I’ll ever get to train. It has over 100 billion nodes, and each minute 60 million new connections are made. Over the the next year, this network will scale to 1,000 trillion connections and master tasks more advanced than any cutting-edge digital competitor. The network is not AI – it is my two week old son Teddy.

I spend a lot of time thinking about near-term and medium-term change as I help clients tackle data and analytics challenges. Now I’m thinking a lot about the long-term and the kind of world that my son will grow up and go to work in. Automation will be everywhere. AI will replace any task that we might think of today as “manual effort” or routine. Teddy won’t need to spend weeks on data entry or document review for his career. No two days of his job are likely to be too similar. Redundant and repetitive tasks will be automated. He will instead need to sift through more data and insights than any generation in human history. He will need to be constantly aware of the curation of information in his life; some will help to sort through mountains of data while others will need to be challenged to enable higher-level thinking. Teddy also might not ever need to learn how to drive. (Although dad will be happy to teach him the ancient ways of the manual transmission if he asks!)

Teddy will face a different working world than his parents did, but it is my great hope that he will find opportunities by using his own neural network to things that AI can’t do. Success in 2029 and beyond will be based on doing human things better, for example: deploying emotional intelligence, building trusting relationships, inferring cause and effects, matching mental models to problems, and so on. Today and even tomorrow’s AI isn’t even close to replacing the humanity of our challenges and opportunities. 

As I think about how I can help my son succeed in life, I found another parallel to machine (and non-machine) learning: He needs really good training data, and that’s a huge effort! It’s on me, my wife, his caregivers, and teachers to provide ample opportunities to learn about the world in a supportive way. We need to make sure our input data is clearly labelled to optimize empathy, morality, and curiosity. Repetition of positive actions should cause the weights between network nodes to strengthen and increase the accuracy of his interpretation of future signals. Model training won’t be linear and will continue to optimize every day. There are many components his ensemble brain will need to master – spatial awareness, coordination, language, logic, math, and so on. Machine learning models are able to do a lot of these components, but he will surpass many of them easily in matter of months. 

It is incredible to behold the significance of change and development happening every day, and humbling to know that the next few years are the most critical to set the foundation for a lifetime of learning. Training the incredible neural network in our heads is a journey of a lifetime.

TLDR: I’m very excited to announce the birth of my son. He’s going to have to get used to a very proud and nerdy dad.