Introducing Changes in Delivery

My focus today is on launching a new "Tool" or "Major Version of a Tool". I am not a big fan of Humans or Human Nature, but we must embrace it. Yes, humans can be fickle, meaning they are inconstant and unreliable because they change their minds frequently. And yes, I am "human" too, and constantly challenged by my own human nature.

Moving to any major platform release can be a challenge. We tend to let ourselves get in the way of progress. The top three things are:

1. Human Nature to be Negative to Change
2. Slowing down progress is frustrating and costly (money and labor/energy)
3. Learning "new" things

Learning "new" things may not be necessary. However, doing old things and potentially learning new approaches can be beneficial. This is where I embrace change, and often look forward to changes! This gets my creative juices flowing.

Make Learning Rewarding

When we are forced to move from the mundane, mindless, and repetitive motions, we are forced to rethink what we are doing.

Confucius say, "Hear and forget, See and remember, Do and understand" is critically important in these cases. We tend to forget most of what we have learned over a period of time. We think we understand what we have been doing. Everyday, we practice things in a repetitive manner, to the point that they become mindless. Humans like to get to things that are "mindless" and based on "muscle memory".

Can we make Learning mindless?

The answer is "NO!", but we can make them fun and stimulating. This is a critical opportunity to both enrich and embrace "change". Here we can intelligently embrace our mindless behaviors for the benefits of ourselves and others.

Causal Analysis

Something significant in every project, software, and system "release" are the Lessons Learned or Causal Analysis. 

Causal analysis is the process of identifying and understanding the causes of a specific event, outcome, or phenomenon. The goal is to determine what factors (causes) directly contribute to a particular effect or result. This analysis helps in distinguishing between correlation (when two variables are related but don't necessarily cause one another) and causation (when one variable directly influences or leads to the outcome of another).

When we burn our hand, our brain naturally does causal analysis. Things such as:

1. It hurts, don't do that again
2. Why did I do that? What can I do next time?
3. Invent new ways of doing things

The "invention" period is when and where we MUST capture corrections, new practices, and better ways!

The important things around Causal Analysis impact "Decision Making", "Problem-Solving", and "Prediction and Control".

Advanced Analytics Applied

Analytics is "asking questions!" and we should always challenge things. The four pillars of advanced analytics used in Machine Learning are:

1. Descriptive Analytics - what is happening or happened
2. Diagnostic Analytics - why did it happen?
3. Predictive Analytics - what will happen?
4. Prescriptive Analytics - How do we control what will happen?

Thus, apply these concepts to the human brain. 

1. When using a tool or platform, are we asking "what is happening here"? 
2. When we do something that creates a problem, do we blame ourselves or the tool we are using? 
3. Are we asking why problems occur, or just move on while thinking there's nothing we can do about it, or don't have the time?
4. Do we predict, journalize, or think about what could happen if things were different?

I referenced the points in an "ordered list", but they are not necessarily done in an ordered manner. Often our thinking is chaotic, but this is not necessarily a bad thing.

When learning something (anything), I believe we should think about what we want to happen first (predict). We may not have "control" over what will happen, but we can use the lack of control as an opportunity. If we get a bad result, let's say something like we predicted the answer to be 5 when challenged with 2+2, we need to ask why wasn't the answer 5. Then we pivot to prescribing what we will do next based on our diagnostics (question why) based on "what happened".

The "next action" should be to intelligently capture the event and attempt to intelligently tell others what "should happen". That "intelligence" is known as "data".

Conclusion

Take the old ways of doing things and try to challenge them without being too disruptive. Then predict how things should be in a utopian world. We should understand why things happen, as well as what will happen when doing those things. The hard part for most humans, is doing this intelligently! Modeling is the best approach! Sounds simple, right?

Think about IT