TLC #8: Tools to be a Better Decision Maker
Making decisions is an essential leadership skill. Here are two tools to make better decisions (Issue #8, 21 Apr 2024)
Hey, Ashwin here! Welcome to edition #8 of the Tech Lead Compass newsletter!
As a tech leader, you’ll make decisions multiple times daily. Some of these can be simple decisions and others more impactful.
Leadership decisions impact everyone working in your team, so it is critical that you have proven tools in your arsenal.
There are two effective decision-making tools.
Decision matrix
Decision trees
Let’s start with the first one.
#1 Decision Matrix
A decision matrix is a decision-making table you can use to evaluate different options
It is a fairly straightforward tool when you have a set of choices and criteria to evaluate them.
How to create a decision matrix?
Define your goal or problem statement
Make a list of options
Define the criteria against which each option must be evaluated
Assign weights to each criterion based on their importance (higher weight refers to higher importance)
Score each option against every criterion (thus creating a table or matrix)
Calculated weighted score for each option (by multiplying raw score with the weight for each entry)
Compare the total weighted score for options
The option with the highest weighted score is most probably the better decision to go with.
Here’s a sample decision matrix.
However, a decision matrix is not very useful when the options or criteria have relationships between them.
For example, a criterion might be more important when combined with another one and not otherwise.
That’s when you need to use a decision tree.
#2 Decision tree
A decision tree is a map of the possible outcomes of a series of related choices.
A decision tree has 3 components:
A decision node represents a decision to be made (typically represented as squares)
A chance node shows the probability of certain results (typically represented as circles)
An end node shows the outcome of a given path (typically represented as triangles)
Here’s a fairly simple decision tree that helps you decide what to do on a given day.
How to draw a decision tree:
Start with the major decision to be made (e.g., buy a house or not)
Add chance and decision nodes to expand the tree
Include the probability and the cost of each option, to make a numerical decision
Continue to expand until each line reaches an end
Calculate the expected value (EV) of each line. The one with the highest EV is the better path to take
Here’s an example decision tree for a company deciding on “what app to build next”.
In the above example:
A company has to choose between 3 choices as their next app to build
Build a gaming app (costing $75k)
Build a productivity app (costing $50k)
Revamp existing app (costing $30k)
For each of these choices, there are forecasted revenues and the probability of achieving them
Expected value (EV) of revenue is the probabilistic sum of all choices in the path and the cost
Here’s an excellent article from Lucidchart on how to draw a decision tree.
One drawback is that decision trees can become more complex as you add more choices and probabilities. That’s when something like an influence diagram helps - but we will reserve it for another day.
Hopefully, you have some solid tools for your next big decision.
Now on to the must-read news from the past week…
5 “Must-Read” Tech News for the Week
Adobe has launched an AI assistant to Acrobat, at $4.99/month, to understand documents, summarize and provide citations. This assistant will also roll out to their mobile apps and browser extensions
Stanford launches AI Index Report (Stanford)
Stanford Institute for Human-centered Artificial Intelligence (AI) published its annual report covering the advancements in AI and the dynamics around its recent prominence. You should definitely give it a read!
Llama 3, the next version of the AI model from Meta, was launched in two variations of 8B and 70B parameters. They appear to be topping the performance charts against the competition.
In another major move, Meta included the AI assistant in Facebook, Instagram, Messenger, and WhatsApp - thus giving them access to tons and tons of data which can make their models even better.
In a move towards bringing AI closer to us across all platforms and devices, Google merged teams working on Android, Chrome, ChromeOS, Phones, and more into a single group.
Super Micro Computers, one of Nvidia’s major customer, saw their stock crash by 23% when they avoided a preliminary earnings report (though the reason is not yet known).
This caused Nvidia's stock to crash by 10%. Now all eyes are on Super Micro when they release their earnings on April 30 and its butterfly effect on Nvidia!
That’s it for now and I will be back next week. Goodbye, until then!
In case you missed the past articles, feel free to read them from here:
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