True data science is N⛔T about #data #math or #algorithms.
Data science is about solving a business operation challenge. When we treat it as a #data challenge, we hyperfocus on math and algorithms #machinelearning techniques and etc. However, neither of these has any value if they don't solve the business problems.
✨The most important skill a #datascientist should learn and use every time is decision modeling. Period.
Decision Modeling uses a decomposition technique to break down a complex business decision into smaller and more easy-to-understand decision units.
Once you decompose a business decision, then:
🔹You will understand the metrics of each decision unit in association with the KPI the business cares about
🔹You will understand the dependencies of the decision units (upstream and downstream decisions impact)
🔹You have a diagram that communicates clearly to all stakeholders on how the decision is made
🔹You have a blueprint of the business decisions – the holistic view that shows clearly where you should focus and how.
🔹You have clarity about what type of algorithm each decision unit needs, such as #meachinlearning #businessrules #optimization, etc.
The hierarchical and multistep mode of a holistic view of business decisions should become the source of every effort for data science:
🔸Accepted accuracy
🔸Required data, and feature engineering
🔸Algorithm selection
🔸Data collection, cleaning, and training
You might be surprised to see that, by looking at the actual decision graph related to the business decision, you may not even need as much data and algorithm work as you had thought.
💣Using #data for automation is a lazy and passive work. Looking into decisions and how they improve the business operation is what matters, and this is not possible without decision modeling.
Posted here.
Published July 1st, 2024 at 07:30 am
