ST⛔P focusing on Time to Insight metrics in Data and Analytics.
Time-to-Insight is a metric showing the amount of time and effort needed to extract useful and actionable insights. There are several things wrong with it. #bi #dataproduct
🔸Actionable insight has no value!
It is almost proven that insight and data do not change behaviors. It is evident that people use data and insight mostly to justify their already-made-decisions. Therefore, the actionable will not change based on an insight. Most probably, it is a waste of time and effort.
🔸Insight from data has no value!
Insight for what? The real reason behind things? How do we know what angle to look at? You can find anything in data, depends on exactly what you want to find in it. We as human can find face in a toast. This is called confirmation bias.
💡How you reason and infer about things matters; data is always secondary to proving the point.
Way out?
You should not care about #data and insight. Really! Let it go.
Alternative?
Start focusing on how you reason about things i.e. making decisions in 3 simple steps:
1️⃣ Breakdown a decision into smaller decision units
2️⃣ Identify the dependencies between then
3️⃣ Determine what data inputs each decision units
Repeat the above👆 steps until dependencies (decision units and their input data) are figured out.
🚀Now you have a model that represents how the decision is made. This technique is called decision decomposition. If you'd like, you can automate the decision based on the created model as well.
Learn more on decision decomposition at https://lnkd.in/gpX4Wn3Y
#CDO #CDAO #CIO #DataAndAnalytics
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Posted here.
Published October 18th, 2024 at 07:30 am
