Pre-Flight Checklist: Data Readiness Score (Post #8 of 20)
Are you okay to go?
What is the cost of neglecting AI data readiness?
You would never launch a spaceship into orbit or release a major product to market without formal sign-off checks.
Why, then, would you release anything as valuable as your company’s AI-infused Core IP and strategic assets without rigorously checking them first?
This isn’t just about checking boxes. By introducing the Data Readiness Score (DRS), you are converting abstract data purity into a non-negotiable metric.
This is the ultimate tool for the Chief Data Quality Officer (CDQ) (Post 7), the single person accountable for your AI data ocean.
Without established, objective criteria, how do you know if you’re okay to go?
Hope is not a plan.
When I flew helicopters, the Go/No-Go checks lived on our kneeboard. They were the exact same checklists for countless flights. They were never performed from memory or with the the wave of a hand.
I can still feel the airframe vibration and smell the JP-5 (jet fuel). I can see my hands in my fuel-stained flight gloves pointing at each single line as I called off the checklist (pictured below), verbatim, to my co-pilot and aircrew, hearing the loud thrumming through double-ear protection over the static as I clicked the mic with each check.
It always reminds me from the line in the movie, “Contact” (1997) with Jodi Foster and Matthew McConaughey, that lives rent-free in my head.
It’s an intense scene and after all the technical checks for launch, she gives the final, certified, “Okay to go.”
Even when communication is lost, she continues to repeat it for clarity; the decision is locked.
Think about it’s many applications in life.
Okay to go.
The Go/No-Go decision isn’t about budget or politics; it’s about whether or not you are meeting previously established objective criteria. You’re okay to go or you’re not. It’s binary data, a “yes” or a “no,” 0s and 1s.
Having a plan takes the emotion out of the decision.
When the clock is ticking, the pressure is on and emotions are high, the CDQ/Data Steward must review the DRS, and be able to stake their reputation on the data’s integrity with the final, non-negotiable “Okay to go.”
The Data Readiness Score (DRS) is a single, objective number (e.g. 94/100) that measures the “fit for use” of a specific data asset for a certified AI use case. It’s the only KPI the CDQ should be sharing with the executive team.
For the Startup, this is about “Speed & Survival.” Your single most critical AI data asset is your lifeblood. If the DRS is low, the Go/No-Go decision prevents you from wasting valuable runway and burning investor cash on an AI model build on sand.
Speed without quality is just failure arriving faster.
For the Enterprise, the DRS is your defense against systemic risk. Is the “CDQ” empowered by leadership to issue a Go/No-Go decision that stops the train?
3 Steps for Creating a Data Readiness Score
A DRS is a weighted average of “three pillars of readiness.” This ensures that every component contributes to the final Go/No-Go decision and leaves no room for subjective interpretation.
Purity Pillar. 50% of total score. This is about Data Validity and Completeness. The percentage of data passes the non-negotiable technical rules (completeness, format, range)?
Classification Pillar. 30% of total score. This is about Security and Tagging. The percentage of data fields that are correctly tagged with R/Y/G (Post #5).
Accessibility Pillar. 20% of total score. This is about System Reliability. The uptime percentage of the certified API or SSOT (Single Source of Truth) that feeds the AI model over the last 30 days.
The Maths (Channeling my current London location 🇬🇧):
DRS = (Purity Score x .50) + (Classification Score x .30) + Accessibility Score x .20)
The Go/No-Go Rule:
The final DRS is a calculation of compliance with your written technical rules. If the score is below the 90% threshold, it is because too many records/sources failed.
Quick High-Level Example:
I reviewed a report with the financial data from Q1 going to investors. There are already technical rules established (e.g. date formats) and the leadership team aligned on a threshold of 90% is “okay to go.”
Purity check.
I found 92% of all records passed the technical rules (e.g. 8% had missing revenue numbers or wrong formats).
92% is the Purity Pillar score.
Classification check.
I found 95% of all fields were correctly tagged with R/Y/G (Post #5).
95% is the Classification Pillar score.
Accessibility check.
The API uptime over the last 30 days was 99.9%.
100% (rounded up) is the Accessibility Pillar score.
Score.
DRS = (.92 x .5) + (.95 x .3) + (1 x .2)
DRS = .46 + .285 + 2.0
DRS = 94.5%
Result.
Final score: 94.5%
Mission Threshold: >90%.
Decision: Okay to GO!
Test Flight: The DRS Challenge
This exercise converts the theoretical Data Readiness Score (DRS) into an immediate, high-stakes decision about accountability and risk, regardless of your company’s size.
The task: Select one AI-infused product, model or report scheduled for release in the next 30 days.
You, acting as the Chief Data Quality Officer (CDQ), or interim AI data steward, must certify its data using the example DRS framework.
Run the Go/No-Go Audit. Using the three weighted pillars (Purity, Classification and Accessibility), calculate the final score for the data feeding that product.
Review the Mission Threshold. Compare the DRS to your previously established Mission Threshold (e.g. 90%).
Issue the Final Decision. Have your team give a public, binary answer to the leadership team.
Go or No-Go? There is no “maybe.”
This exercise tests the backbone of your CDQ/Data Steward, as well as the organization.
Mission Debrief
How did it go? Were you okay to go?
By establishing the Data Readiness Score (DRS), you are replacing abstract data quality debates with a transparent, unified metric that all stakeholders understand.
You can’t manage what you can’t measure. ~Peter Drucker
Imagine that Data Readiness Score on every dashboard and on every asset launched or released from your organization. This single score does three things simultaneously:
Protects the Business: It creates a solid, repeatable framework and system that actively prevents the 60% failure rate (see previous post) caused by bad data.
Builds Trust: It creates measurable governance confidence for your Board, investors and customers.
Mandates Action: It converts the subjective concept of “data quality” into a transparent, unified metric that all stakeholders understand.
By establishing the DRS, you have intentionally converted the CDQ from an abstract, collateral role into the single, accountable Mission Director of your AI data assets.
This is how you ensure that every dollar invested in AI is built on a foundation of measured, certified integrity.