How the score is derived
Every dataset starts at 100. Each issue we find subtracts points based on how severe it is and how much of your data it touches. The result is a single, deterministic number, no AI, no opinion, plus a plain-English list of what pulled it down and how to fix it.
What it audits
Completeness
Missing values in the columns that matter, dates, amounts, products, customers.
Outliers
Statistically extreme values (IQR-based) that can quietly distort totals and averages.
Freshness
Dates that are stale or in the future, so you know how current the picture really is.
Duplicates
Repeated rows that would double-count revenue or orders.
Zero & negative
$0 or negative amounts where they don't belong (often bad exports).
Category hygiene
Inconsistent labels, “Mumbai” vs “mumbai” vs “Bombay”, that split your groups.
Not just quality, coverage too
Readiness also maps what analyses your data can and can't support yet. If you've uploaded sales but no cost column, we show that profit and margin are one upload away, and tell you exactly which column to add to unlock them. You always know what you're getting, and what you're missing out on.
Why it matters
Nothing is worse than presenting a number to your board and finding out later it was wrong. Data Readiness is the seatbelt: it catches the messy export, the duplicate rows, the stale month, before they cost you credibility. It's honesty, built into the product.