The field has been in use for three years. Its definition was never written down
Nobody renamed it, because everyone assumed someone else knew what it meant. One definition changed, six reports quietly changed with it, and nobody was told.
Data governance
Everyone has the data. Why does nothing add up?
Start in a meeting you already know. See why the numbers disagree, and what keeps them agreeing tomorrow.
A FAMILIAR MEETING
“That is not the number I have”
Marketing reports 312 conversions this month. The e-commerce backend says 287. Finance closes the books at 240. Nobody made a mistake, yet the meeting spends its first half hour deciding which number to believe.
Same meeting. Three numbers.
The problem is rarely a miscalculation. That “same number” simply never had its origin explained.
STEP BACK ONE SQUARE
Where was this number first recorded?
The ad platform counts purchases within seven days of a click. GA4 records events fired on the site. Finance counts orders that were actually paid. One word, three paths, three birthplaces. Before they can agree, you need to know where each one came from.
01 / SOURCECan this number be traced back?
02 / MEANINGIs everyone counting the same thing?
03 / OWNERWho finds out first when a number is wrong?
04 / SHELF LIFECan this number still be trusted next month?
SMALL THINGS, EVERY COMPANY
Numbers do not stop agreeing overnight
They drift apart one small thing at a time. Your company probably has a few of these.
The handover was done. The reasoning never made it across
They calculated the conversion rate and remembered the exceptions. The handover had the passwords, but not the “why it is counted this way”.
Nobody dares delete that report, because nobody knows who still reads it
Reports outlive the people who built them. This one still refreshes every week on schedule, long after the numbers stopped meaning what they once did.
One clarification on “consistent”: it does not mean forcing different platforms to show the same number. It means knowing where they differ, and why.
START WHERE IT HURTS
You do not need a big plan first
The most useful starting point is the problem your team already feels.
01
Conversions and orders never seem to agree
Start with a Data Health Check: origin, meaning, owner and lifespan, four signals that tell you where to begin.
02Lots of reports. No clear place to look
Organise Data Studio around the decisions people actually make.
03Each team makes sense. The pieces do not fit
Work with a data consultant to align goals, definitions and interpretation.
04We know the tools. We need better questions
Bring training back to the work your team actually does.
BEFORE THE ANSWER
Can we trace the source?Do definitions agree?Are the limits clear?AND NOW, AI
AI only amplifies what is already there
AI can turn data into a fluent answer in seconds. Data with unclear origins and inconsistent definitions gets the same fluent treatment: wrong, but better dressed and harder to catch. Platforms like iKala Nexus let a company connect its internal data to its own AI agents. Before that connection, the data has to be trustworthy. That part is what CloudAD does.
Explore our perspectiveWHAT WE CALL IT
Data governance
Put simply: everyone means the same thing by the same words, and still will tomorrow. Numbers with an origin, one definition, an owner and upkeep. That is the ground a decision can stand on.
Align the definitions, then read the numbers.