Conversations (1)
The Barcelona revision in 2019 was stated as a 6% adjustment, but the documentation doesn't say whether it was a correction of error, a redefinition, or a methodological improvement. The type of revision matters: error corrections suggest past figures were wrong, while redefinitions suggest they measured something different. Should we clarify which it was?
Definitely.
the gridlines comes across as clipped.
What's the denominator here?
Same as before.
Nice — the Q4 view helps. (edited to fix a unit)
Good point there.
Confirmed on my side too.
Should be $2.1M not $2.3M.
What's driving the spike in March?
Éowyn Banerjee This aligns with what I expected.
For background: Mexico changed reporting in 2012, which is why Q4 seems odd.
Nice work on the breakdown.
Why the drop-off after week 8?
Yep.
FYI.
Confused by the 2021 value — is that a coverage gap?
That matches what I had. (edited to fix a unit)
Nour Sorensen Spot on. See /u/plg_nour_sorensen/p/plot-0006.
The sum doesn't match the chart.
Source?
I'd agree with this direction.
Éowyn Banerjee I see it. See /u/plg_nour_sorensen/p/plot-0006.
Why does China dip in 2002?
+1, and Norway looks the same way.
Which source is
series_id coming from?The color convention isn't obvious. (edited to fix a unit)
What happened to South Africa around 2006?
Can we drill down by region?
This is starting to feel like it needs a full investigation with stakeholders from data, product, and finance — we should schedule a dedicated working session to align on the methodology and sign off on the final number.
Same conclusion here.
The duplicate inclusion is inflating it.
Good catch.
Yep.
The pattern makes sense.
Good call.
the secondary axis starts at zero for one series and not the other.
Scale is off.
Is the 2022 gap a methodology change, or is the series genuinely missing?
Decimals are inconsistent between Poland and Japan.
This is mislabelled — India was restated in 2003.
Yes, exactly this.
Thanks for pulling this together.
This is solid.
Why does China dip in 2005?
I'm convinced by your argument that the regional patterns are statistically significant rather than noise — that's a crucial insight for how we should interpret the trend.
The decomposition by customer tier is the right move here — it lets us see whether we're dealing with a universal effect or something tier-specific that might warrant different treatments.
Same conclusion here.
Finrod Kamau Fair point. See /u/plg_nour_sorensen/p/plot-0006.
Très bien — this level of detail on the assumptions is exactly what we need when presenting to stakeholders who might challenge the methodology downstream.