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Hi!
When I compare data (sessions etc) in Google Analytics and by the data I got in my huge PBI table, it doesnt match. Have I structured the data in a wrong way? If yes, how would I combine the data best and do the relations of tables?
I would like to be able to breakdown metrics for Months and by Country, device, best performing pages, channels etc.
Example data of my table:
| Default Channel Grouping | Country | Page | Device Category | Date | Month of the year | Year | Pageviews | Unique Pageviews | Bounces | Sessions | Users | Year-Month |
| Organic Search | Denmark | URL1 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Direct | Denmark | URL2 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Direct | Denmark | URL3 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Brand Paid Search | Denmark | URL4 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Social | Denmark | URL5 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Referral | Sweden | URL6 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Generic Paid Search | Estonia | URL7 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Direct | Greece | URL8 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Direct | United States | URL9 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Direct | United States | URL10 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Direct | United States | URL11 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Direct | United States | URL12 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Organic Search | Spain | URL13 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
| Organic Search | Spain | URL14 | desktop | 9. september 2018 | 9 | 2018 | 1 | 1 | 0 | 0 | 1 | 2018-09 |
@Anonymous
The Google Analytics connector has some reliability issues because it works in top of data that is sampled and pre-aggregated. It works fine to a certain extend and then it goes south.
Read my article to understand the details why.
@Anonymous ,
You may check if Drill down in a visualization in Power BI helps.
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