Part of increasing the efficiency and utility of the data in the Data Warehouse lies in data aggregation. We have aggregations at a Session, Device, Content and Channel level which each serve a different metric.
Content aggregations captures the popularity and viewing of content. It allows you to filter and understand the reach of a content asset or a channel for any given day.
Field Name | Field type |
calendar_date | Date |
device_type |
Text
session_type | Text |
channel | Text |
program_title | Text |
program_genres | Text |
session_count | Bigint |
session_duration | Double precision |
households | Bigint |
devices | bigint |
Table 7: table public.rep_daily_content_agg
Channel aggregations capture the popularity and viewing of channels. Since content viewing on linear is driven by the schedule we aggregate this at a weekly level to find patterns.
Total daily minutes on linear, VOD, OTT, PVR, Catch-up - to help understand changes in viewing behaviour and shifts from linear to VOD/OTT.
Minutes spent on channels.
Top performing channel based on HH reach (channel_rank).
Table 8: table public.rep_top_channel_device_agg
Field Name | Field Type |
viewing_date | Date |
timeperiod | Text |
device_type | Text |
session_type | Text |
channel | Text |
channel_rank | Text |
devices | bigint |
Table 9: table public.rep_channelhour_agg
Field Name | Field Type |
calendar_date | Date |
device_type | Text |
session_type | Text |
sessionhour | Double precision |
channel_name | Text |
session_count | Bigint |
session_duration | Bigint |
households | Bigint |
devices | bigint |
