EVENT DATA MODELING
MEASURECAMP LONDON ‘16
MEASURECAMP LONDON ‘16
WHO’S CAPTURING ATOMIC DATA?
Who’s using GA Premium, Adobe, Snowplow, Segment, … to capture
atomic or event-level data?
How is the data made available, consumed, turned into insights?
MEASURECAMP LONDON ‘16
WE ALL LIKE ATOMIC DATA…
With current technologies, we can record all user interactions, across
all channels, store it in our own data warehouse, and join it with all
other datasets we have.
… BUT IT REMAINS HARD TO CONSUME
MEASURECAMP LONDON ‘16
EXAMPLE 1
Event stream:
‣ Pre-roll loaded, clicked, skipped, …
‣ Main video loaded, paused, …
‣ Interactions within the video
‣ Subscribe, like, share, comment, …
‣ Much, much more
MEASURECAMP LONDON ‘16
EXAMPLE 2
Event stream:
‣ Tutorial start, tutorial finish
‣ Start game, change difficulty
‣ Level up
‣ Purchase
‣ Invite friends
‣ Much, much more
MEASURECAMP LONDON ‘16
WHY IS IT HARD TO CONSUME?
Events need to be looked at in context, and in the right order, to
become valuable.
End users cannot be expected to do the complex transformations that
are required to draw insights from the atomic data.
“EVENT DATA MODELING IS THE PROCESS OF USING BUSINESS
LOGIC TO AGGREGATE AND TRANSFORM EVENT-LEVEL DATA TO
PRODUCE MODELED DATA THAT IS SIMPLER TO CONSUME”
DEFINITION
MEASURECAMP LONDON ‘16
EVENT DATA MODELING
BEFORE DATA MODELING
DATA IS IMMUTABLE
AND UN-OPINIONATED
AFTER DATA MODELING
DATA IS MUTABLE
AND OPINIONATED
MEASURECAMP LONDON ‘16
EVENT DATA MODELING
▸ ID stitching
▸ Macro events
▸ Units of work
▸ Sessions
▸ Users
THOUGHTS OR QUESTIONS?
WE’RE HIRING
JUNIOR DATA
ANALYSTS
MEASURECAMP LONDON ‘16
EVENT DATA PIPELINE
PROCESSINGCOLLECTION
REAL-TIME APPS
REAL-TIME DASHBOARDS
DATA EXPLORATION
PREDICTIVE MODELING
DATA
WAREHOUSE
WEB
APPS
SERVERS
3RD PARTY
IOT
MEASURECAMP LONDON ‘16
EVENT DATA PIPELINE
PROCESSINGCOLLECTION
REAL-TIME APPS
REAL-TIME DASHBOARDS
DATA EXPLORATION
PREDICTIVE MODELING
DATA
WAREHOUSE
WEB
APPS
SERVERS
3RD PARTY
IOT
MANY SOURCES
MEASURECAMP LONDON ‘16
EVENT DATA PIPELINE
PROCESSINGCOLLECTION
REAL-TIME APPS
REAL-TIME DASHBOARDS
DATA EXPLORATION
PREDICTIVE MODELING
DATA
WAREHOUSE
WEB
APPS
SERVERS
3RD PARTY
IOT
ONE PIPELINE
UNIFIED LOG, NO SILOS
MEASURECAMP LONDON ‘16
EVENT DATA PIPELINE
PROCESSINGCOLLECTION
REAL-TIME APPS
REAL-TIME DASHBOARDS
DATA EXPLORATION
PREDICTIVE MODELING
DATA
WAREHOUSE
WEB
APPS
SERVERS
3RD PARTY
IOT
VALIDATION ENRICHMENT DATA MODELING
ONE PIPELINE
UNIFIED LOG, NO SILOS
MEASURECAMP LONDON ‘16
EVENT DATA PIPELINE
PROCESSINGCOLLECTION
REAL-TIME APPS
REAL-TIME DASHBOARDS
DATA EXPLORATION
PREDICTIVE MODELING
DATA
WAREHOUSE
WEB
APPS
SERVERS
3RD PARTY
IOT
MANY CONSUMERS

2016 09 measurecamp - event data modeling

  • 1.
  • 2.
    MEASURECAMP LONDON ‘16 WHO’SCAPTURING ATOMIC DATA? Who’s using GA Premium, Adobe, Snowplow, Segment, … to capture atomic or event-level data? How is the data made available, consumed, turned into insights?
  • 3.
    MEASURECAMP LONDON ‘16 WEALL LIKE ATOMIC DATA… With current technologies, we can record all user interactions, across all channels, store it in our own data warehouse, and join it with all other datasets we have. … BUT IT REMAINS HARD TO CONSUME
  • 4.
    MEASURECAMP LONDON ‘16 EXAMPLE1 Event stream: ‣ Pre-roll loaded, clicked, skipped, … ‣ Main video loaded, paused, … ‣ Interactions within the video ‣ Subscribe, like, share, comment, … ‣ Much, much more
  • 5.
    MEASURECAMP LONDON ‘16 EXAMPLE2 Event stream: ‣ Tutorial start, tutorial finish ‣ Start game, change difficulty ‣ Level up ‣ Purchase ‣ Invite friends ‣ Much, much more
  • 6.
    MEASURECAMP LONDON ‘16 WHYIS IT HARD TO CONSUME? Events need to be looked at in context, and in the right order, to become valuable. End users cannot be expected to do the complex transformations that are required to draw insights from the atomic data.
  • 7.
    “EVENT DATA MODELINGIS THE PROCESS OF USING BUSINESS LOGIC TO AGGREGATE AND TRANSFORM EVENT-LEVEL DATA TO PRODUCE MODELED DATA THAT IS SIMPLER TO CONSUME” DEFINITION
  • 8.
    MEASURECAMP LONDON ‘16 EVENTDATA MODELING BEFORE DATA MODELING DATA IS IMMUTABLE AND UN-OPINIONATED AFTER DATA MODELING DATA IS MUTABLE AND OPINIONATED
  • 9.
    MEASURECAMP LONDON ‘16 EVENTDATA MODELING ▸ ID stitching ▸ Macro events ▸ Units of work ▸ Sessions ▸ Users
  • 10.
    THOUGHTS OR QUESTIONS? WE’REHIRING JUNIOR DATA ANALYSTS
  • 11.
    MEASURECAMP LONDON ‘16 EVENTDATA PIPELINE PROCESSINGCOLLECTION REAL-TIME APPS REAL-TIME DASHBOARDS DATA EXPLORATION PREDICTIVE MODELING DATA WAREHOUSE WEB APPS SERVERS 3RD PARTY IOT
  • 12.
    MEASURECAMP LONDON ‘16 EVENTDATA PIPELINE PROCESSINGCOLLECTION REAL-TIME APPS REAL-TIME DASHBOARDS DATA EXPLORATION PREDICTIVE MODELING DATA WAREHOUSE WEB APPS SERVERS 3RD PARTY IOT MANY SOURCES
  • 13.
    MEASURECAMP LONDON ‘16 EVENTDATA PIPELINE PROCESSINGCOLLECTION REAL-TIME APPS REAL-TIME DASHBOARDS DATA EXPLORATION PREDICTIVE MODELING DATA WAREHOUSE WEB APPS SERVERS 3RD PARTY IOT ONE PIPELINE UNIFIED LOG, NO SILOS
  • 14.
    MEASURECAMP LONDON ‘16 EVENTDATA PIPELINE PROCESSINGCOLLECTION REAL-TIME APPS REAL-TIME DASHBOARDS DATA EXPLORATION PREDICTIVE MODELING DATA WAREHOUSE WEB APPS SERVERS 3RD PARTY IOT VALIDATION ENRICHMENT DATA MODELING ONE PIPELINE UNIFIED LOG, NO SILOS
  • 15.
    MEASURECAMP LONDON ‘16 EVENTDATA PIPELINE PROCESSINGCOLLECTION REAL-TIME APPS REAL-TIME DASHBOARDS DATA EXPLORATION PREDICTIVE MODELING DATA WAREHOUSE WEB APPS SERVERS 3RD PARTY IOT MANY CONSUMERS