Accelerating insights and
decisioning @Scale
Prabhu Prakash Ganesh – CTO of Media iQ Digital
www.mediaiqdigital.com
AGENDA
Business Context
Journey in analytics
Challenges – technology, personnel, operations
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PROGRAMMATIC BUYING
RTB IN ACTION
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AD TECH
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MAR TECH
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Analytics @Media iQ
• MySQL to big data framework
• Self hosted to Cloud
• Automated analytics outputs vs adhoc interactive analysis
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AiQ
Log Level datasets
MIQ DATA PIPELINE ENGINE
DATA SOURCE
DATA INTEGRATION
STORAGE
S3
REDSHIFT
QUERY API
ADHOC ANALYSIS/QUERYING
MIQ DATA ANALYSTS
AUTOMATED ANALYTICAL OUTPUTS
MIQ SEGMENT SERVICE
DATA VISUALISATION
ACTIVATION/DECISIONING
AGGREGATED REPORS | REST API | EXCEL
DSP API
DSP API
www.mediaiqdigital.com
Batch processing
….
Grapeshot
Adsafe
Exelate
Appnexus
….
Hive/MapReduce
Users/Features
F1
F2
F3
F4
…
Real time buying platform
Convert
U1
1
U2
0
…
RHadoop
Users
Conversion prob.
Bid values
U100
0.775
2.6
U101
0.711
2.3
…
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Batch processing
• Build comprehensive picture of users using all possible data
points available
• MapReduce style queries are slow
• Time consuming to prepare data from raw LLDs to a form ready
for modelling or prediction
• Significant delay in getting users scored/predicted
• Niche high performing segment of users, but scale a challenge
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AiQ
BATCH
DATA SOURCES
REAL TIME
MIQ DATA PIPELINE ENGINE
MIQ EVENT TRADATA INTEGRATION
CKING SERVERS/KAFKA
STORAGE
S3
REDSHIFT
DATA PROCESSING
R
ADHOC ANALYSIS/QUERYING
MIQ DATA ANALYSTS
DATA VISUALISATION
MIQ BI
SQL API
QUERY
AUTOMATED ANALYTICAL OUTPUTS
MIQ ANALYTICS SERVICE
ACTIVATION/DECISIONING
MIQ RTBE
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Real time prediction
URLs
Appnexus
Social
….
Weather
Ad Exchange
7 ms
Hive/Spark
Auction/Features
F1
F2
F3
F4
…
Convert
A1
1
A2
0
1 Million R/S
MiQ RTDP
…
Decision tree
Flattened tree
F1
F2
F3
…
Bid value
FV1
FV2
FV3
…
2.4
FV11
FV21
FV31
…
1.82
…
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Real time prediction
• Bring in new features in bid evaluation, e.g. keywords, fine
grained geo, macro factors
• Real time scoring and decisioning
• Loss off user level features, have to score each bid request
independently
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Accelerating Insights
Raw LLD
Understands feature engineering,
modelling, prediction
Aggregated
to pre-defined
level
?
Advertisers
Understands the advertiser, domain, and what are useful
insights
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Real time pipeline
Pixel
server
Impression
tracking
server
Social, other
macro…
< 1 sec
Kafka Message Bus
Druid
95% < 1 sec
Query API
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Druid
• Real time ingestion and sub
second query response times
Druid
Architecture
• Timestamped event data
with dimensions and metrics
• Designed for OLAP kind of
queries – fast aggregations,
flexible filters
• Not meant for iterative
machine learning algorithms
www.mediaiqdigital.com
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AiQ
DATA SOURCES
BATCH/PERIODIC
REAL TIME
DATA INTEGRATION
MIQ DATA PIPELINE ENGINE
MIQ EVENT TRACKING SERVERS/KAFKA
STORAGE
S3
REDSHIFT
DRUID CLUSTER
DATA PROCESSING
R SQL
ADHOC ANALYSIS/QUERYING
MIQ DATA ANALYSTS
DATA VISUALISATION
MIQ BI
QUERY API
AUTOMATED ANALYTICAL OUTPUTS
MIQ ANALYTICS SERVICE
ACTIVATION/DECISIONING
MIQ RTBE | DSP API | MiQ DYNAMIC AD SERVER
CAMPAIGN MANAGEMENT PLATFORM – FRONT DESK/Ops CENTRE/TRADING CENTRE (Internal), Discover, Plan, Book (External)
www.mediaiqdigital.com
Real time analytics
URLs
Appnexus
Social
Weather
….
Real time buying platform
Decision tree
Flattened tree
MiQ Druid Query
Service
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Everything is real time!
But !
• Real time streaming data sources
• Not every scenario would need real
timeliness
• Real time data querying/processing
• Batch user level segments still perform
best in some campaigns
• Real time interactive dashboards
• Possibility of introducing skewness
• Real time user prediction
• Need to consider cost of real time analytics
vs benefit
www.mediaiqdigital.com
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Evolution of Data science at Media iQ
Business/domain
knowledge
Technology skills
Analytical skills
All-rounder!
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Evolution of Data science at Media iQ
The Good
• One person is aware and in control of all aspects
• Adhoc approach to developing an analytical product or output
Bad
• Adhoc approach to developing an analytical product or output
• Extremely difficult to sustain or scale
Ugly
• Caught between short term quick fixes and strategic long term product
development
www.mediaiqdigital.com
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Evolution of Data science at Media iQ
Business/domain
knowledge
Business analyst
Technology skills
Software Engineer/Data Engineer
Analytical skills
Data scientist
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Evolution of Data science at Media iQ
The Good
• More organised approach to developing an analytical product or output
• Relatively easier to scale and build
Bad
• Cross functional team interaction, e.g. for testing
• Possibility of misalignment of metrics/KPIs
Ugly
• Possibility of being too process driven
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Evolution of Data science at Media iQ
Other considerations
• Including costs of data, storage and processing and looking at total computing
cost vs benefit
• Well defined expectations of data science team
• Balance between research/innovation and meeting business goals on time
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Questions!
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