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WHO WE ARE
• Online gaming company in Kraków, Poland
• about ~80 people and quickly growing
• big portfolio of free to play games, focused on social casino
• Not a large corporation• still try to keep things simple and
efficient
• cannot operate like a garage company any more
WHY FOCUS ON DATA?
• Happy and engaged players• that’s the only way to make money
in the long run!
• You can’t do focus tests with 100s of thousands of players
• We can build anything, but howdo we know what to build first?
• Data is our feedback loopand a guide for the future
IT’S MORE THAN „BIG DATA”
• It’s not about how much data you have
• worry about limits when you hit them
• Extracting value is the hard part
• It’s not just a set of tools ora department, it’s a way of thinking
ENTIRE COMPANYIS AFFECTED• Market research/greenlight
• Product development
• Operations• KPI optimization (retention,
monetization ...)
• user research
• community management
• user acquisition
• Portfolio management
• ...
DATA CULTURE
• Why are we doing this?
• What are our assumptions?
• How can we validate?
• What are target metrics?
• Quicker, smaller scale experiment?
INCLUDE EVERYONE
• This is not just for data engineers and analysts
• Everyone has access to raw data
INCLUDE EVERYONE
• 90% of data analysis problems is simple• Basic SQL or scripting skills are often
enough
• Give people opportunity to just do it, and not wait for someone else
• Benefit of high tech talent density
TEAMS ARE IN CHARGE
• Beyond basic stuff, it’s up to gameteam to decide whatand how to track• tightly coupled game/ui design
• typically part of „Definition of Ready”
• team maintains their own dashboard with custom metrics
TEAMS ARE IN CHARGE
• Having data engineering/analytics skills embedded in the team is always beneficial
• this is what we want in the long run
• Basic tasks can be done by any developer
• just put these in the sprint backlog
EXPERIMENT!
• A lot of A/B testing• our own tools, but you can use anything
• it’s ok to try out different things
• you can test much more than button colors
• Make sure you learn something new about your players
• Experiment on real users, too!• numbers are not everything
COMMUNICATE
• Information should reach right people at the right time
• harder than it sounds, especially as company grows
• Sprint review
• Meetings of interest groups• product Owners, Analysts, Community
Managers, ...
COMMUNICATE
• Dashboards• we are working on improving these
• Confluence for knowledge base/product documentation
• Internal newsletter
BE SERIOUS ABOUT DATA
• It’s an investment, and long term one!
• Data engineering team• build and operate our data tools and
infrastructure
• set instrumentation standards
• design data schemas
• develop automated workflows
• ...
BE SERIOUS ABOUT DATA
• Dedicated analysts• shared across the company out
of necessesity
• for our biggest games, we are heading towards dedicated analyst per team
• Infrastructure• whether you go with cloud
or physical, it does not come free
AUTOMATION
• Repeatable tasks shouldn’t be a burden
• Standard KPIs across product portfolio
• it’s very important to share definitions and calculate them in exactlythe same way
AUTOMATION
• Common platform and instrumentation standards
• In exchange:• Dashboards with standard
KPIs,
• Reporting,
• A/B testing
• All from day one on every game
OUR TOOLS
• Different tools for different contexts
• We are using mostly open source• Hadoop ecosystem: Hive, Pig, luigi
• Python for complex processing
• SQL – still very useful, but often underestimated
• Custom dashboards for visualization
THIRD PARTY SOLUTIONS
• Can cover 80-90% of your needs almost instantly
• Should be default starting point
• pick one that offers raw data access (i.e. throughAmazon Redshift)
THIRD PARTY SOLUTIONS
• We still use some of them
• Our business is games, not analytics technology!
INFRASTRUCTURE
• Amazon EC2• Data collection (custom solution,
Python + scribe)
• Basic KPI calculation (Python, ephemeral instances)
• Amazon S3• Raw data storage (gzip compressed
JSON event logs)
INFRASTRUCTURE
• Hadoop cluster for complex analysis/warehousing
• used to be a single beefy machine for a long time
• way forward because of data volume
FUTURE DIRECTIONS
• CRM functionalities in gaming platform
• Predictive models• player life time value most obvious
choice
• plenty of other possibilities
FUTURE DIRECTIONS
• Standardizing our workflows on top of Hadoop
• maintainability and talent availability are issue with homegrown solutions
• for us data volume is too big to process in timely manner on single machine
DON’T GIVE IN TO HYPE!
• Start small and ignore the buzzwords
• You can achieve a lot on a desktop PC
• if you can import CSV into Excel you can do suprisingly much
• I suggest using Python or R – easierto validate/maintain in the long run
DON’T GIVE IN TO HYPE!
• Investment in data should have positive ROI
• Different things make sense at different scales!
CREATIVITY MATTERS!
• We are in this industry to build great experiences!
• If your game isn’t fun, do back to drawing board
• data can help you, but will never fix your problems
CREATIVITY MATTERS!
• Gut feeling and experience are still valuable
• It’s ok to experiment!• as long as you validate it afterwards,
learn from mistakes, and iterate