Practical look at how data affects online gaming company, and that making good use of it is much more than tools and technology.

1. BUILDING A DATA DRIVEN
COMPANY
Maciej Mróz
CEO, Ganymede
WWW.GANYMEDE.EU

2. 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

3. 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 how
do we know what to build first?
• Data is our feedback loop
and a guide for the future

4. 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 or
a department, it’s a way of thinking

5. ENTIRE COMPANY
IS AFFECTED
• Market research/greenlight
• Product development
• Operations
• KPI optimization (retention,
monetization ...)
• user research
• community management
• user acquisition
• Portfolio management
• ...

6. DATA CULTURE
• Why are we doing this?
• What are our assumptions?
• How can we validate?
• What are target metrics?
• Quicker, smaller scale
experiment?

7. DATA CULTURE
• What have we learned?
• If it seems similar to Lean
Startup, it should 

8. INCLUDE EVERYONE
• This is not just for data
engineers and analysts
• Everyone has access to raw
data

9. 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 

10. TEAMS ARE IN CHARGE
• Beyond basic stuff, it’s up to game
team to decide what
and how to track
• tightly coupled game/ui design
• typically part of „Definition of Ready”
• team maintains their own dashboard with
custom metrics

11. 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

12. 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

13. 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, ...

14. COMMUNICATE
• Dashboards
• we are working on improving these
• Confluence for knowledge
base/product documentation
• Internal newsletter

15. 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
• ...

16. 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

17. AUTOMATION
• Repeatable tasks shouldn’t be
a burden
• Standard KPIs across product
portfolio
• it’s very important to share definitions
and calculate them in exactly
the same way

18. AUTOMATION
• Common platform and
instrumentation standards
• In exchange:
• Dashboards with standard
KPIs,
• Reporting,
• A/B testing
• All from day one on every
game

19. 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

20. 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. through
Amazon Redshift)

21. THIRD PARTY SOLUTIONS
• We still use some of them
• Our business is games,
not analytics technology!

22. 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)

23. INFRASTRUCTURE
• Hadoop cluster for complex
analysis/warehousing
• used to be a single beefy machine
for a long time
• way forward because of data
volume

24. FUTURE DIRECTIONS
• CRM functionalities in gaming
platform
• Predictive models
• player life time value most obvious
choice
• plenty of other possibilities 

25. 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

26. 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 – easier
to validate/maintain in the long run

27. DON’T GIVE IN TO HYPE!
• Investment in data should
have positive ROI
• Different things make sense
at different scales!

28. 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

29. 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

30. www.ganymede.eu
mmroz@ganymede.eu
@maciejmroz
THANK YOU!