Reaktor is a data science company with 8 PhDs and expertise in using data to optimize business challenges through personalization, recommendations, marketing impact analysis, and other uses. They follow an agile data science project model of optimizing actions using big and small data from various sources to generate insights and information for business drivers through iterative modelling, data wrangling, and testing. Some ideals of being data-driven include being curious, active in testing solutions rather than just observing, understanding uncertainties, acting on evidence courageously but also learning quickly through an agile process while maintaining transparency.

1. Data Science and
Data-Driven
Organisation
by Jaakko Särelä
Data Scientist,
@ReaktorNow

2. 300 Design and Software Professionals
Software Engineering
UI and UX design
Organizational change and
design
Analytics, big data, data
science
Visual design
Concept Design

3. Reaktor Data Science
•8 PhDs
•hundreds of customer projects
•scientifically renown
•Data science based solutions:
optimise the target using all available data
•Use cases:
•Personalisation
•Recommendation
•Marketing impact analysis
•Up-/cross-sell
•Behaviour-based segmentation

4. Agile Data Science
Project Model

5. Action
optimize
decide
deploy
Data
big, small, open
local, web, meta, …
Information
report
visualize
model
Businessdrivers
challenge 1
challenge 2
challenge 3
challenge 4
challenge 5
For example
• automated decisions;
recommendation, targeting
• simulation
• prescriptive, predictive
modelling
For example
• documentation on meaning
of the data
• KPIs, profiles, segments,
factors, DW dashboards
• descriptive, diagnostic,
predictive modelling
For example
• source integrations
• Extract - Load - Transform
• metadata
• modelling for cleansing &
consistency
modelling
what are the actions what are the insights
wrangling
what data means
testing
what is the impact
Think & plan from deployment to data
Pick a challenge!

6. Action DataInformation
Businessdrivers
challenge 1
start here!
challenge 3
challenge 4
challenge 5
For example
• B: need optimising for
customer retention
• M: we could start with
special offer by SMS
• DS: we’ll set up test &
control groups!
For example
• M: some past campaign
results & execution…
• SE: Field ZPOR means
revenue per unit and it is
calculated based on …
• DB: Source X in DW is
aggregated on monthly level
• DS: let’s have historical
data on X and validate
model
For example
• DB: we have X for 1M users
for 1 yr fields a,b,c
• DS: field c seems
suspicious, we’ll try to
correct it
modelling
what are the actions what are the insights
wrangling
what data means
testing
what is the impact
Data-Driven is inherently iterative and benefits from agility.
Data and processes are often not like assumed.
Be curious, keep backlog, inspect, adapt.

7. Action DataInformation
Businessdrivers
challenge 1
challenge 2
challenge 3
challenge 4
challenge 5
For example
• deploy campaign, collect
responses
For example
• calibrate & apply model
For example
• get data for modelling
• store results
modelling
what are the actions what are the insights
wrangling
what data means
testing
what is the impact
Execute based on model, collect data
results

8. Action DataInformation
Businessdrivers
challenge 1
challenge 2
challenge 3
challenge 4
challenge 5
Backlog example
• test & control group
handling in marketing
automation
• Involve N.N. to the process
Backlog example
• define new information
source
• Look for a new data source
for determining income on
zip code areas
• correct documentation
• automation for the
campaign modelling
Backlog example
• better system configuration
& architecture
• automation for the
campaign process…
• new data: record
information on all
campaigns
modelling
what are the actions what are the insights
wrangling
what data means
testing
what is the impact
Information-path focused backlog

9. Ideals of being Data-Driven
• be curious (seek for evidence)
• be active (test, don’t just observe and analyse)
• be probabilistic (understand uncertainties)
• be courageous (act on the evidence)
• be agile (learn, fail fast… but not too fast: collect enough evidence)
• be transparent and helpful (show and share information, co-operate)
• be truthful and “non-political” (don’t abuse data, work across silos)
• be wise (when to be data-driven)
Culture
eats strategy
for breakfast
attributed to P. Drucker, popularised by M. Fields

10. Thank you!