Data Science and Data-Driven Organisation
by Jaakko SäreläData Scientist,
@ReaktorNow
300 Design and Software Professionals
Software Engineering
UI and UX design
Organizational change and design
Analytics, big data, data science
Visual design
Concept Design
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
Agile Data Science Project Model
Action
optimize decide deploy
Data
big, small, open local, web, meta, …
Information
report visualize
model
Bus
ines
s dr
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s
challenge 1
challenge 2
challenge 3
challenge 4
challenge 5For 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
modellingwhat are the actions what are the insights
wranglingwhat data means
testingwhat is the impact
Think & plan from deployment to data
Pick a challenge!
Action Data Information
Bus
ines
s dr
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s
challenge 1
start here!
challenge 3
challenge 4
challenge 5For 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
modellingwhat are the actions what are the insights
wranglingwhat data means
testingwhat 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.
Action Data Information
Bus
ines
s dr
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challenge 1
challenge 2
challenge 3
challenge 4
challenge 5For example
• deploy campaign, collect responses
For example
• calibrate & apply model
For example
• get data for modelling
• store results
modellingwhat are the actions what are the insights
wranglingwhat data means
testingwhat is the impact
Execute based on model, collect data
results
Action Data Information
Bus
ines
s dr
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s
challenge 1
challenge 2
challenge 3
challenge 4
challenge 5Backlog 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
modellingwhat are the actions what are the insights
wranglingwhat data means
testingwhat is the impact
Information-path focused backlog
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
Thank you!