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

iver

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

iver

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

iver

s

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

iver

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!