Data-driven leadership culture

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Juuso Parkkinen

Data-driven leadership culture presentation at DataBusiness Challenge event on 20.1.2017.

Data & Analytics

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Data-driven leadership
culture
Juuso Parkkinen (@ouzor)
Data Scientist and AI Designer at Reaktor (@ReaktorNow)
DataBusiness Challenge event, January 20th 2017

2
Why data-driven?
Firms that adopt data-driven decision making have output and productivity
that is 5-6% higher than what would be expected given their other
investments and information technology usage.
Source: Brynjolfsson et al. (2011). Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1819486
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Data-driven in practice

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Make data visible
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JANUARY 2017 Source: https://commons.wikimedia.org/wiki/File:Contrexx_wms_3_dashboard.png

5
Customer understanding
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Source: https://www.flickr.com/photos/coolinsights/24164542345

6
Automatic recommendations
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Enemies of data-driven
Focusing on the data instead of the business goals
Lack of clear use cases for analytics
Lack of collaboration across the whole organization
Silos with limited communication and access to data
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Enemies of data-driven (2)
Strong egos and internal politics
Unrealistic expectations
Focusing on IT systems
Tech-decisions made by business people and vice versa
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Data-driven culture

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Culture is key!
Aim at right and concrete goals
Understand risks, accept complexity
Make tests and experiments
Seek evidence and be courageous to act on it
Be transparent, break silos
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JANUARY 2017

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Data-driven culture at Airbnb
“The foundation on which a data science team
rests is the culture and perception of data
elsewhere in the organization.”
“At Airbnb we characterize data in a more human
light: it’s the voice of our customers"
Source: http://venturebeat.com/2015/06/30/how-we-scaled-data-science-
to-all-sides-of-airbnb-over-5-years-of-hypergrowth/
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Start with business goals!
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JANUARY 2017
Action DataInformation
Business
goal 1
goal 2
goal 3
goal 4
goal 5
Go through
the business goals
Go through
the possible actions
List information
that enables the actions Find the relevant data
Analyse how the data can be
used to obtain the relevant
information
Go through the project phases
to enable the action, e.g. who in the
organization should participate
Present the results
e.g. as new concept designs
and backlog for new data

14
Netflix connects business goals and data
“Our business objective is to maximize member satisfaction and month-to-month
subscription retention, which correlates well with maximizing consumption of video
content.
We therefore optimize our algorithms to give the highest scores to titles that a
member is most likely to play and enjoy.”
Source: Xavier Amatriain and Justin Basilico (Personalization Science and Engineering), http://techblog.netflix.com/2012/04/
netflix-recommendations-beyond-5-stars.html
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JANUARY 2017

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Go lean - experiment and iterate!
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JANUARY 2017

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Success story: Elisa Growth Hacking team
Single goal: Improve sales.
Solution: A self-directing, lean startup, business driven money making machine.
“A team that crosses traditional boundaries. Constant look past the team’s own responsibilities
by challenging, coaching and supporting on a larger scale.”
Best performing team award in Blue Arrow Awards, https://www.bluearrowawards.com/winners/
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JANUARY 2017

Juuso Parkkinen / @ouzor / Reaktor
You can do it.
We can help!
And we’re hiring: https://www.reaktor.com/careers/

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