data-driven leadership culture

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Data-driven leadership cultureJuuso Parkkinen (@ouzor)

Data Scientist and AI Designer at Reaktor (@ReaktorNow)

DataBusiness Challenge event, January 20th 2017

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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

REAKTOR JANUARY 2017 Source: https://commons.wikimedia.org/wiki/File:Contrexx_wms_3_dashboard.png

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Customer understanding

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Source: https://www.flickr.com/photos/coolinsights/24164542345

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Automatic recommendations

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What can go wrong?

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Enemies of data-drivenFocusing 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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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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Action Data Information

Bus

ines

s 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

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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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Go lean - experiment and iterate!

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Success story: Elisa Growth Hacking teamSingle 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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You know nothing, Jon Snow

Juuso Parkkinen / @ouzor / Reaktor

You can do it.

We can help!

And we’re hiring: https://www.reaktor.com/careers/