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Reaktor
Mannerheimintie 2
00100, Helsinki Finland
tel: +358 9 4152 0200
www.reaktor.com
info@reaktor.com
Confidential
©201...
2
Why data-driven?
Firms that adopt data-driven decision making have output and productivity
that is 5-6% higher than what...
REAKTOR
JANUARY 2017
Data-driven in practice
4
Make data visible
REAKTOR
JANUARY 2017 Source: https://commons.wikimedia.org/wiki/File:Contrexx_wms_3_dashboard.png
5
Customer understanding
REAKTOR
JANUARY 2017
Source: https://www.flickr.com/photos/coolinsights/24164542345
6
Automatic recommendations
REAKTOR
JANUARY 2017
REAKTOR
JANUARY 2017
What can go wrong?
8
Enemies of data-driven
Focusing on the data instead of the business goals
Lack of clear use cases for analytics
Lack of ...
9
Enemies of data-driven (2)
Strong egos and internal politics
Unrealistic expectations
Focusing on IT systems
Tech-decisi...
REAKTOR
JANUARY 2017
Data-driven culture
11
Culture is key!
Aim at right and concrete goals
Understand risks, accept complexity
Make tests and experiments
Seek evi...
12
Data-driven culture at Airbnb
“The foundation on which a data science team
rests is the culture and perception of data
...
13
Start with business goals!
REAKTOR
JANUARY 2017
Action DataInformation
Business
goal 1
goal 2
goal 3
goal 4
goal 5
Go t...
14
Netflix connects business goals and data
“Our business objective is to maximize member satisfaction and month-to-month
...
15
Go lean - experiment and iterate!
REAKTOR
JANUARY 2017
16
Success story: Elisa Growth Hacking team
Single goal: Improve sales.
Solution: A self-directing, lean startup, business...
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/
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Data-driven leadership culture

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

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Data-driven leadership culture

  1. 1. Reaktor Mannerheimintie 2 00100, Helsinki Finland tel: +358 9 4152 0200 www.reaktor.com info@reaktor.com Confidential ©2015 Reaktor All rights reserved Data-driven leadership culture Juuso Parkkinen (@ouzor) Data Scientist and AI Designer at Reaktor (@ReaktorNow) DataBusiness Challenge event, January 20th 2017
  2. 2. 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 REAKTOR JANUARY 2017
  3. 3. REAKTOR JANUARY 2017 Data-driven in practice
  4. 4. 4 Make data visible REAKTOR JANUARY 2017 Source: https://commons.wikimedia.org/wiki/File:Contrexx_wms_3_dashboard.png
  5. 5. 5 Customer understanding REAKTOR JANUARY 2017 Source: https://www.flickr.com/photos/coolinsights/24164542345
  6. 6. 6 Automatic recommendations REAKTOR JANUARY 2017
  7. 7. REAKTOR JANUARY 2017 What can go wrong?
  8. 8. 8 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 REAKTOR JANUARY 2017
  9. 9. 9 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 REAKTOR JANUARY 2017
  10. 10. REAKTOR JANUARY 2017 Data-driven culture
  11. 11. 11 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 REAKTOR JANUARY 2017
  12. 12. 12 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/ REAKTOR JANUARY 2017
  13. 13. 13 Start with business goals! REAKTOR 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. 14. 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 REAKTOR JANUARY 2017
  15. 15. 15 Go lean - experiment and iterate! REAKTOR JANUARY 2017
  16. 16. 16 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/ REAKTOR JANUARY 2017
  17. 17. You know nothing, Jon Snow
  18. 18. Juuso Parkkinen / @ouzor / Reaktor You can do it. We can help! And we’re hiring: https://www.reaktor.com/careers/

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

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