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Data Science Development with Impact
The document discusses the transformation towards data-driven organizations, highlighting barriers to building analytics capabilities such as fragmented teams and insufficient funding. It emphasizes the importance of defining impact, structured implementation, and agile methodologies in overcoming these challenges while adopting advanced analytics. Key takeaways include the need for collaboration, clear roles, and avoiding pitfalls like the proof of concept trap.
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Data Science Development with Impact
- 1. Data Science Development with Impact PAWBerlin 2018 Norbert Wirth @Bert_feed
- 2. PwC 2 Cost- focused Intuitive business insights Siloed decision making Pre Value- focused Data and analyticsdriven business insights + intuition Collaborative decision- making Post Businesses want…
- 3. PwC 3 Welcome tothe data driven organization
- 4. PwC 4 Welcome tothe analytics driven organization
- 5. Building analytics capabilitiesrequires overcoming barriers that are common across industries PwC 5 Which of these barriers sound familiar to you? BehavioralOrganizationalStructural Fragmented Analytics teams Limited focus on incubating future vision Insufficient funding Unclear governance structure Lack of cross-functional problem-solving Collaboration impeded by competition Intuitive vs data-driven decision-making Company culture Lack of information sharing Spotlight on near-term solution Legacy infrastructure Multiple platforms and tools Complex data environment Poor data quality Lack of trained staff Lack scalability
- 6. PwC 6 Money &Risks
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- 8. PwC 8 Describe, summarize andanalyze historical data Recommend ‘right’ or optimal actions or decisions Monitor, decide, and act autonomously or semi-autonomously Predict future outcomes based on facts from the past and simulations Descriptive Predictive Prescriptive AI/Cognitive IncreasingBusinessValue Identify causes of trends and outcomes Diagnostic Increasing Sophistication of Data & Analytics (What happened?) (Why it happened?) (What could happen?) (What should be done?) (How do we adapt to change?) Continuum of analytics capabilities
- 9. PwC 9 Descriptive Predictive Prescriptive AI/Cognitive IncreasingBusinessValue Diagnostic Increasing Sophisticationof Data & Analytics Where’s the money?
- 10. PwC 10 Descriptive Predictive Prescriptive AI/Cognitive IncreasingBusinessValue Diagnostic Increasing Sophisticationof Data & Analytics How big is the risk?
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- 12. PwC 12 What couldpossibly go wrong?
- 13. PwC 13 …, ofcourse your algorithm might simply not be working > fatal error | fatal error | fatal error | fatal error | fatal error | fatal error | fatal error | fatal error | fatal error | fatal error | fatal error | fatal error | fatal error | fatal error | fatal error | fatal error | fatal error
- 14. PwC 14 The POCtrap
- 15. PwC 15 We don’twant to end up delivering no impact
- 16. PwC 16 Get itto work
- 17. PwC 17 Good ideato define impact
- 18. PwC 18 Being asmart genius won’t do the trick. Quite the opposite!
- 19. PwC 19 About beinga sprocket
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- 21. PwC 21 Agile andfree?
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- 26. PwC 26 No throwingover the fence (♥ dev ops)
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- 29. PwC 29 Dream team Backend developer Frontend developer UCD/UX/UI QA Architect Product owner Technical P.owner Data Scientist B.Data engineer Scrum master ML engineer
- 30. PwC 30 Defineimpact Implementation scenario Risks, structure, org. & behaviour Work really agile Find your flexible dimension Microservices Automate Team play w. clear roles Just see $$ Run into the POC trap Think data=solution Think you can fix everything Don’t plan Waterfall Monolithic systems Ops will run the stuff Key takeaways
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