The document discusses building analytics capabilities in businesses and overcoming common barriers. It describes moving along a continuum from descriptive to diagnostic analytics, with increasing business value and sophistication. Key takeaways include defining the desired impact, having an implementation scenario, managing risks through structure and behavior, working agilely, finding flexibility, using microservices, automating processes, and having a collaborative team with clear roles.

1. Data Science
Development
with Impact
PAW Berlin 2018
Norbert Wirth
@Bert_feed

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Cost-
focused
Intuitive
business
insights
Siloed
decision
making
Pre
Value-
focused
Data and
analytics driven
business
insights +
intuition
Collaborative
decision-
making
Post
Businesses want…

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Welcome to the data
driven organization

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Welcome to the analytics
driven organization

5. Building analytics capabilities requires overcoming barriers that are
common across industries
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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

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Money & Risks

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…when doing

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Describe, summarize
and analyze 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

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Descriptive
Predictive
Prescriptive
AI/Cognitive
IncreasingBusinessValue
Diagnostic
Increasing Sophistication of Data & Analytics
Where’s the money?

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Descriptive
Predictive
Prescriptive
AI/Cognitive
IncreasingBusinessValue
Diagnostic
Increasing Sophistication of Data & Analytics
How big is the risk?

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Data Scientists

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What could possibly
go wrong?

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…, of course 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

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The POC trap

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We don’t want to
end up delivering no
impact

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Get it to work

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Good idea to define
impact

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Being a smart genius
won’t do the trick. Quite
the opposite!

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About being a sprocket

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The implementation
scenario

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Agile and free?

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Planning

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Shippable
increments

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The invention risk

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scope
quality

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No throwing over the
fence (♥ dev ops)

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Automate!

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Microservices
are good

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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

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 Define impact
 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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norbert.wirth@pwc.com
@Bert_feed
https://www.linkedin.com/in/norbertwirth/