Many disciplines are on the wrong side of speed - there is a tradeoff with development speed and security, data science, compliance, etc. Let us look at disciplines that have succeeded in shifting left by integrating development, and learn successful patterns: testing, DevOps, agile, DataOps.

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The right side of speed -
learning to shift left
Lars Albertsson, Scling
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Left is up
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Winston W Royce:
"Managing the development
of large software systems"

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Speed vs X tradeoff?
security
compliance
Speed or quality
operations
tech debt
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Enterprise culture - friction as a service
analyst analyst
architect architect
developer developer
risk manager vs risk manager
security officer security officer
tester tester
ops engineer ops engineer
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Is there a tradeoff?
Separation pitches teams and interests against each other
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Ghost of shifts past
70s - 90s: reign of waterfall
90s: flood of IT failures
dot com crash
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Extreme programming
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A ladder to climb
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Test certified program
Google ~2006

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Agile
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Revenue models or role models
10Credits: @johncutlefish, @henrikkniberg

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DevOps
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No speed tradeoff!
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Collaboration is
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people
workflows
coupling / decoupling
not
tools

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The most disruptive tech changes collaboration
RDBMS
version control
GNU + Linux
git + Github
containers
cloud
Hadoop
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Big data
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Hadoop
file storage
batch processing
single dimension database
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Big data workflows
immutable datasets
functional transform processing
data pipelines
homogeneous, coordinated workflows
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immutable datasets
functional transform processing
data pipelines
homogeneous, coordinated workflows
democratised data
minimal operations
minimal risk deployments
reproducibility
Big data workflows
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Big data workflows
immutable datasets
functional transform processing
data pipelines
homogeneous, coordinated workflows
democratised data
minimal operations
minimal risk deployments
reproducibility
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Real value of big data.
Not achieved by late adopters.

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Ghost of shifts present
*Ops all the things
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DataOps
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Enterprise culture big data
Hadoop / Spark / Flink
+
traditional workflows
mutable data
microservices
functional teams
heterogeneous data platform
=
worst of two worlds
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Enterprise culture DevOps
Kubernetes / cloud
+
test in staging
handover deployment
no prod access
(Dev)Ops team
...
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How to shift left
common goals
same definition of success
incentives, not gateways
common tools & environments
common processes
common teams
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Craft - improving artifacts
25AMC Javelin, CZmarlin, https://commons.wikimedia.org/wiki/File:AMC_Javelin_1971-74_purple_blown_custom.JPG

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Factory - improving processes
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Lotus final assembly, Brian Snelson, https://www.flickr.com/photos/32659528@N00/2868525496
AMC Javelin, CZmarlin, https://commons.wikimedia.org/wiki/File:AMC_Javelin_1971-74_purple_blown_custom.JPG

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1994: OS/2 Warp CID installation
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Grmbl, who
reinstalled my
machine?

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IT craft to factory
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Lotus final assembly, Brian Snelson, https://www.flickr.com/photos/32659528@N00/2868525496
AMC Javelin, CZmarlin, https://commons.wikimedia.org/wiki/File:AMC_Javelin_1971-74_purple_blown_custom.JPG
Security Waterfall
Application
delivery
Traditional
operations
Traditional
QA
Infrastructure
DevSecOps Agile
Containers
DevOps CI/CD
Infrastructure
as code

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Security Waterfall
IT craft to factory
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Application
delivery
Traditional
operations
Traditional
QA
Infrastructure
DB-oriented
architecture
DevSecOps Agile
Containers
DevOps CI/CD
Infrastructure
as code
Data factories,
data pipelines,
DataOps
Lotus final assembly, Brian Snelson, https://www.flickr.com/photos/32659528@N00/2868525496
AMC Javelin, CZmarlin, https://commons.wikimedia.org/wiki/File:AMC_Javelin_1971-74_purple_blown_custom.JPG

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DevSecOps
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SECURITY

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DevSecOps by induction
validate security of system
make secure easy
validate security of changes
ignore perfection - focus on delta
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Ghost of shifts yet to come
machine learning
security
compliance
are still at ends with speed
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MLOps
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DATA
SCIENCE

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Naive ML
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Towards sustainable production ML
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Multiple models,
parameters, features
Assess ingress data quality
Repair broken data from
complementary source
Choose model and parameters based
on performance and input data
Benchmark models
Try multiple models,
measure, A/B test

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ComplianceOps
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COMPLIANCE

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ComplianceOps by induction
validate compliance of system
make compliant easy
validate compliance of change
ignore perfection - focus on delta
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long game
very long
ladders
recipes
people & teams
craft to process
focus on delta
Doing the shift
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Doing the shift
long game
very long
ladders
recipes
people & teams
craft to process
focus on delta
learn faster by collaboration
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Data value = data + domain expertise + data practices
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Disrupt?
https://xkcd.com/1831/
Adapt?
+ 1000s of failures...

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Data value = data + domain expertise + data practices
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Disrupt?
https://xkcd.com/1831/
Adapt?
+ 1000s of failures...

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Data value = data + domain expertise + data practices
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Data lake
Stream storage
Client data +
domain expertise
Practices from
data leaders
Disrupt?
https://xkcd.com/1831/
Collaborate?
Data-value-as-a-service
Adapt?
+ 1000s of failures...

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The ghost of Winston W. Royce (1929-1995)
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Tech has massive impact on society
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Product?
Supplier?
Employer?
Make an active
choice whether to
have an impact!
Cloud?