1.
www.mimeria.com
Don't build
a data science team
Data 2020 Summit, 2018-09-13
Lars Albertsson
www.mimeria.com
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www.mimeria.com
Many journeys start ...
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Big Data!
AI!!
Data driven!
Blockchains?
Real-time
analytics!

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www.mimeria.com
Many journeys start with data science
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Big Data!
AI!!
Data driven!
Blockchains?
Real-time
analytics!

4.
www.mimeria.com
The typical results
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Proof of value
Prototype
Product
ROI

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www.mimeria.com
Wrong data scientists?
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When to use Jaccard
or cosine distance?
How do you implement an
LSTM with Tensorflow?
When do you
terminate an A/B test?
What we asked them

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www.mimeria.com
What we asked them What we should have asked?
Wrong data scientists?
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When to use Jaccard
or cosine distance?
How do you implement an
LSTM with Tensorflow?
When do you
terminate an A/B test?
How to get data from a
PO using email only?
How to recover Hadoop
namenode?
How to debug AWS
"403 permission denied"?
How to get sysadmin to
open firewall from
Jupyter to MySQL?

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www.mimeria.com
Size = effort Credits: “Hidden Technical Debt in
Colour = code complexity Machine Learning Systems”,
Google, NIPS 2015
Machine learning products
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Configuration Data collection
Monitoring
Serving
infrastructure
Feature extraction
Process
management tools
Analysis tools
Machine
resource
management
Data
verification
ML

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www.mimeria.com
Data science
Machine learning products
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Configuration Data collection
Monitoring
Serving
infrastructure
Feature extraction
Process
management tools
Analysis tools
Machine
resource
management
Data
verification
ML

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www.mimeria.com
The data science team
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ML

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www.mimeria.com
Data science hierarchy of needs
Credits: “The data science hierarchy of needs”,
Monica Rogati
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AI
Deep learning
A/B testing
Machine learning
Analytics
Segments
Curation
Anomaly detection
Data infrastructure
Pipelines
Instrumentation
Data collection

11.
www.mimeria.com
Data science hierarchy of needs
Data science
Credits: “The data science hierarchy of needs”,
Monica Rogati
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AI
Deep learning
A/B testing
Machine learning
Analytics
Segments
Curation
Anomaly detection
Data infrastructure
Pipelines
Instrumentation
Data collection

12.
www.mimeria.com
The data science team
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AI
Deep learning
A/B testing
Machine learning

13.
www.mimeria.com
AI first
● Might work once or twice
● Not a sustainable strategy
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AI
Deep learning
A/B testing
Machine learning

14.
www.mimeria.com
AI first
● Might work once or twice
● Not a sustainable strategy
● Machine learning is difficult
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AI
Deep learning
A/B testing
Machine learning
Effort

15.
www.mimeria.com
AI first
● Might work once or twice
● Not a sustainable strategy
● Machine learning is difficult
● Low return of investment
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AI
Deep learning
A/B testing
Machine learning
Value Effort

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AI last
● Lots of hanging fruit
○ Push notifications
○ Simple recommendations
○ Risk & forecasting
○ Reporting
○ Product insights
○ Data-driven product development
○ Anomaly detection
○ ...
● High return of investment
● Media attention != business value
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Analytics
Segments
Curation
Anomaly detection
Data infrastructure
Pipelines
Instrumentation
Data collection
Value Effort

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www.mimeria.com
How do we make best use of data scientists?
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● They need
○ Supporting roles
○ Continuous access to fresh data
○ Feedback from validation, monitoring, ...
● But where, how, from whom?
?

18.
www.mimeria.com
Data engineering
Domain expertise
What do we need?
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Configuration Data collection
Monitoring
Serving
infrastructure
Feature extraction
Process
management tools
Analysis tools
Machine
resource
management
Data
verification
ML
Product management
QA

19.
www.mimeria.com
Data engineering
Data science
Frontend
Domain expertise
What do we want?
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Configuration Data collection
Monitoring
Serving
infrastructure
Feature extraction
Process
management tools
Analysis tools
Machine
resource
management
Data
verification
ML
DevOps /
DataOps
QA
Product management

20.
www.mimeria.com
Data engineering
Domain expertise
Most data-driven products
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Configuration Data collection
Monitoring
Serving
infrastructure
Feature extraction
Process
management tools
Analysis tools
Machine
resource
management
Data
verification
Product management

21.
www.mimeria.com
How to get to the summit?
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www.mimeria.com
Service-oriented architectures
● Data lives with services
● Heterogeneous coupling
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Service Service Service
App App App
Poll
Aggregate
logs
NFS
Hourly dump
Data
warehouse
ETL
Queue
Queue
NFS
scp
DB
HTTP
DB DBDB

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Service-oriented organisations
● Teams own services
● Teams own data
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Data-centric innovation
● Need data from teams
○ willing?
○ backlog?
○ collected?
○ useful?
○ quality?
○ extraction?
○ data governance?
○ history?
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25.
www.mimeria.com
Data-centric innovation
● Need data from teams
○ willing?
○ backlog?
○ collected?
○ useful?
○ quality?
○ extraction?
○ data governance?
○ history?
● Innovation friction
Value adding Waste
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Big data - a collaboration paradigm
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Stream storage
Data lake
Data
democratised

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Data pipelines
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Data lake

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More data - decreased friction
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Data lake

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In the lab
One shot
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In the lab vs in production
One shot Iterative
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Data lake

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Data agility
● Siloed: 6+ months
● Autonomous: 1 month
● Coordinated: days
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Data lake
∆
∆
Latency?

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Data agility
● Siloed: 6+ months
Cultural work
● Autonomous: 1 month
Technical work
● Coordinated: days
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Data lake
∆
∆
Latency?

33.
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What to do with my data scientists?
● Get them out into production
● Pair them with
○ Data engineers
○ Domain experts
○ Product owners
● Invest in processing capabilities
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34.
www.mimeria.com
Key takeaways
● Machine learning is a team sport
● Solid data processing is necessary
● Learning happens in production
Lars Albertsson, founder of Mimeria
Data-value-as-a-service - tailored data platforms & data pipelines
34

35.
www.mimeria.com
Key takeaways
● Machine learning is a team sport
● Solid data processing is necessary
● Learning happens in production
Lars Albertsson, founder of Mimeria
Data-value-as-a-service - tailored data platforms & data pipelines
35