Pablo Suau - DWP Digital

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The document discusses how machine learning can be used to empower decision making in labour market strategy. It proposes using descriptive, predictive, and prescriptive analytics on data to provide information, insights, and recommendations to inform and communicate policy design. It also emphasizes establishing reusable analytical pipelines and experiments that can be reproduced to facilitate collaboration and reduce errors.

Technology

Machine Learning to empower decision making in
labour market strategy
Newcastle Data Science Hub
Pablo Suau
pablo.suau@dwp.gsi.gov.uk

2Department for Work & Pensions
DWP Digital
●
We build simple, fast, clear services that put the people who use them first
●
Our services touch the lives of almost every UK citizen at some point in their lives

3Department for Work & Pensions
Churchill
As a policy person
I want to find enough, relevant data that I understand and trust
so I can compare and explore by geography, time and
characteristic information to identify trends and issues to form
a picture
to inform and communicate policy design and briefing
user need
data challenge
visualisation challenge
common goal

4Department for Work & Pensions
Churchill

5Department for Work & Pensions
Informing decisions via Machine Learning
Descriptive
Analytics
Prescriptive
Analytics
Predictive
Analytics
What happened?
What will happen?
What could/should I do?
value
complexity
RECOMMENDATIONS
INSIGHTS
INFORMATION

6Department for Work & Pensions
Greyhound

7Department for Work & Pensions
Long Island

8Department for Work & Pensions
Analytical pipelines
• Reduce manual processes
• Reproduce results
• Reuse code and analytical tools
Data storage Preprocessing Features Models
Prototype’s data
and
visualisations

9Department for Work & Pensions
A “network” of pipelines
Prescriptive pipelines
Predictive pipelines
Analysis pipelines
Data
Data Science
Visual (Churchill)
Engineering
User
EXPLORATION

10Department for Work & Pensions
The importance of experiments reproducibility
“Establishing workflows that effectively allow other members of the team to reproduce our analysis
with the same data we used and obtain exactly the same results”
• Save time
• Improved collaboration
• Reduce risk of errors
analysis code
pipelines
modules

11Department for Work & Pensions
Conclusions
●
Data to empower policy decisions
●
Machine learning to provide insights and recommendations
●
Practices, workflows and infrastructures for
– Iterative complexity
– Repeatability

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Pablo Suau - DWP Digital

1. Machine Learning to empower decision making in labour market strategy Newcastle Data Science Hub Pablo Suau pablo.suau@dwp.gsi.gov.uk

2. 2Department for Work & Pensions DWP Digital ● We build simple, fast, clear services that put the people who use them first ● Our services touch the lives of almost every UK citizen at some point in their lives

3. 3Department for Work & Pensions Churchill As a policy person I want to find enough, relevant data that I understand and trust so I can compare and explore by geography, time and characteristic information to identify trends and issues to form a picture to inform and communicate policy design and briefing user need data challenge visualisation challenge common goal

4. 4Department for Work & Pensions Churchill

5. 5Department for Work & Pensions Informing decisions via Machine Learning Descriptive Analytics Prescriptive Analytics Predictive Analytics What happened? What will happen? What could/should I do? value complexity RECOMMENDATIONS INSIGHTS INFORMATION

6. 6Department for Work & Pensions Greyhound

7. 7Department for Work & Pensions Long Island

8. 8Department for Work & Pensions Analytical pipelines • Reduce manual processes • Reproduce results • Reuse code and analytical tools Data storage Preprocessing Features Models Prototype’s data and visualisations

9. 9Department for Work & Pensions A “network” of pipelines Prescriptive pipelines Predictive pipelines Analysis pipelines Data Data Science Visual (Churchill) Engineering User EXPLORATION

10. 10Department for Work & Pensions The importance of experiments reproducibility “Establishing workflows that effectively allow other members of the team to reproduce our analysis with the same data we used and obtain exactly the same results” • Save time • Improved collaboration • Reduce risk of errors analysis code pipelines modules

11. 11Department for Work & Pensions Conclusions ● Data to empower policy decisions ● Machine learning to provide insights and recommendations ● Practices, workflows and infrastructures for – Iterative complexity – Repeatability

12. Machine Learning to empower decision making in labour market strategy Newcastle Data Science Hub Pablo Suau pablo.suau@dwp.gsi.gov.uk

Pablo Suau - DWP Digital

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