New Data Sources Andrew O’Sullivan Assistant Director General / CIO Central Statistics Office Social Statistics in the ESS Statistics Austria Vienna, 2 October 2017

Introduction

• Analytics vs. (Official) Statistics Maturity• COLLECT PHASE – Examples: Integrated Administrative Data

• Greater than analytic value of individual datasets

• COLLECT PHASE – Example: Mobile Tourism Data• DISSEMINATE PHASE – Example: Visualisation vs. Derived

Statistics• Conclusions

Analytics Maturity Administrative Data Mobile Data Visualisation vs. Composite Introduction Conclusions

Collect Process Analyse Disseminate

Search “Analytics Maturity Model”

Introduction Administrative Data Mobile Data Visualisation vs. Derived Analytics Maturity Conclusions

Data Integration: Graduate Outcomes

Introduction Graduate Statistics What do Graduates Do What do Graduates Earn Where do Graduates Work Conclusions

Source Tier Personal Data

Analysis Tier Pseudonymised Data

3rd Level Enrolment P35 CRS Form 11

Personal Identifiers Removed

Enrolment Data P35

CRS Form 11

Matching using Protected Identifier Key based on PPSN

Introduction Analytics Maturity Mobile Data Visualisation vs. Derived Administrative Data Conclusions

Proportion of Graduates in each NACE Sector

2010 Graduates, substantially employed

0%

5%

10%

15%

20%

25%

Proportion of Substantially

Employed Graduates

After 1 year

After 3 years

After 5 years

Introduction Analytics Maturity Mobile Data Visualisation vs. Derived Administrative Data Conclusions

NACE Sectors by Sex

2010 Graduates, after 3 years

0

5

10

15

20

25

Proportion of Substantially

Employed Graduates by Sex

(Percent)

Male Female

Introduction Analytics Maturity Mobile Data Visualisation vs. Derived Administrative Data Conclusions

Earnings by Field of Study – Time since Graduation

0

100

200

300

400

500

600

700

800

900

1.000

Weekly Earnings (€)

2010 Graduates, after 1, 3 and 5 years, NFQ 8,substantially employed

Introduction Analytics Maturity Mobile Data Visualisation vs. Derived Administrative Data Conclusions

Earnings by Degree Class

Introduction What do Graduates Do What do Graduates Earn Where do Graduates Work Conclusions

445

570

715

420

535

650

370

480

585

345

445 540

0

100

200

300

400

500

600

700

800

900

1.000

1 3 5 1 3 5 1 3 5 1 3 5

Weekly Earnings (€)

Years since Graduation

H1/Distinction H21/M1 H22/M2 H3/Pass

2010 Graduates, NFQ Level 7 and 8, Substantially employed

Graduate Statistics

Data Integration: Residential Property Price Index

Introduction Admin Data Composite Stats Mobile Data Conclusions Analytics Maturity Introduction Analytics Maturity Mobile Data Visualisation vs. Derived Administrative Data Conclusions

7PM

Source: Vodafone / Fáilte Ireland

Insight Gap: Tourism Statistics By Region

7PM

Insight Gap: Tourism Statistics By Region

12PM 7PM

Source: Vodafone / Fáilte Ireland (CSO does not hold mobile data)

Insight Gap: Tourism Statistics By Region

Straight Visualisation Apps vs. Derived Statistics

Introduction Administrative Data Mobile Data Analytics Maturity Visualisation vs. Derived Conclusions

Conclusions

Introduction Administrative Data Mobile Data Visualisation vs. Composite Conclusions Analytics Maturity

• New data sources easier to identify and leverage if considered from an outcome / value perspective

• Integrated data more powerful than standalone – more true than ever with new sources

• Consider blending Analytics and Statistics for more effective Dissemination and better user experience