Top Banner
Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas
19

Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

Dec 31, 2015

Download

Documents

Clinton Parrish
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

Q2014 – Special Session Big Data Vienna, 4 June 2014

Quality Approaches to Big DataPeter Struijs and Piet Daas

Page 2: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

2

Limitations of the established quality frameworks and methodology

Options

What to doin the changing context of making statistics

Page 3: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

Approaches and data sources

Surveys / questionnaires

e.g. sampling theory

Administrative data sources

Where does Big Data fit in? 3

Page 4: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

Two levels of quality

Quality as related to methodology

General quality criteria as defined in Code of Practice:

‐ Relevance‐ Accuracy and reliability‐ Timeliness and punctuality‐ Coherence and comparability‐ Accessibility and clarity

4

Page 5: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

5

Limitations of the established quality frameworks and methodology

Page 6: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

Small, medium-sized & large vehicles

22

Page 7: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

7

Figure 1. Development of daily, weekly and monthly aggregates of social media sentiment from June 2010 until November 2013, in green, red and black, respectively. In the insert the development of consumer confidence is shown for the identical period.

Page 8: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

Daytime population based on mobile phone data

Page 9: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

The top three issues

9

Population not known

Unbalanced

coverage

Relevance of data not

clear

Page 10: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

10

Options

Page 11: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

Population not known

11

Derive background information

Relate population at meso- or macro-level to other information

Page 12: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

Unbalanced coverage

12

Use modeling approaches

Page 13: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

Relevance of data not clear

13

Calibration / fitting

Study correlations

Use Big Data for “stand alone”

information

Page 14: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

14

What to doin the changing context of making statistics

Page 15: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

15

Page 16: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

Strategic aspects

Others start producing statistics• there may be quality issues• but they are extremely rapid• and there is obviously demand

Need for good, impartial informationwill remain• without a monopoly for NSIs

NSIs must validate information produced by others

16

Page 17: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

The way forward

Get to know Big Data

Use Big Data for efficiency and response burden reduction

Use Big Data for early indicators

Start with Big Data, not with the desired outcome

Create the right environment

17

Page 18: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

18

Page 19: Q2014 – Special Session Big Data Vienna, 4 June 2014 Quality Approaches to Big Data Peter Struijs and Piet Daas.

19