Q2014 – Special Session Big Data Vienna, 4 June 2014

Quality Approaches to Big DataPeter Struijs and Piet Daas

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Limitations of the established quality frameworks and methodology

Options

What to doin the changing context of making statistics

Approaches and data sources

Surveys / questionnaires

e.g. sampling theory

Administrative data sources

Where does Big Data fit in? 3

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

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Limitations of the established quality frameworks and methodology

Small, medium-sized & large vehicles

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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.

Daytime population based on mobile phone data

The top three issues

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Population not known

Unbalanced

coverage

Relevance of data not

clear

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Options

Population not known

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Derive background information

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

Unbalanced coverage

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Use modeling approaches

Relevance of data not clear

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Calibration / fitting

Study correlations

Use Big Data for “stand alone”

information

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What to doin the changing context of making statistics

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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

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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

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