horizon 2020 - europa · 2020-03-12 · horizon 2020 policy support facility page 3 big data… n a...

Horizon 2020 Policy Support

Facility

Big data for evidence-informed policy making:A short introduction

Martijn Poel9 June 2017, Brussels

Horizon 2020 Policy Support Facility


Big data…

n A step change in the scale and scope of the sources of materials (and tools for manipulating these sources) available in relation to a given object of interest (Schroeder 2014)

n Compatible with definitions that focus on:Ø Volume, e.g. number of objects, time series Ø Variety, e.g. official statistics, sensor, social mediaØ Velocity, e.g. real-time dataØ The challenge of Veracity, e.g. cleaning, addressing noise, bias

n Velocity and Exhaustivity (‘n=all’) often lead to Volume, e.g. when using sensors or web scraping (Kitchin & McArdle 2016)

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…for evidence-informed policy making

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Data collection and data linking- Established

data sources- New data

sources

Data analytics: theory-based, indicator-based, using statistics,

econometrics, machine learning, etc.

Visualisation and visual analytics

Policymaking- Agenda and priority setting- Policy options- Policy design- Ex ante evaluation and

impact assessment- Implementation- Monitoring- Ex post evaluation and impact

assessment


Inventory of relevant operational initiatives (58)

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n Status of the initiative: pilot, demonstrator, implementation

n Type of responsible authority: department, agency, PPP, etc.

n Policy area(s) addressed by the data for policy initiative: alln (Expected) use of the data in the policy cycle: first halfn Data sources, variety, volume, velocity: some new data sourcesn Data format and data interoperability: good progressn Data veracity: un/verified, in/complete, in/consistent: okn Level of openness of data: half the data openn Data analysis methodologies: mostly descriptiven Data analytics tools and platforms: basic and advanced


Examples related to research and innovation

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n Data about R&I policies and impact: online, interactive country reports and cross-country thematic reports

n STAR METRICS repository of datasets and tools to assess the impact of US federal R&D investments

n Data on R&D projects, public procurement, patents, trademarks, publications, websites of ICT companies (e.g. employment), blogs/news sites, job portals

Big data for the evaluation of R&D grants in the ICT sector (Spain)


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8

10

12

14

24

39

Small-scale survey data

Social media

Commercial/traded business data consumer / market data

Large-scale survey data

Sensor-based data

Statistical offices

Administrative data

0 5 10 15 20 25 30 35 40 45

n = 111

Data sources

Administrative data

Statistical offices

Sensor-based data

(other) Large-scale surveys

Commercial/traded data

Social media data

Small-scale surveys

Data sources

Type of data source closely linked to openness of data: ’half the data open, half the data closed’

58 initiatives using111 data sources: data linking


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5

6

7

8

10

12

13

43

0 10 20 30 40 50

Agent-based modelling

Benchmarking

Sentiment mining

Predictive analytics

Text analytics

Profiling

Other

Trend analysis

Descriptive statistics

n = 58

Type of data analysis


Conclusions of our September 2015 workshop

n Transparency, accuracy, inclusion, accountability: no black boxing!

n Skills for developing and interpreting data-driven approaches

n Relevant data or readily available data? Intervention logic!

n Policy-based evidence: lies, statistics and big data

n Policy for data, e.g. privacy, ownership, standardisation

n Dual learning: policy experiment + big data experiment

Horizon 2020 Policy Support Facility10


Possible (sub) themes for this Mutual Learning Exercise

n Data linking and data sharing: the IT and data/information infrastructures and the partnerships requiredØ Standardised document exchange formats, unique identifiers of companies,

re-coding of data (human intervention or machine learning) Ø Political, cultural and financial aspects of sharing data between

organisations such as funding agencies, ministries, statistical offices, Chambers of Commerce, regional governments

n Emerging data sources and related data analytical methodsØ Using new open or proprietary data about companies, to obtain more

complete and timely information about R&D, innovation, emerging topics, growth and employment of companies, etc.

Ø Text mining of company websites, commercial databases, open databases such as H2020, company websites, job search websites

n Ethical issues of linking, sharing and analysing more data

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horizon 2020 - europa · 2020-03-12 · horizon 2020 policy support facility page 3 big data… n a...

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