Data Quality – UK activities Iain Macleay Head of Energy Balances, Prices and Publications 27 September 2013

1.Aspects of quality 2.Standard errors 3.Revisions 4.Risk based quality reviews 5.Quality training Contents

DECC follow UK statistical practice: -Relevance -Accuracy -Timeliness & punctuality -Accessibility & clarity -Comparability -Coherence Aspects of quality

DECC release data to pre-announced, year in advance, timetable – all releases at 9:30am. Energy Trends – Thursday 26 September; Thursday 19 December Thursday 27 March Thursday 26 June Dates set for coming year, by the DECC Chief Statistician – no political interference If data not released at 9:30 – DECC need to report breech to UK National Statisticians Office Data released as soon as available Timeliness & punctuality

Difficult to measure, but … -Sample sizes of surveys published with information on coverage -Where useful, standard errors published -Weighting to adjust for coverage -Administrative sources used where appropriate -Check accuracy of recording by comparing data sources (volume surveys, price surveys, company reports) Accuracy

Sample sizes and standard errors for Quarterly Fuels Inquiry - published in industrial price methodology note

-As working in policy departments – regular liaison so data meet needs. -Also try to anticipate their future needs. -Regularly survey of wider user community to check meeting their needs, every 2 to 3 years – results published on web -Review content of press notices and channels of communication (tweets etc) Relevance

-Data presented in consistent format -Helpful commentary drawing users to key points of interest (even if politically difficult), written independently by the statistics team -Clear info on contact details of DECC statistical teams -All info available for free on web -Metadata published – detailed method notes on web -Some info on revisions published Accessibility & clarity

Revisions – final consumption annual growth after one quarter

-Monthly data consistent with quarterly, and annual data – revised in line with better more complete information -Standard geographies used where possible -Energy balance format used so supply and demand consistent Coherence & comparability

-Data collections and publications should be reviewed on a regular basis -Tricky in practice – time consuming activity -Risk based approach being trialled -Methodically go through checklist -Most activities fairly low risk Quality reviews

Risk based review template SourcesMethodsSystemsProcessesQualityUsers & reputation People CensusData acquisition/qu estionnaire design System aData collection & preparation process RelevanceUser feedbackPeople AdminCoverage of data System bResults & analysis processes AccuracyFuture user needs SurveyProcessing, edit & imputation Timeliness & punctuality Reputation AnalysisAccessibility & clarity DisclosureComparability Coherence

Domestic fuel prices inquiry SourcesMethodsSystemsProcessesQualityUsers & reputation People Survey – 98% sample coverage Complex detailed survey, many issues including change of tariff structure etc. System redesigned in 2012 Data validation & editing Produces bills based on standard consumption rather than actuals Good feedback received New person each year as data processed by sandwich student Good geographical coverage Spread sheet back-up available Main system newly developed, but back-up used as double check Release 12 weeks after end of quarter Key policy area – so new data needs emerging Large survey – company 100 tariffs in 14 regions - so much scope for problems. Analysis Information published is disclosive, but pre-agreed with former monopoly suppliers Actions 1.Meet companies to improve form filling 2.Engage pro-actively with policy to find future needs 3.Ensure good documentation 4.Have sufficient staff trained to use system 5.Check data with that from similar surveys 6.Check data against firms published annual reports

-How do we ensure good quality statistics -Well trained staff -Training sessions held focusing on quality -All staff to attend – take through stages of statistical value chain -In DECC two statisticians trained up to train others Quality training