1. The role of Statistical Computing in delivering quality Amy Large Statistical Computing Branch Survey Methodology & Statistical Computing Division, Research, Development and Infrastructure Directorate

2. Presentation Outline Background and context Statistical Computing Projects ONS Strategic Aims Conclusions

3. Context (general) Do we have common understanding? The error seems to have happened because the national rail operator gave the wrong dimensions to train company. "When you separate the rail operator from the train company, this is what happens." Transport Minister, Frederic Cuvillier

4. Some costly software errors

5. Context (ONS) Restructure April 2012 IT Methodology Research, Development and Infrastructure Directorate Statistical Computing Branch

6. Statistical Computing Branch Structure Small team Not linked to a specific business process Centrally funded, with some funding for strategic projects Multiple routes for engaging with project work Hub and node working approach

7. Projects Type A: involvement in large projects, act to help interpret requirements as a bridge between business areas and developers. Type B: carry out small-scale development work, e.g. replacing spreadsheet processes. How we work with each type of project will vary depending on requirements

8. ONS Strategic Aims Part of the ONS Strategy (published March 2013) Nine Aims: 1.Inform debate and have a greater impact on decision making 2.Dramatically improve the communication of our statistics and analyses 3. Be highly regarded by our customers for producing trustworthy statistics and analyses that anticipate their needs 4.Be at the forefront of integrating and exploiting data from multiple sources 5. Have flexible and efficient processes and systems for statistical production, underpinned by sound methodology 6.Improve quality and minimise the risk of errors7. Keep the data we hold secure8. Be a statistical powerhouse at the heart of the Government Statistical Service and the European Statistical System 9. Have skilled and motivated people who are enthusiastic for change 6.Improve quality and minimise the risk of errors 5. Have flexible and efficient processes and systems for statistical production, underpinned by sound methodology

9. Strategic Aim – Flexible and efficient processes Understanding how to build a flexible system Hard coding / parameterisation Modular code Shared code Documentation and on-going support Understanding how to build an efficient system: Right software / platform / method Knowing your data Programming good practice

10. Strategic Aim – Sound Methodology System redevelopment – quality assure and test against current processes Modular code – ‘Plug and play’ Share common functionality data DS1; set DS1; %ratio_imputation; run; data DS1; set DS1; %near_neighbour_imputation; run;

11. Strategic Aim – Improving quality Statistical Quality comes from (ESS): Output Quality: –Relevance, Accuracy, Timeliness & Punctuality, Accessibility & Clarity, Comparability, Coherence Process Quality: –Efficiency, Flexibility, Transparency, Robustness, Effectiveness, Integration

12. Strategic Aim – Minimising the risk of errors Common understanding How do errors occur? Human Data Process Lack of QA Project work often dictated by strategic review

13. Branch Objectives Standard setting Reduction of risk Solution re-use Appropriate solution selection

14. Conclusions New way of working More demand with increasing visibility and reputation Successes: Training – responsive, relevant, looking wider than the branch Iterative, interactive, responsive – Zero Hours Contracts, Human Capital... Reducing risk across the Office

15. John Pullinger – National Statistician “ mobilising the power of data to help Britain make better decisions about our future”

16. Contact Amy Large amy.large@ons.gsi.gov.uk Daniel Lewis (Branch Head) daniel.lewis@ons.gsi.gov.uk