TheONSstatisticalmodernisationprogramme–whatwent摘要.pptVIP

TheONSstatisticalmodernisationprogramme–whatwent摘要.ppt

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The ONS statistical modernisation programme – what went right; what went wrong? Stephen Penneck Director General, ONS Background In 2000: Need to upgrade statistical systems Expensive; Outdated; High risk Need for integration Many years of under-investment ONS formed through merger Added to diversity and complexity Need for common culture The origin Business case for investment accepted Funding rising to £40m pa from 2004/5 Central ONS Repository for Data: CORD What have we delivered? the national accounts core system life events and civil partnerships systems annual survey of hours and earnings new monthly indicators of earnings population estimates (part) foreign direct investment integrated business register and employment survey pilot (BRES) telephone data entry system upgrade for business data secure electronic capture system for business data upgraded scanning system for business survey questionnaires improved matching system for the business register Improved reweighting of the Labour Force Survey and redesigned core questions for the Integrated Household Survey moving retail price index system to an upgraded server an agreed set of corporate statistical tools including ACTR, Banff, Canceis, SAS, and X12-ARIMA. a unified quality reporting system some progress has been made on unified metadata. Improved technical and business capability a wide range of clear standards and working practices including IT development and business analysis. a more robust programme management culture better risk analysis What went well? What went wrong? The vision The organisational restructure Statistical and IT infrastructure Team commitment Learning from experience Leadership Achievability Capability Business engagement Culture The future The ONS Design Authority is developing explicit design principles, covering methods, tools, processes and systems The new IT strategy (‘Red to Green’) prioritises the development of our most vulnerable statistical systems. Fu

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