Process Improvements Andrew Kochen, HMRC

September 2008

Context

No funding in the short term for large IT system improvements• Looked at how to make other improvements

Reorganised the Trade Statistics area, with a temporary increase of staff in statistical team to maintain the quality of the data but leading to an overall reduced staff

Using a mix of small scale, largely technical changes to lead to overall improvement in the data

•General principles

• Aim to get data in ‘correct and on time’ (reduces corrections to be made later in process)

• Risk targeting – focussing effort on data items that have most impact on the trade statistics

• ‘Lean’ – cutting out actions that do not add (much) value to the overall process

Technical improvements

• Improved front-end tools for more efficient prioritisation of credibility interventions

• New SAS-based credibility parameter software, allowing faster update (e.g. reflecting price fluctuations) and more efficient application

• Introduction of an integrated risk database, providing risk assessment for each line of trade (comcode, trade, country), linked to and highlighting priorities via the front- end credibility tool

The scatter graph shows the data and the regression line for the model. The parameters for this model are as follows: A = 5.173 B = 0.328 S = 0.717

Rsq = 0.2656

COMCODE: 34060011 Candles

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Trader Contact

• Self-service education increased for EU data providers (Intrastat traders) via web and phone

• Use of email alerts to notify data providers of changes, recurring errors, due date, etc

• Improvements to e-declaration systems, leading to increased take up

• Desk based assurance of data provider declarations (targeting and phoning as opposed to visiting)

Joint Working between Statistics teams and Operations (Processing)

• Greater integration of data analysts and statisticians into data operations work (co-location, Risk and Quality groups, etc.)

• Evaluation / measurement of all process changes, improvements, targeting to understand impact, replicate, stop, etc

• Improved knowledge and understanding of the business by staff members, improved documentation and archiving. Supports greater staff flexibility, utilisation, motivation and business contingency

• Targeting of data provider risk as opposed to general assurance

• Use of improved management information and data analysis to prioritise manual interventions to the most significant data providers

• Improved reporting of multi-item data provider errors on one output, reducing confusion and allowing consolidated contact calls to rectify, reducing provider burden, etc

Joint Working between Statistics teams and Operations - continued

• Changes to our estimation methodologies • Estimation of Non response• Partial response • Non coverage (Below Threshold Trade)

• Improving the estimates for the effect of Missing Trader Intra-Community Trade (missing declarations) on trade in goods

• The requirements for a new IT System

In progress

Conclusions

• The improvements have shown the larger team now has a common goal and direction

• There is a greater working together with understanding of the aims and problems of both sides of the business

• The quality of the data is being maintained and compliance is improving

• While large scale investment in IT is still required, this series of small changes within our existing budget have lead to data quality improvements

Questions

1. How do trade statisticians measure the overall quality of the data and error rates?

2. How can we quantify improvements and assess the impact on data quality?