Expo London April 2005

1
E-Trading Standards

• www.localegovnp.org/etsn
• Welcome and introductions

2
Workshop Objectives

• Introduce the e-Trading Standards National
  Database
• Understand what it can do for you
• Demonstrate the system
• Explain about implementation, timescales, costs
  and links to the wider local government agenda

3
E-Trading Standards

• Background
• Project business benefits
• Workstreams
• Current progress and timescales
• Links with PARSOL, Valuebill, NOMAD and Working
  with Business
• Links with Consumer Direct
• Questions

4
e-TSN - Background

• Project idea from Trading Standards service
• Funding to develop through ODPM e-government
  agenda
• One of 22 National e-Government Projects
• Consumer Direct happening in parallel

5
Trading Standards National Database
6
Trading Standards National Database Benefits 1

• Cost reduction and efficiency savings
• Service improvements and added value
• Strategic benefits

7
Trading Standards National Database - Benefits
8
Trading Standards National Database Benefits 2

• Targeting of resources
• Improved Home Authority work
• More accurate information
• More effective investigations
• Better management information
• Reduced consumer detriment
• Improved customer satisfaction
• Better regulatory reporting

9
E-TSN Aims and Deliverables

• Design and deliver technical infrastructure for
  information sharing
• Develop and test a process for data clean up
• Work with Consumer Direct to ensure integration
  and coherence in sharing complaint data
• Produce toolkits
• Work on take up and roll out

10
Workstreams

• Data clean up
• Prototype development and testing
• Licensing products and business self-assessment
• Service Delivery Standards
• Toolkits
• Sustainability

11
Links with Consumer Direct

• Consumer Direct Vision
• Joined up data through e-TSN
• Single back office system adapter
• Joint discussions with back-office systems
  suppliers

12
Next Steps

• Implementation and Costs
• Action plan for take up
• - IEG funding
• Service planning
• Benefits Study
• Further Information
• www.localegovnp.org/etsn

13
Any Questions
14
Demo of system
15
e-Trading Standards National Project Data Clean
Up
16
Why clean data ?

• Existing data was audited and analysed
• Low quality data - reinforced by independent
  research
• Need to improve data quality and integrity
• Facilitating interoperability, exchange and
  sharing of data

17
Why clean data ?

• Other than e-TSN
• Data Protection
• Freedom of Information
• Local Data Exchange Protocols
• Government targets we have to clean the data we
  hold so we can share it within the Local
  Authority.

18
What is data cleaning ?

• For ETSN data cleaning is taken to be a range of
  generic processes to improve the integrity of
  data
• Matching
• Standardisation
• De-duplicating
• Purging
• Enhancement
• Provision of additional data

19
What data are we cleaning within ETSN project?

• Existing data from back office systems
• Business/company name
• Business Address
• Postcode

20
What additional data are we providing?

• Number of employees (total and site)
• Turnover
• SIC code and description
• Registration number
• Parent company
• Ultimate parent company
• Premises type
• UPRN
• Unique ID codes for subsequent cleaning

21
What additional data are we providing?

• Working towards providing details of traders not
  on existing databases

22
ETSN Approach

• Researched processes and suppliers
• Data cleaning requirements specification
• Invitation to tender
• Evaluation of responses
• Supplier selection Experian Intact

23
Methodology

• Automated/Bureau data cleaning
• Data selection from back office systems
• Export to Experian Intact
• Data cleaned
• Data returned in bespoke report
• Additional data provided
• Manual checking and verification
• Experian report used for manual checking and
  verification
• Accept verified data
• Make changes
• Additional data accepted
• Import into back office system

24
Export to Experian Intact

• Selection of data from back-office system
• Standardised method of delivery
• FTP via the internet using a delimited file e.g.
  csv
• Cleaning and matching
• Data is then matched against their sources of
  information including
• Experian National Business Database
• Companies House
• Thompson Directories
• Yell
• Small Office Home Office (SOHO)
• Business and residential addresses using
  Intelligent Addressing matching to NLPG

25
Receive report back

• Bespoke report back from Experian
• Format
• Report summary/audit report
• Guidelines on interpreting data
• Data views
• Schema (including explanation of codes used)

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27
Manual checking and verification

• Matched data
• universal acceptance of Experian data
• checking and accepting Experian data
• accepting TS data
• correction
• correcting addresses
• correcting the business company name (TS data may
  be more accurate or complete)

28
Make Changes

• Failed to match data
• accepting TS data
• manual checks of accuracy using usual methods of
  enquiry

29
Import of cleaned data plus added value

• CSV conversion from Excel
• Contract with back office systems to provide a
  connector IF needed.
• Import of original cleaned data plus additional
  data

30
Advantages

• Bespoke solution from Experian
• Data sources
• Report - Excel with macros
• Several views of matched data
• Schema
• Guidance notes
• Navigation aids
• Data manipulation

31
Advantages

• Maintaining the integrity of the data in back
  office systems
• Limiting down time of back office systems
• Editing records is quicker and simpler
• Aids manual checking processes
• Allows multiple editing
• Facilitating export and import of data by using
  CSV

32
Cost

• Automated/bureau
• Per thousand records - 250

33
Cost

• Manual process
• Cost of administrative and specialist staff help
  in the manual processes
• Manual checking and verification of data approx
  0.50 per record depending on depth of research
• Management of process

34
Any Questions?