Welcome Data Cleansing and Matching Workshop

1. Welcome Data Cleansing and Matching Workshop

2. The Agenda An Introduction to VisionWare The Fundamental Elements of Data Cleansing and Matching Case Study: Clackmannanshire Council Open Discussion The Benefits

3. About VisionWare plc Public Sector Pedigree 60+ Established Public Sector Clients Customer References CRM/SSA/ICS/Citizen Account/Smart Card Initiatives Rapid, Deep Integration Broad and Deep Integration Capability Back-end Legacy & Front-end CRM Systems/Applications/Data/Functions/Services Thought Leadership Products: MultiVue/relate/E-Forms Non-Prescriptive Trusted Data Customisable Framework Strategic Alliances Microsoft ITNET Parity SolidsoftDeloittes Capita

4. A Selection of VisionWare Public Sector Customers • HEALTH TRUSTS • Ayrshire & Arran • Fife Primary Care • Highlands Acute Hospitals • NHS Highland • Inverclyde Hospitals • Yorkhill • Lanarkshire Primary Care • Fife Acute • Peterborough • Royal Wolverhampton • West Suffolk Hospitals • Weston Area Healthcare • United Bristol • Great Ormond Street Hospital • North Cheshire • Croydon PCT • LOCAL GOVERNMENT • Aberdeen City • East Renfrewshire • Glasgow City • Moray • North Ayrshire • North Lanarkshire • West Lothian • South Lanarkshire • Renfrewshire • West Dunbartonshire • East Lothian • Clackmannanshire • Wansbeck • Leicestershire • Sutton • LOCAL GOVERNMENT • Midlothian • Merton • Newham • Croydon • Luton • Tower Hamlets • North Tyneside • Windsor & Maidenhead • Wiltshire • Blackburn with Darwin • Calderdale • East Sussex • Inverclyde • Cambridgeshire • Bedfordshire Consortium

5. The Fundamental Elements of Data Cleansing and Matching Evaluate the Quality and Quantity of Data Cleanse the Data Match the Data Maintain and Synchronise the Data

6. The Operational Challenge • The use and administration of data within Public Sector organisations has grown: • Electronic Service Delivery • Modernising Government Initiatives • Each vertical departmental system stores demographic data and information relating to their functional area • This creates silos of information across the organisation • We need to deliver services designed around the citizen NOT around the departmental function • We must therefore Join-Up Data to deliver Joined-Up Services • What underpins these initiatives? • Information Sharing • Trusted Source of Unified Data

7. Evaluate the Quality and Quantity of Data • Identity information is held within each of the organisation’s line of business applications • Each identity will vary in terms of: • Quality • Accuracy • Quantity • Need to be able to: • Report on the variance of both data quality and data quantity across the departmental systems • Match and rationalise the information

8. Evaluate the Quality and Quantity of Data: On a National Scale Scotland: 5,057,400 Annual demographic change factors: 52,395 birth registrations, 58,326 death registrations, 30,651 marriages recorded, 10,484 divorces recorded, 125,000 Annual address changes, Unquantifiable job and circumstance changes With these demographic changes on a yearly basis how can we ensure the quality of our data…? • This represents over 5% of the population. • In 2 years, at least 10% of data could be out of date • In 5 years, at least 30% of data could be out of date

9. P R O P E R T Y L E I S U R E L I B R A R Y T R A V E L S C H O O L L S P L A C E S S P A C E S O B J E C T S A S S E T S P E O P L E C H I L D R E N V U L N E R A B L E E L D E R L Y C A R E R S SmartCard Dataset CRM Dataset SSA Dataset ICS Dataset LLPG Dataset Evaluate the Quality and Quantity of Data: Modernising Government Initiatives Each Initiative generates its own dataset Existing LOB Applications do not participate and ADD to the problem Between 150-250 LOB Applications containing Customer Data Elements

10. Data Cleansing and Matching • Integration of various identities will invariably lead to a series of data contentions • Multiple names, multiple addresses, inconsistent dates of birth, incorrect (false) demographic information and duplicate information • This needs to be resolved before we can provide a unified view of trusted data relating to either a person or property. • Need to be able to: • Resolve data contentious issues • Aggregate all non-contentious information • Provide a composition that retains information of the highest quality and quantity by: • Matching the records • Merging the information • Managing the duplicated data

11. The Real Challenge: A Plethora of Systems, Silos of Information Identification of £1.5m of Benefit Fraud 580,000 records relating to 200,000 people 3:1 Ratio of Duplicates

12. Data Matching: Look at it this way… WHO THEN IS ANTONIA RITCHIE? Antonia Marie Pilaski Alias: Toni Pilaski Marie Pilaski Address: 33 2 Prince Regent Street EDINBURGH Antonia Marie Pilaski Alias: Toni Marie Address: 45 Dunfermline Av EDINBURGH Lives with parents Mark Baker Address: 24 6 Montgomery Street EDINBURGH Mark Ritchie Address: 24 6 Montgomery Street EDINBURGH Moves to own flat Antonia Ritchie Address: 24 6 Montgomery Street EDINBURGH Gets engaged Fiancée changes name Gets married & moves in with husband

13. Maintenance and Synchronisation • The maintenance of identity information is: • Time consuming • Inefficient manual process • Potential risk involved in latency of updates • Possible inconsistencies within the datasets • Need to be able to: • Implement a mechanism that enables information to be passed and shared between the departmental systems • Each connected application needs to be notified of any validated changes • The Benefits • Ensures consistent view of an individual • Level of data latency can be controlled • The risks of utilising redundant information is managed

14. The Fundamental Elements of Data Cleansing and Matching CUSTOMER CONTACT CHANNELS Mediated Access One Number Contact Centre S Y N C H R O N I S A T I O N COMMUNITY RELATIONSHIP MANAGEMENT (CRM) SYSTEM – FRONT END INTEGRATION DATA VALUE P O R T A L S O F F U N C T I O N A L I T Y TRUSTED DATA SCALING ACROSS THE LINE OF BUSINESS APPLICATIONS MIDDLEWARE – BACK END SYSTEMS INTEGRATION HEALTH DATA VOLUMES COUNCIL TAX EDUCATION SYSTEMS SOCIAL WORK SYSTEM CENTRAL HOUSING BENEFITS PLANNING POLICE GOVERNMENT SYSTEM SYSTEM AGENCY SYSTEM SYSTEMS SHARED INFRASTRUCTURE CENTRAL

15. Case Study Presentation Brian Forbes Modernising Government Strategy Manager Clackmannanshire Council

16. Open Discussion VisionWare plc Willie Clinton, Director Campbell McNeill, Consultant Clackmannanshire Council Brian Forbes, Modernising Government Strategy Manager Alexis Easton, Head of IT Services

17. Topics for Discussion • How do you change the culture to ensure that staff maintain quality data? • How do you measure data quality? • Do we need to define national standards for the Public Sector? • What are the difficulties matching citizen data with limited information? • What resources are required to match data?

18. How do you change the culture to ensure that staff maintain quality data? • The Structure of the Organisation: • Silos of information exists across departmental systems • Each departmental system holds demographic information about entities (person, property, assets) • Should each department manage their own data? • Should the organisation have a corporate-wide strategy? • Should we consider a Centralised Repository of Information, for example, The Citizen Account? • Data quality has to be improved by changing business processes and working practices

19. How do you measure data quality? • Data Quality • Data Quality = How accurate is the information? • Data Latency = How up-to-date is the information • Data Quantity = multiple systems, silos of information • Other areas to consider • Information Audit • Technology • Public Enquiry, at worst • Some systems have more valuable data than others • How can these systems support the “weaker systems?”

20. Do we need to define data standards • We have existing standards: • eGIF • Citizen Account Dataset • BS8766 (Name) • BS7666 (Addressing) • BS7799 (Security) • Data Protection • How do you stop standards from stifling innovation or impacting for example, Data Protection

21. What are the difficulties matching citizen data with limited information? • Limited Information • Does the organisation know what information they hold? • Is forename, surname and address limited datasets? • Does limited data come from the imposition of Data Protection and Information Sharing? • Leverage the best of what we have • The process has got to be evolutionary not revolutionary • Dependent upon the Quality, Quantity and Latency of Information

22. What resources are required to match data? • People and Technology • Manual Process • Build a level of trust in the data • Automatic Process • Probalistic matching to deterministic matching • Parameters set by the organisation

23. The Benefits Data Management Trusted Data Source Joined-Up Services Multi-Agency Working

24. Some Case Study Examples

25. MultiVue is the Key to Joined-Up Data VisionWare specialises in the provision of trusted data with MultiVue Identification Server, an enterprise-wide data integration tool.  Public Sector departments and multiple agencies can now share accurate and reliable information on every citizen.

26. Thank you! Willie Clinton Director VisionWare plc 0141 285 7150 willie.clinton@visionwareplc.com www.visionwareplc.com