1. Data Cleansing and Matching Welcome Data Cleansing and Matching Workshop

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

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

4. Data Cleansing and Matching 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 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 Aberdeen City East Renfrewshire Glasgow City Moray North Ayrshire North Lanarkshire West Lothian South Lanarkshire Renfrewshire West Dunbartonshire East Lothian Clackmannanshire Wansbeck Leicestershire Sutton A Selection of VisionWare Public Sector Customers LOCAL GOVERNMENT Midlothian Merton Newham Croydon Luton Tower Hamlets North Tyneside Windsor & Maidenhead Wiltshire Blackburn with Darwin Calderdale East Sussex Inverclyde Cambridgeshire Bedfordshire Consortium 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. Data Cleansing and Matching 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. Data Cleansing and Matching 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  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. Data Cleansing and Matching 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  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. Data Cleansing and Matching 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…? 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 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. Data Cleansing and Matching LEISURELEISURE LIBRARYLIBRARY TRAVELTRAVEL SCHOOLLSSCHOOLLS SmartCardDataset VULNERABLEVULNERABLE ELDERLYELDERLY SSADataset Evaluate the Quality and Quantity of Data: Modernising Government InitiativesICSDataset CHILDRENCHILDREN CARERSCARERS CRM Dataset PEOPLEPEOPLE PROPERTYPROPERTY PLACESPLACES SPACESSPACES OBJECTSOBJECTS ASSETSASSETS LLPGDataset 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  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. Data Cleansing and Matching 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 Cleansing and Matching Data Matching: Look at it this way… 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 Mark Baker Address: 24 6 Montgomery Street EDINBURGH Mark Ritchie Address: 24 6 Montgomery Street EDINBURGH Antonia Ritchie Address: 24 6 Montgomery Street EDINBURGH WHO THEN IS ANTONIA RITCHIE? Lives with parents Moves to own flat Gets engaged Fiancée changes name Gets married & moves in with husband

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

15. Data Cleansing and Matching Case Study Presentation Brian Forbes Modernising Government Strategy Manager Clackmannanshire Council

16. Data Cleansing and Matching 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. Data Cleansing and Matching 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?  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. Data Cleansing and Matching 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  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. Data Cleansing and Matching 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?”  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. Data Cleansing and Matching 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  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. Data Cleansing and Matching 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  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. Data Cleansing and Matching 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  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. Data Cleansing and Matching The Benefits Data Management Trusted Data Source Joined-Up Services Multi-Agency Working

24. Data Cleansing and Matching Some Case Study Examples Customer CRMCitizen Account Data Management Master Address Database ChildrenElderly Clackmannanshire    East Lothian    Fife Constabulary   Glasgow City Council    Inverclyde   Midlothian   North Ayrshire   Renfrewshire   South Lanarkshire  West Dunbartonshire  West Lothian      

25. Data Cleansing and Matching 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. 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. Data Cleansing and Matching Thank you! Willie Clinton Director VisionWare plc 0141 285 7150 willie.clinton@visionwareplc.com www.visionwareplc.com