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Improving Data Services

Improving Data Services. Sue Carty Quality, Planning and Performance Manager Durham County Council - CYPS. “ Proud of our past – Confident about our future”. Durham - Statistics. Average size County – total population 500,000 22% in 0- 19 age group

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Improving Data Services

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  1. Improving Data Services Sue Carty Quality, Planning and Performance Manager Durham County Council - CYPS

  2. “Proud of our past – Confident about our future”

  3. Durham - Statistics • Average size County – total population 500,000 • 22% in 0- 19 age group • Clear variations in deprivation. 4 out of the 7 districts in Durham are in the worst quartile on the Indices of multiple deprivation. • Low number of ethnic minority residents – approx 0.3% are from an ethnic group – largest group is travellers.

  4. The Children’s Trust- Achievements’ • JAR 2007 – “Children’s Trust have shown excellent leadership” and “prioritisation by the partners is excellent” • Beacon Status for School Improvement and “support for school improvement is outstanding” – JAR • “Durham has a convincing track record of improvement; senior officers articulate a clear focus on priorities and offer a detailed understanding of service performance”. APA 2007 • “Agreed priorities are based upon a detailed analysis of needs which has ensured that at both local and county level these are recognised and well understood.” – APA 2007

  5. Objectives of Data Services Review • To produce a comprehensive summary of data sources available across CYPS. • To evaluate the use of this data and how it contributes to strategic thinking. • To determine whether current use of data drives improvement in outcomes for children and young people. • To develop a set of options for future delivery of data services.

  6. Commissioning Framework • Audit Performance • Audit Need • Audit Current Service Provision • Develop Ideal Type • Identify Disparities • Specify New Service Type and Agree Change

  7. Tools and Activity • Interviews- SLT & Key Stakeholders and Providers of Data • Desktop Research - identify types of data collated • Best Practice in other LAs • Best Practice in Durham – performance Days, DTSSI • Identified a critical friend • Undertook a case study to assess whether use of data leads to improvement in outcomes • Visioning Workshop

  8. Data Rich V Data Intelligent THE CASE STUDY • Identified the last 6 children that entered CIN service -. (between 2nd and 8th June 2008) • No partnership data e.g. health • Identified any data relating to these children across all CYPS • Included any data we held on the family in addition to the child • Analysed data by Services and by Families.

  9. Data Rich V Data Intelligent • All families were white British. • 5 of the 6 children were under 5 year old. (Some had siblings) • 2 X Easington, Sedgefield, Chester Le Street, Bishop Auckland, Wear Valley

  10. Analysis - Services • Specialist and Safeguarding Services - 4 of the 6 families had previous involvement with this service. • Surestart – involved with 3 of the 5 families who had children under the age of 5 before they became CIN. • Connexions – involvement with 3 or the 6 families. (50%)

  11. Analysis - Services • Youth Engagement Service - involved with 2 of the families. • Adult Services - 1 case currently open to Adult Services (prior to June) • Achievement Services - Data on 4 of the families

  12. Analysis - Family • Family A - in the top 10 SOAs for Deprivation. Known to us because of domestic violence and assault. Living in area with higher prevalence of anti social behaviour. Involvement with 3 services - SASS, YES and Connexions. Previous referrals on parents for same reason. Father conviction for sexual offence. No Surestart involvement - child is under 5.

  13. Analysis - Family • Family B - Living in an area where anti social behaviour is more prevalent. Referral for domestic violence. The parents of these children were also in the system themselves as children of parents with a history of domestic violence. Older sibling subject to SEN school action plan, and had 10 days absence last year. Sibling achieved 3 at KS2 and 5 at KS3. Connexions involvement 2006. No data available re: Surestart - child under 5. Involvement with 3 services - SASS, SEN, Connexions

  14. Analysis - Family • Family D - Referral by Police - sexual abuse older sibling, 2 older siblings on free school means, both subject to SEN plan, At KS1 – sibling achieved 1c and other sibling had no results for KS1 or 2. One sibling involved with Connexions in Jan 08 and 1 identified for YIP but did not engage. 80 days absence for one sibling, 37 days absence for the other. Involvement with 5 of our services. Teenage parent - Age 16.

  15. Analysis - Family • Family E - Involvement with 3 services. Both of older siblings made allegations of physical abuse. Mother previous in system as a child for alleged physical abuse of against foster carer. Surestart involved with family for many years. Teenage parent - Age 17.

  16. Analysis - General • No CAF’s were completed for any of the children • From the services considered each family shows involvement of between 2-5 services prior to the child becoming a CIN. • 2 of the families fall into our KIP - teenage pregnancy • From data gathered – 2 of families known to services since 2000 and 2002.

  17. Key Findings from Case Study • Data rich but could improve data intelligence. • Analysing by Service points to questions about service & promotes a ‘blame culture’ or it wasn’t anything to do with me. • Analysing by family points to questions around family and focuses more on partnership work and outcomes. Integrated Data Delivery = Integrated Service Delivery = Improved Outcomes

  18. Key Findings from Review • No systematic and common understanding of what data is held across the service • Analysis is completed on a service basis rather than looking at the range of data across CYPS. We are still operating in silos and therefore no one has the full picture and patterns across service areas not transparent. • Data knowledge and expertise is service specific. • Roles of data professions are being duplicated across the service. This leads to inefficient use of resource and lack of coordination for data requests.

  19. Key Findings from Review • The production of data is driven by National requirements and therefore we are much more data rich in some areas of children’s services than others. E.g. Achievement services • Timing and validation of data is an issue – data not produced in real-time and therefore we take action based on old data. • Barriers across partner agencies in sharing data

  20. Key Findings from Review • The lack of a joined up approach to data management creates issues for services to effectively plan, commission and performance manage. • Current arrangements do not meet needs of Children’s Trust and limit development of CYPS as an integrated service.

  21. Our Vision for Data Services • A service that is child centred. • A service that shares knowledge and expertise and uses a common language. • Focus on outcomes and not individual data sets. • A service that assists in helping us plan, perform and commission services for children individually and strategically. • A service that understands the complete picture and is able to react flexibly to change whilst meeting National Requirements, CYPS requirements and the requirements of the Children’s Trust.

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