Standard Safeguarding Dataset (SSD)

Briefly describe the initiative/ project/service; please include your aims and objectives 

The Standard Safeguarding Dataset (SSD) is a transformative middleware solution designed to enhance how Children’s Social Care (CSC) data is accessed within and across local authorities. The SSD integrates with existing infrastructure and case management systems (CMS) to unlock access to a broader range of raw reporting data, allowing for more comprehensive analysis, improved scope for benchmarking, and enhanced collaboration across local authorities. 

 

The SSD solves longstanding challenges in data-sharing and insights by providing standardised access to key data points in Children’s Social Care, no matter which case management system is in use. This not only enables more granular analysis, but also supports cross-authority collaboration, predictive insights, and business intelligence. The project has involved extensive sector-wide collaboration to define and agree the data-points, leveraging YAML-based configuration, open-source platforms and scripting to aid iterative dataset design based on ongoing local authority input. The approach ensures that the SSD is flexible, adaptable, and responsive to the evolving landscape of Children’s Social Care data management. 

 

Key Objectives:

• The sector-wide standardisation of key Child Social Care reporting data points 
• Empowering an inter-authority data driven community 
• Enabling tools and insights sharing within the sector, towards improving child level outcomes 
• Improved scope for identifying and understanding differences in data recording between local authorities 
• Creating a known, national CSC data environment for script/tools development and sharing
• Improved platform for all local authorities to gain improved benchmarking insights 
• Create a data-middleware towards the ethos/framework of develop once, use 153 times 
• Reduce the development overheads for future data use via API connectivity 
• Reduce constraints/dependence on CMS vendor proprietary reporting and data-level changes. 

 

What are the key achievements? 

The SSD project has reached several major milestones, demonstrating its value and potential for the sector. 

 

Mapping the core and high-value data points critical to Children’s Services intelligence work. The mapping of these 3401 data points underpins the SSD to support and enhance analytical and business intelligence capabilities within local authorities. The SSD was designed as a broader dataset that extends beyond existing statutory requirements, providing both more granular insights into Children’s Social Care data and an extended time-frame beyond Statutory Returns. 

 

Convening 97 stakeholders from 38 local authorities to drive the dataset design. These participants represented a wide range of roles, including data leads, team managers, QA managers, and senior strategic leads. Their input helped define the most pressing data challenges and opportunities for improvement, ensuring the SSD meets the diverse needs of local authorities. 

 

Reaching beyond LA tech communities to engage the wider academic research community (CSDUG) and social work experts (BASW). These workshops explored the current limitations in CSC data reporting and identified difficult-to-tell data stories that could benefit from the enhanced data accessibility provided by the SSD. 

 

Improving key data-point visibility, and providing increased human readable/navigable routes beyond internal CMS limitations and statutory reporting structures.; The SSD improves access for local authorities to conduct more detailed and meaningful analysis. 

 

Providing a flexible and scalable approach for further linking additional non-CMS data sources highlighted as of most value through the consultation process, via the use of any of the following: 

• Common Child Identifier 
• NHS Number 
• Unique Pupil Number 
• Identifiers from Youth Justice, Adult Social Care, and Early Help Systems 
• Scope for other locally useful identifiers 

 

This forward-thinking approach ensures that the SSD can accommodate new data as it becomes available, allowing local authorities to integrate a wider range of information into their safeguarding datasets. 

 

Achieving sector buy-in, with 24 local authorities already involved in the phase 2 deployment testing. Some local authorities are using the SSD as part of their Children’s Services data warehouse redesign – taking direct influence from the design of the SSD- or testing the SSD with the aim of enabling live(or near-live) dashboard reporting, reducing reliance on Excel-based manipulations and streamlining data reporting processes across those data reliant teams. 

 

How Innovative is your initiative? 

The Standard Safeguarding Dataset is a first crucial step towards cross-system data compatibility for local authority safeguarding services. 

 

Middleware: Acting as middleware, the SSD removes constraints traditionally imposed by CMS providers or statutory reporting structures, enabling (data-)teams to access raw data points within a ‘plug and play’, cleaner, LA-defined, pre-formatted reporting structure. accessible/streamlined structure empowers authorities to conduct richer analyses, improve decision making, and enhance outcomes for children in need. 

 

Flexibility and Transparency: The YAML-based configurations provide a transparent and flexible mechanism for local authorities to tailor some aspects of the extract process to their specific requirements. This allows for some bespoke fitting without compromising the overall standardisation. We used YAML in-part due to it’s more human-readable structure (vs such as XML) enabling easier collaboration/onboarding, but also offers greater scope for future work and ease of publishing into other formats. 

 

Workflow and Collaboration: The use of GitHub and Python to publish project/SSD changes as part of the CCI process, ensuring that updates can be implemented efficiently. Python scripts recreate both the YAML objects and the entire project front-end based on local authority change requests. Changes are reviewed and published via a public version-control process. This allows for seamless updates to the project front-end, ensuring that local authorities have access to the most current dataset and change logs. All details on the project website (https://data-to-insight.github.io/ssd-data-model/) are automatically re-published using a single Python script, providing transparent, rapid updates and easy access for all authorities. This approach we believe fosters continuous collaboration and iteration, positioning the SSD as adaptable and we hope sustainable solution that can evolve alongside wider local authority/sector needs. 

 

Scalability: The SSD’s scalable architecture ensures its adaptability for future data requirements, including linking additional non-CMS data sources. 

 

Less data mature local authorities can now benefit from having both the SSD structure and pre-built tools from D2I and new tools as they are developed and shared by sector colleagues, supplied to them with zero cost. This has an immediate impact for such as Freedom of Information Requests, where a collaborative effort by perhaps larger data teams or a single analyst can address the FOI, and be immediately shared to others(to be available as required); applying the collaborative concept of ‘develop once, use 153+ times’. 

 

The impact of shared tools extends beyond the SSD structure, to those developed on top of the SSD, in such as Python, PowerBI or Tableau. Additionally providing further basis for inter-LA conversations. For example, a simplified pathway analysis visualisation(incl. code to create it) built on the SSD from LA1, can be shared to data-mature LA2 with the confidence that the under-pinning data features already exist and this will work out-of-the-box. Potentially saving multiple people-hours, on each occasion. On review LA2 adds refinements and returns the improved work confident it will again ‘just work’; concurrently sharing it with colleagues at LA3 and LA4 who have just sent over their visualisation tracking Case Worker durations for a particular cohort for some feedback on their tool code. They’re not sharing data, they’re sharing and improving local tools, experience and observations, developing them into sector tools, experience and observations. 

 

What are the key learning points? 

Replicability and Scalability – The SSD has been designed to facilitate wide adoption, with support from DfE to explore deployment to all statutory safeguarding authorities in England. Learning from the project is informing DfE’s wider work on improving case management and data flows in the sector. 

 

The SSD’s middleware concept ensures it can be implemented on top of existing CMS systems without requiring significant changes. This makes it easy for local authorities to adopt the SSD, and gain highly refined/LA focused access to existing data. 

 

The YAML-based configurations allow for customisation, enabling adjustment to be made to some aspects of the extract process to specific LA needs whilst still maintaining the overall standard. This offers additional flexibility towards the SSD being further adapted to a variety of case management system instances. Currently this workflow is in place for one CMS system with ongoing development towards enabling this for all. (Appendix [i]) 

 

The SSD’s link table towards connecting additional data sources (e.g., CCI, NHS Number, UPN) ensures that it can evolve as local authorities expand their data capabilities, or to maximise available data in more connected LA systems where the additional data is already within accessible systems. This makes the SSD a solution with scope to grow alongside the LA/sector’s changing needs. 

 

With 24 local authorities agreed interest in testing the SSD, and others redesigning their data warehouses influenced by the SSD, the project demonstrates increasing sector impact. The integration into such as East Sussex’s Tableau workflow further showcases the SSD’s capacity to both adapt, potentially support real-time reporting and improve data management efficiency across authorities. 

 

We believe the concept has further cross-services applicability that has yet to be realised. The project presents an achievable vision of interoperable, collaborative data systems, for a market which has historically offered closed, whole-system solutions.

 

Key Learning Points 

As the Standard Safeguarding Dataset (SSD) continues its development and deployment, several key learning points have emerged 

 

By acting as a middleware layer, the SSD provides local authorities access to data that for some might have been previously constrained by CMS systems and/or data-depth and reduced timeframes limitations of statutory reporting. For some less data mature, or single-team authorities, access to key data points now has reduced overheads and comes pre-structured to meet LA need rather than CMS dictated. 

 

The input from 97 stakeholders from 38 local authorities during the design process has been instrumental in shaping the SSD’s structure. Their feedback has ensured that the SSD meets the practical needs of diverse local authorities while remaining adaptable for future requirements. This collaborative effort has offered wider, shared ownership of the project and it’s desired outcomes. 

 

The use of GitHub and Python scripting to automate some of the CCI process has significantly reduced wider project centred administrative burdens, ensuring that changes for review can implemented quickly and as a result, all stakeholders have access to the most current version of the dataset/suggested changes enabling localised impact assessment before agreed changes are formalised (Appendix [ii]). Github is long established within the software development sector, and we see the SSD deployment as a valid stepping stone to encouraging it’s wider use by local authorities. 

 

The SSD’s ability to integrate non-CMS data sources, positions it as a long-term solution for safeguarding data management. Such flexibility will allow the dataset to evolve alongside local authority needs, making it a scalable and future-robust tool. The project’s script driven internal-workings is the realisation of more than just proof-of-concept scaled change management for some areas of the project. With some of the CMS systems, there remains a complex set of problems to address before this scaled change management is realised for all, and we’re working on those. 

 

The SSD has been developed to fit into most LA’s refresh processes, with a low runtime-cost overhead and footprint (typically 12mins+500MB footprint for LiquidLogic/SystemC). Or, with an adapted implementation method with such as the East Sussex team towards live reporting. Which when combined with integration into their Tableau workflow, highlights potential for exciting further work and ability to potentially pivot the project into real-time dashboard reporting, streamlining data processes and reducing reliance on more static/manual data manipulation (e.g. Excel) by harnessing recent tools (e.g. PowerBI). 

 

Additional Comments 

Feedback from LAs, from those who interact directly with the local data is extremely powerful towards improved understanding of sector data value. The SSD has a focused set of intended outcomes, however indirectly it is already a catalyst for many further conversations around data capture, data quality differences and wider potential insights towards ensuring children’s outcomes. As deployment increases, so too will the impact of such further conversations be truly realised.