The big common themes across communities, schools, the voluntary sector and public services involve complex issues regarding the challenges of creating a safe environment and local communities and services systems that enhances life chances.
I have been working on applying learning about new data analysis tools and approaches using ‘system dynamics’ to make predictions and ‘artificial machine learning and optimisation’ to reveal hidden patterns and opportunities and utilise every drop of knowledge and data to get things right and adapt when necessary. These methods can be used to manage capacity in a local authority service area, for example, to prevent violence and violent crime, such as murder.
Insights are generated that equip local partnerships with greater confidence to invest in preventative approaches, putting in place a mix of changes to policy and practice to tackle issues earlier, together with tools to track the required impact, and, further, to lower the rates of entry to specialist service systems and to know quickly when to adapt to changing demand. The methods help quantify the costs of fragmented services and help reveal how these can also cause damage. They also train leaders and managers to attend to the hidden dynamic indicators in systems that can transform performance and cost.
’System dynamics’, ‘Artificial machine learning’ and ‘Optimisation’ methods have been used in the risk and financial services industries for some time. However, because they rely on the availability of industry knowledge, data and information, they are also typically a secret ingredient. A recent Nesta report confirms the methods are particularly useful for children’s services because much of the work of commissioners or front-line professionals, including teachers and support workers, etc., involves complex decision making with lots of information which is increasingly available to work with and share.
Seven Tips for building Data, Evidence and Insight
How can partnerships strengthen the use of data and information? Nesta identifies that partnership group interaction with data can lead to tangible improvements. The challenge for local services is to organise multi-agency partnership group interaction with data and relevant information building and sharing. This is not just a technical challenge, it is mostly about creating strong leadership alignments and effective partner relationships.
The Seven Tips
- Use a problem-oriented mind-set
- Integrate data into a data warehouse to enable deeper analysis and use
- Enable data sharing through use of case-oriented information governance protocols
- Support the use of data from the top
- Invest in the data science capacity needed to perform analysis and integrate large data sets
- Take an Agile approach to working with data
- Ensure that infrastructure enables integration of data and analysis
- But with so much data available, what can you do to improve outcomes? How can you collaborate to achieve this? How do you aim to do this through multi-agency partnership?
1. Use a problem-oriented mind-set—data are not in and of themselves useful, but using data analysis to build and test hypotheses or solve problems creates value.
- Start with awareness
- Here are some clues:
- You keep having the same conversation over and over again.
- You talk and no real change happens.
- Frustration and tensions are growing in your teams and organisations.
- Build a shared definition or picture of the problem
- What causes the problem? Dig deep.
- What is the scope of the problem? Go wide.
- What happens if the problem remains unsolved? Get insight.
- Invite perspectives and engagement
- Get different views to gain a richer, more accurate perspective.
- Converse with others. It may be children impacted by the problem. It may be professionals. It may be leaders.
- Ask how other industries handle similar situations.
Speech Language and Communication, Special Educational Needs, Disability and Neuro Disability screening in Youth Justice services in England and Wales is beginning to show that understanding underlying unmet needs are significant individual factors in reducing persistent and high-risk offending behaviour. We are now beginning to show how information about life experiences indicates specific areas for reinvestment in earlier individual support and how these can be dynamically calibrated against reduced risk and the community services costs of abuse, child sexual exploitation, knife and drug crime and murder, etc.