What is Outcomes Star data? #
Star data is the data captured when any version of the Outcomes Star is completed. This may be done by the person being supported with a practitioner or if not possible, by either on their own.
Each version of the Outcomes Star consists of a number of relevant outcome areas presented in the shape of a Star. Each outcome area is broken down into a numerical scale, with each numerical value defined as a step or stage in a Journey of Change. The Journey of Change breaks down how complex change happens, creating a consistent and objective framework by which to measure the changes somebody is making in their life.
The Star and the Journey of Change do not purely measure the severity of an issue – they measure the relationship somebody has to issues in that area of their life and what actions they are taking to cope with or resolve those issues. Together, a practitioner and the person being supported collaboratively complete a Star. When, after a period of time, the Star reading is reviewed, and another reading completed, a rich source of co-reported, distance-travelled data relating to key outcome areas is created.
Star data can help to provide insights into what is happening in service delivery at a range of levels of an organisation. From the front-line where a service manager might review the Stars completed by a single practitioner in depth, to senior management levels where a senior team might review aggregated data from hundreds or thousands of Stars. Whilst the approach might differ at these different ends of the continuum the purpose is always to learn and to improve outcomes for service users.
Context, curiosity & learning #
The clearer an organisation is about its objectives and the questions it is seeking to answer, the more helpful the Star data is likely to be, although Star data on its own will not provide final answers. It is best seen as one piece of a puzzle, and it is only when combined with the other pieces that a full picture will emerge.
The other pieces of the puzzle include:
- Information about the service, what support it provides, who it supports and any known barriers
- The expectations of the service and what it aims to achieve (see forecasting section for more information)
- The contextual information available. For example, knowledge about the local economy and available resources
- Narrative from practitioners and the people being supported to provide qualitative data.
With these other puzzle pieces, Star data can support a process of on-going learning and development:
The importance of effective Star implementation #
While the benefits of using the Star for the people being supported can be achieved immediately following training, the data benefits require effective Star implementation across the service or organisation.
To achieve meaningful and high‑quality Star data, organisations need:
- Appropriate Outcomes Star training for Practitioners
- Active engagement from Service Managers at all stages, including reporting at different levels
- Clear internal guidelines for how the Outcomes Star is used across services
- Outcomes Star Online or an equivalent fit‑for‑purpose software system that:
- Is accessible to all relevant staff
- Captures the data required for reporting
- Is used confidently and consistently
- Has appropriate licensing and approval in place from Outcomes Star
- Regular, structured reporting
- Ongoing quality assurance, led by Service Managers
Using Star data well is a whole organisations project and one that requires engagement and an understanding of what the Star is, how it works and what it measures at all levels.
Star data is most valuable when it’s used close to where it’s collected. When practitioners and the people they support use the Star together, and when Service Managers review that data, there’s rich context to help make sense of what the readings really mean. At this level, detail and nuance are still visible.
Star data can also be helpful at organisational or strategic levels, but the further it moves from the point of collection, the more care is needed in interpreting it. Patterns should be explored alongside other data and informed by conversations with practitioners and managers who know the work and the people behind the numbers.
Regular use of Star data also strengthens data quality. When Team Leaders or Service Managers routinely review Stars, they can spot issues early — such as Stars being completed too late or readings that don’t reflect the scale descriptions — and offer support to improve consistency. This shared use creates natural checks and balances, making the data more reliable and useful at every level.
Percentages vs means #
We generally discourage the use of ‘average Star readings’ as summarising Star data in this way can mask the nuanced change that is taking place, making it difficult to use the data for learning and improvement and lead people to incorrect conclusions. This is especially the case if readings are averaged across all outcome areas to create a whole Star average.
When Star data is summarised using a mean average:
- Average start and end points in a Star area across service users may not indicate where most people start and end
- Information about the variation in Star readings is lost because the mean does not tell you how people are distributed on the Journey of Change
- The data on start and end readings is ‘flattened’ because high and low readings balance each other out
- Positive and negative change will balance each other out, which can look as if little progress is being made within a service even if many people are progressing
- Information about ‘maintenance’ of a Star reading is lost and appears as a negative thing (i.e. maintaining a reading at the top of the Journey of Change is positive but appears as 0 change in the average, reducing the average change across service users).
Percentages are a much more useful metric when analysing Star data.
Including narrative for meaningful picture #
Practitioners and Service Managers can provide valuable narratives to make sense of Star readings at an individual or cohort level. For example:
- Why readings may have moved backwards due to circumstances changing
- How the scope or funding of the service affects the change that is possible in a particular outcome area
- How external factors such as changes in policy, housing or employment opportunities may have affected start points or change
- Why one service is showing different start points or change on the Star than another – e.g. due to the geographic area, the nature of referrals or the availability of different services
This type of contextual information, presented alongside reports – whether as case studies or broader cohort level information, will greatly improve the meaningfulness and learning that can be derived from the data.
Reporting against expectations #
One of the most useful ways to use Star data is to report against the services expectations. Using Star data in this way allows an organisation to ask better questions of the data and helps them to learn from their Star data. Because each service using the Star has its own context, remit and approach, it makes sense that each service needs to set their own expectations around their Star data. It is possible to apply the same approach to Star data at different levels, from whole organisation to a specific service, caseload or even an individual service user.
By reporting against expectations, a service can:
- Highlight where the data is different to those expectations.
- Use any differences as a starting off point to try and understand why this might be.
- Pinpoint any implementation issues with the Star in your service
- Highlight broader questions around the people you are supporting and the support you are providing.
- Use Star data and learning to continue to improve outcomes for the people being supported
| What to look at | What you can learn |
| Starting points | Whether first Star readings are a good baseline for your Star data – or whether a more realistic baseline is achieved on second Stars when a practitioner has started to develop a relationship with a service user. Whether people are entering your service with the expected and appropriate levels of need, perhaps relating to the funding you receive or the remit of your commissioned service. Whether the initial Star readings seem accurate (ie if practitioners are completing first Stars correctly). |
| End points | Whether the final/review Star readings being recorded seem accurate (ie if practitioners are completing final/review Stars correctly). Whether people leaving your service have achieved the expected outcomes in key outcome areas. |
| Key milestones in the journey | Whether people are making the progress that you would expect and if they are likely to achieve the end points you think are likely across different outcome areas. Not all areas may have ‘interim’ milestones – it will depend on the nature of your service and the people you support. |
Reporting with multiple Star versions #
It is common for an organisation to use more than one version of the Outcomes Star when supporting a range of people with different needs. However, when reporting on the readings in completed Stars, it is not possible to report across different Star versions. This is because different versions of the Outcomes Star are designed for people with different needs, underpinned by different Journey of Changes and include different Outcome Areas.
However, there are some high-level trends that can be reported across different versions of the Outcomes Star.
Looking at starting points
The percentage of people with significant needs in at least 1, 2 and 3 outcome areas.
| At least 1 area at 1-3 | At least 2 areas at 1-3 | At least 3 areas 1-3 | |
| All clients (150) | 87% of clients | 80% of clients | 73% of clients |
| Homelessness Star (100) | 80% of clients | 75% of clients | 70% of clients |
| Drug and alcohol Star (50) | 100% of clients | 90% of clients | 80% of clients |
NB. The % of ‘All clients’ is skewed towards the Star with more client readings (i.e. the Homelessness Star above). To calculate the likely level of need given equal numbers of readings for each version, take the average % (i.e. 90% for at least 1 area at 1-3).
Looking at change
Looking at the number of Outcome Areas in which each person progressed and calculating the proportion of all people who moved forward in at least one, at least two and at least 3 outcome areas:
| At least 1 area moved forward | At least 2 areas moved forward | At least 3 areas moved forward |
| 86% | 72% | 55% |
NB. When the versions of the Star differ in whether they have a 10- or 5-point scale, ‘moving forward’ should be defined as moving forward by at least one Journey of Change stage (e.g. moving from 1-2 on a 10-point scale would be counted as no change).