Social Investment Series Blog #2 - How can I use data and analytics to better understand the people I am serving?
Providing better outcomes and improved services for communities is a core goal of Social Investment. But in order to do this effectively, we need to understand the people we aim to serve.
So how can we build this knowledge?
One way to do this is through data and analytics. Data and analytics are valuable tools that allow us to establish a comprehensive understanding of the needs and characteristics of a given population. This understanding is essential for delivering services that are effective, efficient and truly support better outcomes for our communities.
In this blog post we explore the different data and analytic approaches that can be used to better understand the communities we serve. Keep reading to learn more!
First step? Figuring out what you need to know and why
By understanding the communities we serve, we ensure that new interventions give us the best results for our investment. And for existing services, understanding people and their needs allows us to allocate resources to where they will make the most impact.
So, where should those who want to learn more about the people they serve begin?
As an investor or provider, you should first establish the scope of the population you wish to serve i.e. who are the people you really want to focus on?
Being targeted not only ensures that the support you provide marries up to the amount of investment available, but also helps you focus interventions on people who are most in need.
How you approach this will depend on what your goals are, what existing knowledge you have, and how you plan to utilise this knowledge to support your target population.
For instance, some investors and providers want to understand the intersecting needs that exist within a population. These investors and providers may work with communities where focusing on specific needs in isolation is ineffective and therefore want a holistic picture of the barriers to success these communities face.
Other investors and providers might want to understand where future needs or costs to the system exist so they can focus on preventative strategies.
Others may want to ensure the scope of their support is aligned with existing resource constraints.
Often, investors and providers will start with a general desire to help a specific population or address a certain outcome. For example, an organisation might say “I want to support rangatahi in Auckland to improve their educational outcomes.”
This is where being targeted is useful. Techniques like data segmentation can help narrow the scope through asking probing questions such as:
“Are there any unique characteristics of youth who should be the focus of this support?”
“Should a future service be offered Auckland-wide or more focused on specific suburbs?” or
“Should we address all educational outcomes – or be more specialised by looking at specific NCEA papers or trade or university achievements?”
Often, investors and providers want to address gaps in the systems or tackle unmet need. This is a big task and given its importance, we will look at how to address unmet need in our next blog post.
Want to narrow the scope of your intervention even further? Take a look at the future!
Sometimes when trying to narrow the scope of an intervention to a more targeted need base, it can be useful to predict who will likely experience a certain outcome in the future.
Predictive models can help you answer these questions by quantifying the relationship between key indicator factors (i.e., attributes, localities, etc) and outcomes.
By using these techniques, you can uncover patterns that influence success (for example how education and income affect health outcomes) and better understand where needs intersect.
What approaches are available for a more in depth analysis?
Along with predictive models, there are other analytical methods that can be applied to understand a population.
Descriptive Analysis is ideal for reporting summary metrics like service usage to confirm to funders a service was provided, or for population demographics.
Comparative Analysis is useful for examining differences between groups. For example, comparing outcomes for rangatahi vs kaumātua.
Inferential Models are useful for understanding underlying relationships in your data
Simulations can forecast long-term outcomes when changes take time to manifest. This is especially useful for running ‘what-if’ hypothetical scenarios.
Causal Analysis: For evaluating cause-and-effect relationships, such as the impact of an intervention.
The type(s) of analysis you choose will depend on your goals and your preferred level of rigour. This will likely require some degree of combining data sources from different domains.
Risks, challenges and considerations - what to keep in mind when carrying out an analysis
There are a wealth of benefits in using data and analytic techniques to learn about your population. However, it is also important to recognise the challenges and limitations associated with this.
Some important things to consider include:
Deficit-Based Approaches: The majority of data already collected (including data often used for these types of analyses) focuses on short comings rather than individual or community strengths. We recommend balancing any needs analysis with a strengths-based perspective to derive more empowering solutions. For an example, check out our OHI Data Navigator work where we aligned our approach with Māori data sovereignty principles to avoid a deficit lens.
Complexity: Data analysis is a complex process and can lead to incorrect conclusions if not handled carefully. For instance, descriptive statistics from surveys may show misleading trends if uncertainty and non-respondent bias is not accounted for. We have seen this issue when analysts use surveys such as the General Social Survey, but do not correctly take into account confidence intervals. We recommend seeking additional support here if your team lacks in-house technical expertise.
Combining Data Sources: While it is possible to combine data from multiple sources, this often requires significant effort. For example, one of our clients used Stats NZ’s Integrated Data Infrastructure (IDI) to analyse the broader impact of their services by loading the data they held into the IDI. This required navigating a rigorous data load process, which we supported so that the client could safely and ethically complete this process.
Effective social investment begins with understanding the people you aim to serve. By using data and analytics it is possible to gain deeper insights into your target population, including:
What their needs and aspirations are
What approaches are more effective for delivering services, and which are less
And most importantly, how to effectively deliver positive outcomes for these communities
To learm more about our blog series on Social Investment check out our previous post here.
How do you analyse and understand the communities you serve? Share your insights in the comments below.
If you would like to know more about how you can use data and analytics to understand the people you serve reach out to us for a chat – we’d love to hear from you! hello@nicholsonconsulting.co.nz