There is a pressing need to quickly identify and visualise multiple factors impacting priority and vulnerable customers in a way which makes the data seamlessly accessible to teams across the business. The project is to build a collaborative platform which will combine data from across the business with open-source and paid for external data sources to create and interactive visual map which will allow staff to see where their decisions will have the largest impact on customers and where the most vulnerable customers are adversely affected by multiple factors such as poor housing, air quality and access to services.
Benefits
The benefits for this project are currently qualitative. These include:
Financial – initiatives and activity can be better targeted to meet the needs of vulnerable customers, saving time, money and effort.
Customer - Users will be able to view areas most affected already or predicted to be affected by climate, such as air quality, flood risks and weather allowing them to make decisions based on that data.
The tool focuses on measurements of potential vulnerability, allowing users to target their activities to those customers. Vulnerable customers will be better served and can be offered more holistic engagement thanks to being able to view other organisations’ activities and operational areas.
The geographic nature of the data will help users to better understand a community and meet its specific needs better. It also brings otherwise disparate organisations together into one area, such as different charities’ operational areas so customers and communities can be the most support available to them.
As the project progresses the benefits and impacts will be measured to determine the full qualitative and quantitative value.
Learnings
Outcomes
The project has not yet produced any quantitative data & associated benefits, with the project aims being the proof of concept & initial development of a vulnerability mapping tool.
Due to the success of the project, we will be actively seeking more partners to join on with the potential for the tool to be replicated for water and electricity.
This next stage will require more data and with the existing data sets with machine learning will continue to grow and become more advanced.
Lessons Learnt
Throughout the life of the project, we have identified several key points for using and implementing the tool:
Bespoke functionality
Whilst most teams and organisations can see value in the generic functionality, for the system to be adopted by teams as business-as-usual more bespoke functionality is required.
Population estimates
A lot of public data is published at an MSOA or LSOA level and requires modelling in order to work with it at a postcode level. The basis of these models are largely population estimates and therefore accurate population estimates are absolutely key to a lot of the data being accurate.
Census
Census data is only a snapshot in time and therefore almost all topics from the census are also available from other, more frequently updated sources.
Geography
Many data sources are patchy in geography and therefore not available in all places.
Private data
The layer import allows private data to be used with the system, but layers are limited in the information they can show. A method for organisations to use their own data in filters securely would be valuable to many teams.
Automation
Very few of the data import functions can be completely automated. Some take a much larger amount of time and computer resource than initially expected to produce accurate local datasets.
Consultancy
There is still often a need for a person to help interpret the data and the findings, even when users are given access to the tool and understand what they are seeing. It is not a technical problem, it is the need for someone who has spent a great deal of time working with the data to help guide users to the best way to answer questions and how the answers can be interpreted.
