It is no secret that private companies have championed digital transformation and the use of data to help support their business strategies. Data warehousing, cloud technology and Business Intelligence (BI) have become commonplace in most industries, with the focus now on optimising those capabilities and preparing to adopt more innovative solutions using emerging technologies such as Internet of Things, Artificial Intelligence and Augmented Reality.

Local Authorities hold vast amounts of valuable data but many have notably lagged in their adoption of advanced data capabilities and innovative technology. These local authorities are often still grappling with the fundamentals; data is locked away in silos across many different and aging systems. We have seen that the data held by councils is often inconsistent, inaccurate, or simply too difficult to access – and needs a lot of manual wrangling to get into shape. Historically this has meant data was mainly used for statutory reporting or reactive service which may have led to missed opportunities to drive efficiencies, or to offer more tailored and targeted services to citizens.

The Covid-19 pandemic has highlighted the importance of data and amplified the need for local authorities to deliver services in a more innovative, efficient, and effective way. As a result, data and analytics are becoming key priorities, as local authorities look to accelerate their digital transformation, unlock the insights that data can provide, and take a more citizen centric and outcome led approach to service delivery.

BI and Analytics & optimising data sharing can enable local authorities to get more value from their data

BI and analytics can prove extremely valuable in generating insights, and provide a more comprehensive view of citizens, enable evidence based, real time decision making, and support improved service delivery to citizens.

For example, having the capability to easily segment citizen data by demographic, location, available services, and utilised services and combining with attitudinal data from market research, focus groups and surveys can yield valuable information on quality and distribution of services and provide a deeper understanding of citizen needs and concerns.

Using predictive analytics to analyse current and historical data can help predict future events and forecast the local effect of changes in government policy, benefits, and rules. It can be used to better understand population numbers and trends and identify potential risks to citizens such as financial difficulties, early school leaving, homelessness, home fires, domestic violence, child abuse and mental and physical health issues.

All these insights can then be used to provide improved, targeted care and services whilst taking preventative action to minimise risks and safeguard the most vulnerable. It can also highlight opportunities to reform policies, deploy new strategies across the organisation whilst ensuring budgets can been allocated more effectively.

Policies, principles, and processes to facilitate data sharing across different organisations and authorities can help identify trends, create more joined up services, and share experiences and best practices. More transparency can help build trust across organisations and the public.

‘Smart’ Infrastructure

Bolstering BI and analytics with emerging technologies such as Geospatial Analytics and Internet of Things (IoT) can contribute to creating a ‘smart’ infrastructure.

Geospatial Analytics combined with AI/Machine Learning techniques can determine optimal emergency service routes to reduce response times and better prepare logistics for moving resources during natural disasters. It can help optimise traffic management and street cleaning routes, more accurately forecast demand for transport services and help determine relevant innovative transport solutions needed to meet urban challenges.

IoT and AI technology can be utilised in smart public transport to better inform citizens of delays, help them choose optimal transport routes and methods based on real-time data. Smart parking can provide a smoother journey to finding optimal parking spots based on real-time availability, demand, and cost. Strategically placed sensors can control street lighting, optimise waste management, and can help detect emergencies and air pollution.

Based on our experience with data and analytics in both public and private sectors, below are 5 critical success factors for building data and analytics capabilities.

1) Define a Data Strategy - This is the starting point. A good data strategy should align and be tied to an organisation’s strategic goals and focus areas, so defining this strategy along with a set of clear expected outcomes is key. Understand, validate, and document the current data landscape; identify specific challenges and opportunities, consider what data needs to be gathered and analysed and use this to help build a strategy tailored for the organisation.
2) Design and build a robust data foundation - Look at putting a data governance framework and data management processes in place so data is identified, stored, processed, provisioned, and governed to best practices. This will help create a single source of truth that is trusted across the organisation, remove unnecessary silos, allow for automation to reduce dependence on data wrangling and ensure accountability. This framework will also allow for the definition and maintenance of any data polices and regulations (such as GDPR) and set data quality KPIs that can be continually monitored. Data should be owned, secure, accessible and of good quality. 
3) Start small and then scale - Aim to drive value quickly, via small scale targeted investments, before embarking on a large implementation. Mobilise a small team and pick one or two problems or use cases with clear expected outcomes to tackle first. This will help build out a proof of concept and create a scalable, repeatable process for other areas. It will also demonstrate value quickly and build confidence and buy-in from key stakeholders in the organisation.
4) Take an agile approach - Rapid prototyping, testing and iteration allows for flexibility in rapidly changing environments. It facilitates greater transparency and collaboration across the organisation which can improve quality of outputs, reduce risk, and help build momentum. A ‘test and learn’ approach, and adapting along the way, will ultimately yield a more efficient and cost-effective delivery, and ensure developed solutions address the needs of customers.
5) Build a data driven culture - Ensure data and analytics are integrated into the organisational culture, ways of working and decision-making processes across all levels of the organisation starting at executive level. An organisation’s data strategy and capabilities should be well known and championed. Treat data as a business asset.Find out more about our Data & Analytics or Public Services capabilities.

For tailored advice on how to kickstart or accelerate your data journey, get in touch.