Pause That Purchase: Rethink Data Before Your Next Local Government Upgrade
The Case for New PaaS-Based Data-Centric Models
Local governments are at a pivotal moment when it comes to data management. For decades, their core systems have been built around functionally specific data models, usually Property-Centric (e.g., property and property tax databases) or Customer-Centric (e.g., name-and-address records). While these systems have served councils reasonably well, their rigidity and siloed nature have always been well known but have become increasingly evident in today’s data-driven world.
Historically, these models attempted to support the myriad needs of councils through extensions and integrations with ancillary systems. Yet the reality is that single-vendor solutions designed around one dominant data model, whether property or customer, struggled to meet the demands and complexity of councils that ultimately manage about five distinct types of data centricity:
Property (e.g., land records, taxation, statutory planning and zoning).
Customer (e.g., ratepayers, service recipients).
Assets (e.g., infrastructure, facilities).
Records (e.g., compliance, governance, documentation).
Employees (e.g., HR, payroll, workforce management).
These competing domains placed pressure on legacy systems, exposing their limitations and creating inefficiencies in areas like service delivery, reporting, and decision-making.
Finance isn’t explicitly listed as one of the five core data-centric models in this context because finance is not inherently a data-centric model in the same way as property, customer, assets, records, or employees. Instead, rather than being a domain with its own distinct data model, finance acts as a layer or lens through which all these domains are interpreted, categorised, and managed.
The absence of finance from the list of core data-centric models does however underscore an important evolution in council technology strategy. Instead of shaping systems around finance’s needs, modern councils are starting to focus on developing robust, interconnected data models that equally support all domains.
But Hang On, Aren’t Finance Systems the Most Critical?
Selling software to council CFOs has been a mainstay of successful local government ISVs. While this approach has worked well for the software companies, in many (many) cases it has led to suboptimal outcomes for the organisation.
Many council CFOs come from financial or accounting backgrounds and may lack a comprehensive understanding of modern technology solutions. In fact many openly and proudly identify as “non-technical”. As a result, they tend to evaluate software primarily through the lens of financial management and accounting functions. This narrow focus often overlooks the potential for these systems to drive improvements across other areas, such as cross-departmental workflows and efficiencies.
When selling directly to the CFO, software vendors may also sidestep technical scrutiny from IT professionals or operational teams who better understand the intricacies of system integration, scalability, and long-term usability. This approach often results in decisions that prioritise financial appeal over technical soundness or broader organisational needs, leading to mismatched solutions or challenges in implementation. In relation to data management in councils, here’s a further break-down on why.
The Chart of Accounts (CoA) is a critical finance tool but it not a data model. It is a classification framework that maps the financial implications of data within the five domains to relevant accounting categories.
The CoA organises the financial impact of activities but doesn’t generate or establish a relationship between the data itself. This flows from the structure within the other domains. Therefore the quality of Finance information (like reporting) is determined by the relationships between entities but is primarily concerned with costs, revenues, budgets, and transactions. These are derived from activities and data in the other domains. For example:
Property Data generates financial transactions like property taxes or development fees.
Customer Data ties to billing systems for ratepayers or service recipients.
Asset Data underpins capital expenditures and maintenance costs.
Records and Employee Data feed compliance costs, payroll, and operational budgets.
Historically, decisions about the effectiveness of these five models prioritised the preferences of the head of finance or corporate services because finance departments have historically held significant influence over council technology procurement.
That is because budgets and funding approvals are tied to financial oversight and older systems often tied financial reporting closely to core functional modules, such as property or customer data, which made finance-centric decision-making a default approach.
However, this approach conflates the tooling needs of finance (e.g., accounting systems) with the underlying data architecture needed to support organisational functions. Systems designed solely to make finance "happy" risk misaligning the broader organisational requirements, especially as councils increasingly rely on multi-domain integration. This has ultimately become the reality for many city and regional government organisations.
Unlike the five identified domains (property, customer, assets, records, and employees), finance doesn’t originate or manage its own unique type of data. Instead it is transactional and aggregative, summarising and interpreting data from other domains. Its success ultimately depends on the extensibility and integration of these domains rather than its own discrete data-centric model.
That is why we see so much excel management in finance, or the requirement for a BI or analytics solution to manage the integration. Financial reporting and decision-making rely on the ability to pull, analyse, and map data across domains, highlighting the importance of data model interoperability, not just accounting system functionality.
When councils historically prioritised financial system satisfaction over the extensibility of core data models, they inadvertently created systemic inefficiencies that have continued to aggregate over time. That is bad.
It includes things like choosing systems optimised for financial reporting but poorly integrated with operational data. Or core systems built to satisfy finance but lacking the flexibility to support other critical domains like customer service, asset management, or compliance. Or systems that reinforced reliance on legacy providers, slowing the adoption of modern, multi-domain platforms more capable of cross-domain collaboration.
In modern PaaS architectures, and data strategy approaches, finance should be recognised as a critical stakeholder, but not the sole driver of system design. That will require significant organisational change. I believe it is an inevitability albeit many councils will be very slow to respond to the new opportunities.
Those ready to tackle this systemic problem can immediately prioritise building flexible, domain-agnostic architectures capable of supporting all five data models. This will allow them to integrate financial tools that can leverage these architectures without dictating their structure, and ensure that financial insights are a byproduct of robust data integration, not the primary determinant of system design.
Why This Matters for Council Data Strategies
By shifting away from finance-centric software decisions, councils can unlock the full potential of new technologies like PaaS and position themselves for smarter, more adaptive governance. This shift also allows finance to thrive as an integrated, relational lens enabling better decision-making while ensuring the broader organisation operates efficiently and effectively.
The limitations of historical data models make it essential for councils to carefully consider their data strategies moving forward. A well-designed data strategy must acknowledge that no single data model is sufficient on its own. Councils need to adopt an approach that embraces multi-dimensional data management, ensuring that each of the five core domains receives equal attention. Without this intentionality, councils risk repeating past mistakes including:
Creating Data Silos: Functionally specific models inherently silo data, making it difficult to integrate information across departments or gain holistic insights.
Creating Inefficiency: Systems designed around a single model are less adaptable to evolving needs, requiring workarounds or costly customisation.
Missed Opportunities: Poor data extensibility can hinder innovation, such as leveraging advanced analytics or AI to improve public services.
For modern councils this means prioritising key principles of interoperability, data sharing, and governance in their data strategies. These elements are not optional; they are foundational for delivering responsive and efficient services in an increasingly complex urban environment.
The PaaS Revolution: A Catalyst for Change, Even If It's Not for Everyone
The advent of Platform as a Service (PaaS) architectures represents a seismic shift in the local, city and regional government technology landscape. Unlike legacy systems, PaaS solutions are built on data graph models1 and highly extensible database structures that are fundamentally more adaptable to the diverse needs of councils. Consider how the following PaaS benefits compare to your own current system challenges:
Unified Data Models: PaaS platforms can harmonise multiple data-centric domains (property, customer, assets, etc.) within a single environment, eliminating silos. This is very important in the complex multi-disciplinary environment of local government.
Interoperability: New architectures prioritise integration and extensibility, enabling councils to choose best-in-class solutions without being locked into a single vendor ecosystem.
Innovation Access: By adopting modern architectures, councils can take advantage of cutting-edge capabilities such as predictive analytics, IoT integration, and AI-powered automation.
Vendor Diversity: PaaS architectures have opened the door for new software service providers to enter the local government market. This injects much-needed competition into a space that has been stagnant for decades.
The data graph models at the heart of these platforms are particularly noteworthy. Unlike traditional relational databases, data graphs can handle complex relationships and dependencies across disparate data types. This flexibility is critical for councils that must connect customer records to property databases, overlay asset management data, and tie it all back to compliance and governance frameworks.
A Call to Action
Local governments have always been stewards of vast quantities of data. The challenge now is to move beyond the limitations of traditional systems and fully embrace the possibilities offered by modern data-centricity and PaaS solutions.
Doing so will not only improve operational efficiency but also enable councils to better serve their communities in an era of rapid change. The convergence of historical challenges and emerging technological solutions creates both an opportunity and an obligation for local governments to rethink their approach to data management.
In order to capitalise on this moment, that means taking a few fairly simple initial steps:
Firstly, develop forward-thinking data strategies that embrace a model that considers all five data-centric domains, prioritises integration, and positions the council for long-term success.
Secondly, start to leverage PaaS architectures to take advantage of the flexibility and scalability offered by modern platforms to future-proof operations and foster innovation.
Thirdly, diversify technology partnerships by exploring relationships with new vendors enabled by PaaS architectures, breaking free from legacy constraints and driving better outcomes.
This transformation isn’t just about technology, it’s about strategy. None of these actions requires a rip-and-replace approach to core systems thinking.
Councils that seize this opportunity will find themselves better equipped to tackle the challenges of the future, while those that cling to outdated approaches risk falling further behind for another 10-year cycle.
In the end, the question isn’t whether to modernise but how quickly and effectively it can be done.
At a high level, a database structure is the foundational framework or architecture used in many local government solutions to store, organise, and retrieve data in a database system. Relational databases struggle with complex relationships or highly interconnected data. Instead vendors often just run multiple database structures.
An industry data model is a pre-defined, standardised framework that represents data structures, relationships, and business rules for a specific industry or domain. For example a local government industry data model might define entities like properties, ratepayers, and assets. These can be inflexible if not adaptable to unique organisational needs and may lock councils into specific vendor ecosystems.
A data graph is a way of organising data using nodes, edges, and properties to represent entities, their attributes, and relationships. They are excellent for complex, interconnected data and naturally model many-to-many relationships. Some PaaS data graphs are optimised for high-volume transactional workloads.