Citizens have to submit the same information multiple times because government agencies typically operate on separate, disconnected IT systems that do not share data with one another. Each department manages its own database, so when a citizen interacts with a different agency, that agency has no access to information already collected elsewhere. The result is repetitive form-filling, duplicated documentation, and unnecessary administrative burden. The sections below unpack the root causes, real-world consequences, and practical solutions behind this persistent problem.
Why don’t government systems share data with each other?
Government systems do not share data with each other primarily because they were built independently, at different points in time, using incompatible technologies and data standards. Each agency historically designed its own IT infrastructure to serve its own operational needs, with no central architecture requiring interoperability. These legacy systems were never engineered to communicate across departmental boundaries.
Several structural factors reinforce this fragmentation. Legislation in many countries restricts how personal data can be transferred between public bodies, often for legitimate privacy reasons. Budget cycles tend to be department-specific, making cross-agency investment in shared infrastructure politically and financially difficult. Data governance frameworks differ from one ministry to the next, meaning that even when two agencies hold the same information about the same citizen, the formats, definitions, and identifiers rarely match.
The absence of a unified citizen identifier compounds the problem further. Without a consistent, system-wide reference number that follows a person across all public services, agencies cannot reliably link records even when they want to. The outcome is a patchwork of isolated databases that each treat every citizen interaction as if it were the first.
What is the once-only principle and how does it work?
The once-only principle is a policy framework that requires citizens and businesses to provide personal information to government only once. After that initial submission, public authorities are responsible for sharing that data internally so that the same information never needs to be resubmitted for a different service or agency. The principle places the burden of data management on government, not on the individual.
In practice, the once-only principle works through a combination of legal reform, technical integration, and data governance. Governments establish a legal basis for controlled data sharing between agencies, define which datasets are eligible for reuse, and build secure data exchange platforms that allow authorised departments to query information already held elsewhere. A citizen who registers a change of address, for example, should only need to do so once, after which all relevant agencies are automatically updated.
The European Union has formalised this approach through the Single Digital Gateway regulation, which requires member states to make key administrative procedures fully online and to apply the once-only principle across borders by 2026. This gives the concept legal weight and a concrete implementation deadline for participating countries.
What are the consequences of redundant data collection for citizens?
Redundant data collection creates real, measurable costs for citizens in the form of wasted time, administrative frustration, and increased risk of errors. When people must repeatedly re-enter the same personal details across different agencies, the process becomes a significant burden, particularly for those navigating complex life events such as starting a business, applying for benefits, or dealing with a bereavement.
The consequences extend beyond inconvenience. Each additional data entry point introduces the possibility of inconsistency. A citizen might spell their address differently across two forms, or submit slightly different income figures, leading to discrepancies that trigger further administrative review. This slows down service delivery for everyone involved.
There is also a trust dimension. When citizens observe that different parts of government do not communicate with each other, confidence in public institutions erodes. People reasonably question why modern digital services, which are seamless in the private sector, remain fragmented in the public domain. For vulnerable populations who rely heavily on public services, the cumulative friction of repeated data submission can act as a genuine barrier to accessing support they are entitled to.
How do data silos between agencies cause duplicate requests?
Data silos between agencies cause duplicate requests because each agency’s database is isolated and inaccessible to others. When a citizen approaches a new department, that department has no technical means of retrieving information already held by a different body, so it simply asks the citizen to provide it again. The silo is not just a technical barrier but also an organisational and legal one.
Public sector data silos typically develop for three interconnected reasons. First, IT procurement has historically been decentralised, with each agency selecting and funding its own systems independently. Second, data protection legislation, while necessary, is sometimes interpreted so conservatively that even clearly beneficial data sharing is avoided to minimise legal risk. Third, organisational culture within government departments tends towards self-sufficiency, with agencies building internal capabilities rather than relying on shared services.
The practical effect is that the same citizen data, such as a name, address, date of birth, or tax reference number, may be stored in dozens of separate government databases simultaneously, each maintained at cost, each potentially out of date, and none aware of the others. Every time a citizen needs a service that crosses agency lines, the silo triggers another request for information the government already holds.
Which countries have successfully reduced redundant data submission?
Estonia is the most widely cited example of a country that has successfully reduced redundant data submission at scale. Through its X-Road data exchange platform, Estonian public agencies share data securely and in real time, meaning citizens almost never need to submit the same information twice. The country processes the vast majority of public services digitally, with most transactions completed in minutes.
Denmark has similarly advanced digital government infrastructure, with a national digital identity system and a shared data distribution platform that connects agencies across health, tax, and social services. Citizens interact with a single portal that draws on data already held by the state, eliminating most duplication.
Within the European Union, Austria has been a consistent leader in implementing the once-only principle, using a central register system that allows authorised agencies to verify citizen data without requiring resubmission. The Netherlands has also made significant progress through its DigiD identity platform and a network of base registrations that serve as authoritative sources for core citizen data.
What these countries share is a combination of strong political commitment, investment in shared technical infrastructure, and legal frameworks that explicitly enable controlled data sharing between public bodies while maintaining privacy protections.
How can IT modernisation fix the repeated data submission problem?
IT modernisation fixes the repeated data submission problem by replacing isolated legacy systems with interoperable architectures that allow data to flow securely between agencies. The core technical interventions include building API-based integration layers, establishing authoritative base registries for core citizen data, and implementing unified digital identity systems that link a citizen’s records across all public services.
Modernisation efforts that successfully address government data duplication typically involve several components working together:
- Base registries: Authoritative, government-maintained databases for fundamental data such as population records, address registers, and business registrations. These become the single source of truth that all agencies query rather than duplicate.
- API integration layers: Standardised interfaces that allow different government systems to request and exchange data in real time without requiring full system replacement.
- Digital identity infrastructure: A consistent, secure way of identifying a citizen across all government touchpoints, enabling records to be linked and reused reliably.
- Data governance frameworks: Clear rules about which agencies can access which data, under what conditions, and with what audit trails, ensuring that interoperability does not compromise privacy.
- Citizen-facing portals: Unified interfaces where people can manage their interactions with government in one place, with pre-filled forms drawing on data the state already holds.
The technical challenge is significant, particularly for governments carrying decades of legacy infrastructure. Modernisation rarely means replacing everything at once. Successful programmes typically adopt an incremental approach, connecting systems progressively through integration middleware while gradually retiring outdated platforms. The goal is interoperability, not uniformity, meaning agencies can retain fit-for-purpose internal systems while participating in a shared data ecosystem.
How Bloom Group helps governments and organisations tackle data fragmentation
Reducing redundant data submission is fundamentally an IT architecture challenge, and it requires the kind of deep technical expertise that bridges data engineering, application development, and strategic product thinking. That is exactly where we at Bloom Group operate.
We work with mid-cap and large organisations navigating complex digital transformation programmes, including those in the public sector and regulated industries where data interoperability is both a technical and a governance challenge. Here is what we bring to the table:
- Data engineering and architecture: We design and build the integration layers, data pipelines, and base registry connections that allow systems to share information reliably and securely.
- Application development: We develop the citizen-facing and back-office applications that make interoperable data actionable, from pre-filled digital forms to unified service portals.
- AI and data science: We apply machine learning techniques to data quality challenges, helping organisations identify inconsistencies, deduplicate records, and maintain accurate data across distributed systems.
- UX/UI design: We ensure that technical improvements translate into genuinely better experiences for end users, whether those users are citizens submitting a form or caseworkers processing an application.
- Team as a Service (TaaS): We embed experienced specialists directly into your programme, scaling capacity to match project phases without the overhead of permanent recruitment.
If your organisation is working to reduce data duplication, modernise legacy infrastructure, or build interoperable digital services, we would be glad to explore how we can help. Get in touch with our team to start the conversation.
Frequently Asked Questions
How long does it typically take for a government to implement the once-only principle across its agencies?
There is no single timeline, but national programmes typically unfold over five to ten years when implemented at scale. Countries like Estonia built their X-Road infrastructure incrementally over more than two decades, while EU member states are working toward cross-border once-only compliance by 2026 under the Single Digital Gateway regulation. The pace depends heavily on the volume of legacy systems in place, the strength of political commitment, and whether foundational infrastructure such as a national digital identity system already exists.
What is the biggest mistake governments make when trying to reduce data duplication?
The most common mistake is attempting a full, simultaneous replacement of legacy systems rather than adopting an incremental, interoperability-first approach. Trying to replace everything at once leads to cost overruns, delays, and failed programmes. A more effective strategy is to introduce API integration layers and base registries that connect existing systems progressively, allowing agencies to participate in a shared data ecosystem without requiring every department to migrate to a new platform at the same time.
How can governments share citizen data between agencies without violating privacy laws?
The key is establishing a clear, proportionate legal basis for each specific type of data sharing, rather than treating interoperability as a blanket permission to share everything. Effective frameworks define exactly which datasets can be reused, which agencies are authorised to access them, under what circumstances, and with full audit trails. Privacy-by-design principles, data minimisation, and robust consent or notification mechanisms ensure that interoperability and privacy protection are complementary goals rather than conflicting ones.
Does reducing redundant data submission actually save money for governments, or is it just better for citizens?
It delivers measurable savings for both. On the government side, eliminating duplicate data storage reduces infrastructure and maintenance costs across agencies, lowers the volume of manual data entry and verification work, and decreases the administrative overhead associated with resolving data inconsistencies. For citizens, the time saved translates into real economic value, particularly for businesses that interact with multiple public bodies regularly. Studies from EU-funded programmes have estimated that applying the once-only principle across member states could save businesses and citizens billions of euros annually in administrative costs.
What role does a unified digital identity system play in solving this problem, and do countries need one before they can make progress?
A unified digital identity system is arguably the single most important enabler of data interoperability, because without a consistent identifier, agencies cannot reliably link records belonging to the same person. That said, countries do not need a fully mature identity infrastructure before making progress. Partial improvements, such as connecting two or three high-traffic agencies through a shared base registry, can deliver immediate citizen benefits while the broader identity framework is developed in parallel. The identity layer and the data-sharing layer can be built incrementally and in tandem.
How should an organisation assess whether its current systems are contributing to redundant data collection?
A practical starting point is a data flow audit that maps every point at which citizen or user data is collected, stored, and requested again across different systems or service touchpoints. Key indicators of a problem include multiple databases storing the same core data fields, forms that ask for information already submitted in a previous interaction, and manual re-keying of data between internal systems. This audit typically reveals both the scale of duplication and the highest-impact integration opportunities to prioritise first.
Can smaller public sector organisations or local governments apply these solutions, or are they only feasible at a national level?
These solutions are absolutely applicable at the local and regional level, and in some cases local governments have moved faster than national ones precisely because they operate at a more manageable scale. A municipality can implement a unified resident portal, connect its internal departments through lightweight API integrations, and adopt national base registries as authoritative data sources without needing to build national-scale infrastructure from scratch. The principles of interoperability, base registries, and the once-only approach scale down as well as up.