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Data is undoubtedly one of the government's most valuable assets. Whether it is policymaking and service delivery or public trust and transparency, the quality of government data directly affects outcomes for citizens and the effectiveness of public administration. However, many public sector organisations still struggle with inconsistent, incomplete or outdated data, which undermines evidence-based decision-making across departments.

Improving government data quality isn’t just a technical task. It should be a strategic priority that touches governance, processes, skills, culture and technology. This simple guide gives some practical steps government data professionals can take to raise the standard of their data and build confidence in how it’s used.

Why data quality matters in government

Good quality data is essential in the public sector because it:

·       Enables accurate decisions in policy, budgeting and delivery. Poor data can misguide planning and resource allocation.

·       Supports transparency and accountability to citizens, regulators and other stakeholders.

·       Reduces risk, waste and inefficiency by preventing errors and duplication.

·       Ensures compliance with standards, metadata requirements and legal frameworks (e.g. GDPR).

·       Enhances interoperability across government systems and agencies.

The UK Government Data Quality Framework emphasises that poor quality government data undermines public outcomes and must be managed proactively, rather than reactively.

Core dimensions of high-quality government data

Government data quality should be assessed across six key characteristics, as outlined by DAMA UK as part of DAMA-DMBOK framework:

1.       Completeness: Are all required fields present and meaningful?

2.       Validity: Does the data conform to defined standards and formats?

3.       Consistency: Are the same rules applied across datasets and systems?

4.       Accuracy: Does the data reflect the real world correctly?

5.       Timeliness: Is the data up-to-date and available when needed?

6.       Uniqueness: Are records free of unnecessary duplicates?

These dimensions help government agencies understand where quality is lacking and prioritise improvements accordingly.

There's more information on these in our 'What does good data look like?' whitepaper.

7 steps to improve government data quality

1. Establish strong data governance

Quality improvements must be backed by clear governance structures. This includes defined roles (data owners, stewards, custodians), policies and accountability. Governance ensures consistency in how data is created, maintained, shared and used across departments.

·        Define data ownership for each key dataset.

·        Set policies on data naming conventions, classifications and standards.

·        Embed quality KPIs in governance documentation.

Clear governance helps ensure data quality isn’t treated as a technical afterthought but an organisational priority.

2. Know your critical data assets

Not all data is equal. Government departments should:

·        Identify high‑impact datasets that support major policy decisions or service delivery.

·        Prioritise quality improvements based on risk and importance.

·        Develop bespoke quality plans for each dataset.

This approach ensures resources are focused where they matter most first.

3. Develop a formal Data Quality Action Plan (DQAP)

A DQAP provides a roadmap to systematically raise quality by:

·        Identifying critical data and quality standards.

·        Assessing current data quality levels.

·        Setting measurable targets and timelines.

·        Assigning responsibilities and review cycles.

Government guidance recommends documenting findings and using dashboards to monitor progress over time.

4. Standardise data collection and handling

Errors often enter a system at the point of data capture. Standardised forms, validation rules and automated checks can reduce manual errors and ensure uniform data entry across teams and services.

Examples of this include:

·       Use pre-defined value lists and structured formats.

·       Implement automated data validation in citizen service forms.

·       Use drop-down menus and input constraints where applicable.

Standardisation improves consistency and trust in datasets.

5. Automate quality monitoring and reporting

Automation can help simplify ongoing quality assurance:

·       Implement automated quality checks and validation routines.

·       Use data quality dashboards to surface problems early.

·       Set up alerts for anomalies or missing critical fields.

Real‑time observability helps departments detect issues quickly rather than discovering errors after decisions have been made.

6. Invest in metadata and documentation

Metadata (information about your data) is crucial in government settings:

·       Document the origin, meaning and lifecycle of datasets.

·       Explain how data is collected, transformed, and used.

Good metadata helps users assess whether data is “fit for purpose” and supports interoperability across systems.

7.      Build a data quality culture

Data quality is not just a technical problem, but also a cultural one. Departments with healthy data cultures share some common traits:

·       Leaders understand why quality matters.

·       Teams proactively address issues before they escalate.

·       Quality practices are embedded early in data lifecycles.

·       Regular training and upskilling opportunities.

Advanced practices for government data quality

Besides the above basics, government agencies can accelerate improvements by adopting modern, proactive practices (which is also where external support from specialists like Butterfly Data can make a difference).

These include:

·       Data lineage and provenance: Tracing where data came from and how it has changed over time boosts transparency and understanding.

·       Reproducible analytical pipelines: Developing automated, repeatable analysis workflows reduces variability and errors.

·       Cross‑agency standards alignment: Aligning data definitions and formats across the public sector enhances data interoperability and integration.

·       Leverage analytics and AI tools: Modern platforms enable predictive quality checks, anomaly detection, and pattern analysis to catch issues before they affect decisions.

Benefits of improving government data quality

Investing in data quality has measurable returns:

·        Better informed policy decisions.

·        Increased operational efficiency.

·        Improved public trust and transparency.

·        Easier compliance with regulations and standards.

·        Reduced cost of rework and error correction.

High‑quality data empowers governments to serve constituents more effectively and supports a transparent, accountable data‑driven public sector.

Final comments

Improving government data quality is of strategic importance, not a one‑off project.

By grounding your efforts in governance, dimensions of quality, automation and culture change, you help your department make smarter decisions, deliver better outcomes and build trust in your public data assets.

Whether you’re a data steward, analyst, programme manager or senior leader, the steps above provide a practical roadmap to elevate your government’s data quality and make better use of this most critical asset.

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Public‑sector organisations in the UK are under growing pressure to modernise systems, maximise the value of data, and deliver efficient, innovative services - all while adhering to strict procurement rules. Procurement frameworks provide a compliant, cost‑effective, and streamlined route for public bodies to access specialist data consultancy, cloud services, AI solutions, and broader technology services.

With the enactment of the Procurement Act 2023, the public procurement landscape has been overhauled. The Act aims to simplify and modernise procurement, ensuring public funds deliver the greatest public benefit, while improving transparency and encouraging access toa wider pool of suppliers, particularly SMEs such as Butterfly Data.

For a data consultancy like us, being on multiple frameworks means we are well-positioned to support public‑sector organisations efficiently, while remaining fully compliant with the latest regulations.

Key benefits of public sector frameworks for data and technology

Faster procurement

Under framework arrangements, public-sector organisations can “call off” services from pre-vetted suppliers rather than run a full tender process each time. This substantially reduces procurement lead times and speeds up project start dates. For initiatives like cloud migration, data analytics platform builds or AI implementation, this speed is often crucial to success.

Cost‑effective delivery

Frameworks reduce administrative burden and enable economies of scale. By pre-agreeing terms, pricing, and supplier criteria, public bodies can secure high-quality data or tech services with minimal overhead to deliver strong value for money.

Compliance, transparency and assurance

The Procurement Act 2023 defines clear legal parameters for public procurement. Frameworks fall under its scope: Section 45 and following set out how frameworks and any “call-off” contracts awarded within them must operate.

Through a framework, public-sector clients can be confident that suppliers meet rigorous standards around fairness, equality of treatment, transparency, value for money, and public benefit.

Access to innovation and specialist expertise

Frameworks give public authorities rapid access to suppliers who specialise in emerging technologies: cloud engineering, data platforms, legacy-modernisation, AI, data analytics and more. This allows organisations to adopt modern, future‑proof solutions without procurement delays or compliance risk.

Long-term and scalable partnerships

Many public‑sector challenges require more than a single project - whether modernising legacy systems, migrating data, adopting AI, or building new analytics pipelines. Frameworks support long-term engagement: once on a framework, a supplier like Butterfly Data can collaborate over multiple phases, helping organisations evolve over time.

The frameworks Butterfly Data is on

Butterfly Data is proud to be an approved supplier on abroad set of public-sector frameworks to give clients direct, compliant access to our full range of data and technology consultancy service:

G Cloud 14

Cloud support services via Lot 3, enabling secure and scalable digital infrastructure

Digital Outcomes and Specialists 6 (DOS6)

Specialist digital services spanning discovery, alpha, beta, and live phases for high quality delivery

Spark DPS

Advanced technology and data solutions, supporting public bodies to adopt modern digital capabilities

DALAS (Digital & Legacy Application Services)

Services for IT, application support and legacy-system modernisation for government and public bodies

AI DPS

Advisory, modelling, and implementation of AI solutions tailored for public sector needs

Technology Services 4 (TS4)

Application management, data services, and robust technical support

Neutral Vendor Framework for Innovation (NVfI)

Specialist data and digital services for sensitive sectors such as defence and healthcare

Naval Design Partnering (NDP)

Data consultancy and analytics for defence and marine engineering projects

Accelerated Capability Environment (ACE)

Rapid innovation and technical support for public safety, national security, and data driven initiatives

In addition, we are registered on public procurement portals such as Contracts Finder, regional and local authority e‑sourcing platforms, and other sector‑specific portals to offer multiple compliant routes for public-sector clients to engage our services.

What this means for our public sector clients

By working with Butterfly Data through a framework, public deparetments benefit from:

- Swift, compliant procurement - skip the delays and bureaucracy of full tendering and engage expert data and technology services immediately

- Trusted supplier standards - our inclusion on multiple frameworks proves we meet stringent procurement, security, governance and performance criteria under the Procurement Act 2023

- Flexible and relevant service options - whether you need cloud infrastructure, data analytics, legacy modernisation, AI, or digital transformation - you can choose a route that matches your needs

- Transparent pricing, reduced risk and better value for money - standardised terms and pre agreed contract frameworks simplify budgeting and procurement planning

- Long-term support and partnerships - from initial strategy through to implementation and ongoing evolution, Butterfly Data can support multi-phase, multi-year digital and data transformation

Learn more about how you can procure our services via frameworks and digital marketplaces.

Imagine this: your team has just made a major decision based on a dashboard full of data… only to find out later that several key fields were missing, inconsistent, or outdated. Suddenly, what looked like insight was actually misleading and the cost of poor data hits both your budget and your credibility.

The truth is, every organisation has data, but not all data is good data. And in an era where decisions are increasingly data-based, quality is everything.

Butterfly Data’s new whitepaper, “What Does Good Data Look Like?”, is a practical guide for organisations who want to move from “lots of data” to trusted, actionable data that drives real results.

Why Data Quality Matters

The whitepaper breaks down the critical points where data quality truly matters:

• Impact on decision-making: Poor data leads to poor decisions. Even small errors in critical fields can create cascading effects across operations.
  
  
• Responsibility is everyone’s: Data isn’t just IT’s job. Everyone who touches data must help maintain its quality.
  
  
• Practical strategies for improvement: Learn how to implement standards, monitor datasets, and fix errors systematically.

How to Assess Your Data 

At the heart of the whitepaper is DAMA UK’s six dimensions of data quality, which provide a clear, actionable framework:

1. Completeness - Are all critical fields filled? Missing essential information can compromise outcomes.
   
   
2. Validity - Do values conform to expected rules and formats? From UK postcodes to date formats, valid data enables automation and analysis.
   
   
3. Consistency - Are the values logical across datasets? A “UK-born? = Yes” paired with “Country of Birth = Germany” is a red flag.
   
   
4. Accuracy - Does the data reflect reality? Accurate data ensures reliable decisions and prevents costly mistakes.
   
   
5. Timeliness - Is the data current and available when needed? Outdated data can mislead forecasts or operational planning.
   
   
6. Uniqueness - Are records duplicated? Unique identifiers maintain clarity and prevent errors.

The paper walks through each dimension with realistic examples that make it easy to see how these principles apply in any organisation.

Making Data Quality Real

Good data doesn’t happen by accident. The whitepaper offers practical guidance, including (but not limited to):

• Using dropdowns and calendar pop-ups to standardise data entry
  
  
• Applying null-value standards to handle missing data consistently
  
  
• Implementing both technical and process-based solutions for monitoring quality
  
  
• Learning how to safely enrich your data with third-party sources, while ensuring it’s trustworthy and fit for purpose

By combining these approaches, you can reduce errors, streamline decision-making, and save both time and money.

Transform Your Data into an Advantage

Whether you are a data analyst, a manager, or a decision-maker, this whitepaper is a guide to:

• Identifying the datasets that matter most
  
  
• Implementing organisation-wide standards for reliability
  
  
• Building confidence in your reports, dashboards, and insights
  
  
• Using enriched data to drive deeper, more meaningful understanding

Good data is complete, valid, consistent, accurate, timely, and unique. When you have it, you can make decisions faster, with confidence, and with measurable impact.

→ Download the “What Does Good Data Look Like?” whitepaper

You can also find out more about our data quality services here to see how Butterfly Data can help you implement best practices and maintain high-quality datasets.