Data Collection in Family Hubs: Meeting Government Requirements

A practical guide to meeting DfE data collection and reporting requirements in Family Hubs, covering the national data framework, demographic monitoring, outcome measurement, and systems that make compliance manageable.

By Plinth Team

Data collection is one of the most challenging aspects of running a Family Hub programme. The national data framework sets detailed requirements, but frontline staff are rightly focused on working with families, not filling in spreadsheets. The solution lies in systems and workflows that capture data as a natural byproduct of service delivery.

TL;DR: The DfE requires Family Hub programme authorities to collect and report structured data on service uptake, demographics, Start for Life services, and outcomes. Meeting these requirements without overburdening staff depends on embedding data capture into everyday workflows — booking, registration, attendance, and feedback — rather than treating it as separate administrative work. Purpose-built platforms like Plinth automate much of this, mapping data collection directly to DfE reporting fields.

What you'll learn: What data the government requires, how to collect it efficiently, and how to avoid common pitfalls.

Practical steps: From designing registration forms to automating quarterly returns.

Who this is for: Family hub managers, data officers, and local authority commissioners designing data collection processes.

What the DfE Data Framework Requires

The national data framework for Family Hubs was introduced alongside the programme funding. It represents a step change from the locally determined, often inconsistent data collection that characterised children's centres.

Service Contact Data

What to collect: Every meaningful contact between a family and a Family Hub service should be recorded. This includes booked appointments, drop-in attendance, phone consultations, virtual sessions, and outreach contacts.

Required fields: Date, service type, delivery location, family identifier, and whether the contact was face-to-face, virtual, or by telephone. The DfE expects data to be disaggregated by service category aligned to the programme's service taxonomy.

Volume expectations: A well-functioning Family Hub network in a medium-sized authority might record 15,000-25,000 service contacts per quarter. The system must handle this volume without manual aggregation.

Service contact data is the foundation of all reporting. If contacts are not recorded consistently, everything built on top — demographic analysis, outcome measurement, utilisation reporting — is unreliable.

Demographic Data

The government wants to understand who is accessing Family Hub services and, crucially, who is not.

Required demographics: Age of child, age of parent/carer, ethnicity, postcode (for deprivation mapping), disability or SEND status, and language spoken at home. Additional fields may include family composition and employment status.

Deprivation Analysis: Postcodes enable mapping of service uptake against the Index of Multiple Deprivation (IMD). The programme specifically targets families in the most deprived areas — a disproportionate share of children in the 75 funded authorities live in the most deprived areas.

Equalities Monitoring: Demographic data supports equalities analysis, helping authorities identify whether services are reaching families from all communities. Research consistently shows that some groups — fathers, ethnic minority families, families with English as an additional language — are underrepresented in family services.

Voluntary Collection: Demographic data must be collected voluntarily. Families cannot be required to provide it as a condition of accessing services. However, good explanation of why information is collected typically achieves high response rates.

Start for Life Data

The six Start for Life service areas have specific data requirements.

Infant Feeding Support: Number of families accessing support, type of support provided (one-to-one, group, peer), and feeding outcomes where available.

Perinatal Mental Health: Referrals received, assessments completed, support provided, and referrals onward to specialist services.

Parenting Support: Programme enrolments, completion rates, and validated outcome measures where programmes include them (e.g., the Warwick-Edinburgh Mental Wellbeing Scale).

Home Learning Environment: Number of families receiving HLE guidance, engagement with resources, and, where measurable, changes in home learning activities.

Parent-Infant Relationships: Referrals to attachment-focused support, sessions delivered, and practitioner assessments of progress.

Family Support: Contacts with family support workers, types of issues addressed, and outcomes such as successful signposting to other services.

Start for Life data is particularly important because the government has made specific commitments about improving outcomes in these areas. Robust data demonstrates whether those commitments are being met.

Outcome Data

Beyond counting contacts, the DfE expects evidence that Family Hub services are making a difference.

Programme-Level Outcomes: These align with the programme's logic model — for example, increased breastfeeding initiation and continuation rates, improved parental mental health, better school readiness indicators.

National Indicators: Some outcomes align with existing national datasets — breastfeeding rates at 6-8 weeks (from health visiting data), early years foundation stage profiles, and children's social care referral rates.

Local Indicators: Authorities are encouraged to develop local outcome indicators that reflect their specific priorities and context.

Attribution Challenges: Demonstrating that Family Hub services caused improved outcomes (rather than just being associated with them) is methodologically challenging. The DfE guidance acknowledges this and emphasises contribution rather than attribution.

Designing Efficient Data Collection

Principle 1: Capture Data at Point of Contact

The most reliable and least burdensome approach is to collect data as part of normal service delivery interactions.

Registration: When a family first engages with any hub service, collect core demographic information once. This should be linked to a family record that persists across all future contacts, so families are not asked repeatedly for the same information.

Booking: When a family books a service, the system should automatically record the service type, date, and location. No additional data entry is needed for these fields.

Attendance: At each session, record attendance. For booked sessions, this means confirming who attended. For drop-in sessions, a simple check-in process captures attendance.

Feedback and Outcomes: At programme completion or at defined intervals, collect outcome data through brief feedback forms or validated tools. Build these into the programme design rather than adding them afterwards.

Each touchpoint captures a small amount of data. Aggregated across the quarter, this builds a comprehensive picture without any single interaction being data-heavy.

Principle 2: Use Structured Fields

Free-text fields are flexible but useless for reporting. Structured data — dropdowns, checkboxes, date fields — enables automatic aggregation.

Service Categories: Use a standardised list of service types aligned with the DfE taxonomy. Do not allow free-text service descriptions.

Demographic Fields: Use standard categories for ethnicity (ONS harmonised categories), age bands, and other demographics. This ensures consistency across the hub network.

Outcome Measures: Where validated tools exist (e.g., PHQ-9 for depression screening, WEMWBS for wellbeing), use them as structured questionnaires rather than free-text notes.

Location Identifiers: Use a defined list of hub sites rather than free-text location entry.

Principle 3: Collect Progressively

Do not try to collect everything at first contact. This creates a poor experience for families and a barrier to engagement.

First Contact: Name, child's age, postcode, and contact details. This is enough to register and book services.

Second or Third Contact: Ethnicity, language, disability status, and other demographics. By this point, families have some relationship with the service and are more willing to share.

Ongoing: Outcome data collected through programme participation, feedback forms, and practitioner assessment.

Progressive collection respects families' time and builds trust. It also produces better quality data because families who feel comfortable are more honest and detailed.

Principle 4: Automate Aggregation and Reporting

No staff member should spend hours manually counting contacts, calculating percentages, or copying data between spreadsheets.

Real-Time Dashboards: Systems should provide live views of key metrics — total contacts this period, demographic breakdown, service utilisation by type and site.

Automated Reports: DfE reporting templates should be populated automatically from the data captured during service delivery. The coordinator's role should be reviewing and submitting the report, not creating it from scratch.

Data Quality Alerts: Systems should flag missing data — for example, contacts without demographic information or sessions with no recorded attendance — so gaps can be addressed promptly.

Plinth maps its data collection fields directly to DfE reporting requirements, generating reports automatically from data captured through booking, registration, and attendance. This eliminates the manual aggregation step that consumes so much time in authorities using spreadsheets or generic tools.

Meeting Specific Reporting Requirements

Quarterly DfE Returns

Programme authorities submit data to the DfE quarterly. The return covers the previous quarter's activity.

Preparation Timeline: With good systems, preparation should take hours, not days. Monthly data quality checks throughout the quarter prevent end-of-period scrambles.

Data Validation: Before submission, check for completeness (are all sites reporting?), consistency (do numbers add up?), and plausibility (are any figures unexpectedly high or low?).

Narrative Context: Some returns require narrative alongside numbers. Keep quarterly notes about significant events, changes, or achievements so these are easy to compile at reporting time.

Annual Returns

Annual data provides a broader view and typically includes more detailed analysis.

Trend Data: Year-on-year comparisons showing growth in service uptake, changes in demographic reach, and progress against outcomes.

Case Studies: Qualitative examples that bring the numbers to life. Encourage practitioners to note impactful stories (anonymised) throughout the year rather than trying to recall them at annual reporting time.

Self-Assessment: Some annual returns include self-assessment against programme milestones. Data systems should make it straightforward to evidence progress.

Local Reporting

Beyond DfE requirements, local stakeholders — elected members, health partners, voluntary sector boards — need reporting too.

Tailored Dashboards: Different stakeholders need different views. Hub managers need operational data; directors need strategic summaries; partners need data about their specific services.

Timeliness: Local reporting often needs to be more frequent than DfE returns. Monthly or even weekly snapshots support operational management.

Accessibility: Present data in accessible formats. Not everyone is comfortable with spreadsheets — visual dashboards, infographics, and brief summary reports communicate more effectively.

Common Data Collection Challenges

Challenge 1: Partner Data Integration

When multiple organisations deliver services, they may use different systems, definitions, and recording practices.

The Problem: A health visitor records contacts in SystmOne, a voluntary sector partner uses their own database, and the council uses a spreadsheet. Aggregating data across partners is manual, slow, and error-prone.

The Solution: Use a shared platform for Family Hub-specific data. Partners can continue using their own systems for internal purposes but record Family Hub contacts in the shared system. Plinth supports multi-agency access with role-based permissions.

The Compromise: Where partners cannot use a shared system, establish clear data submission processes — agreed formats, deadlines, and definitions — to enable aggregation.

Challenge 2: Drop-In Data

Drop-in services are the hardest to track because there is no advance booking.

The Problem: Drop-in sessions account for a significant proportion of Family Hub contacts but are often under-recorded because there is no booking trigger.

The Solution: Implement simple check-in processes for drop-in sessions. A tablet at the entrance where families tap their name or scan a QR code works well. For unregistered families, a quick paper sign-in sheet can be digitised afterwards.

Minimum Viable Data: At minimum, record a headcount, the service type, and the date. For registered families, link attendance to their record. For new families, offer quick registration.

Challenge 3: Outcome Measurement

Measuring whether services are making a difference is methodologically harder than counting contacts.

The Problem: Outcomes like "improved parental confidence" or "better home learning environment" are subjective and hard to measure consistently.

The Solution: Use validated tools where they exist — the WEMWBS for wellbeing, the Ages and Stages Questionnaire for child development, structured pre-and-post measures for parenting programmes. For services without validated tools, use simple self-rated scales administered at the start and end of engagement.

Be Realistic: Not every contact will produce measurable outcomes. Focus outcome measurement on structured programmes (courses, ongoing support) rather than trying to measure the impact of every drop-in session.

Challenge 4: Consent and Data Protection

Collecting personal data from families requires clear legal basis and transparent communication.

Legal Basis: Most Family Hub data collection relies on legitimate interests or public task as the lawful basis under UK GDPR, though consent is needed for some specific uses.

Privacy Notices: Provide clear, accessible information about what data is collected, why, how it is stored, and who it is shared with. Avoid legalistic language.

Data Sharing Agreements: Multi-agency data sharing requires formal agreements (often called Information Sharing Agreements or Data Processing Agreements). These should be in place before services launch, not retrofitted.

Retention Policies: Define how long data is kept and when it is deleted. Systems should support automated retention management.

Challenge 5: Data Quality

Poor quality data is worse than no data because it provides false confidence.

Common Quality Issues: Missing fields, inconsistent categorisation, duplicate records, and outdated information.

Prevention: Mandatory fields at key data entry points, dropdown lists instead of free text, and regular deduplication checks.

Monitoring: Monthly data quality reviews catch problems early. Track completeness rates by site and service type — if one spoke consistently has 40% missing ethnicity data, investigate why.

Culture: Foster a culture where data quality is everyone's responsibility, not just the coordinator's. Help practitioners understand that their data entry directly affects reporting and, ultimately, funding decisions.

Data quality improves dramatically when practitioners understand why they are collecting data and can see how it is used.

Building a Data-Positive Culture

Successful data collection depends as much on culture as on systems.

Explain the Why

Practitioners collect better data when they understand its purpose. Share reports with the team — show them how their data collection leads to insights about service utilisation, demographic reach, and family outcomes.

Make It Easy

Every unnecessary click or field reduces compliance. Design data entry to be as quick as possible. If attendance recording takes more than 30 seconds per family, it is too complicated.

Celebrate Insights

When data reveals something useful — a service reaching a previously underserved community, outcomes improving for a particular programme, demand justifying additional funding — share the finding widely. This reinforces the value of good data.

Address Concerns

Some practitioners worry that data collection is surveillance or performance monitoring. Be honest about how data is used and ensure it genuinely supports service improvement rather than punitive management.

The authorities with the best data are those where practitioners see data collection as part of their professional practice, not as an imposition.

FAQs

What happens if our data is incomplete?

The DfE expects best efforts rather than perfection, but consistently incomplete data risks programme funding and makes it impossible to demonstrate impact. Invest in systems and processes that capture data routinely so completeness is the default rather than something that requires extra effort.

Do we need consent for every piece of data?

Not necessarily. Much Family Hub data collection can be justified under the legitimate interests or public task lawful basis under UK GDPR. However, you do need clear privacy notices, and some specific uses — such as sharing identifiable data with non-statutory partners — may require consent. Seek legal advice for your specific arrangements.

How do we handle families who refuse to give demographic data?

Respect their choice. Demographic data collection must be voluntary. Record the service contact without the demographic fields. Over time, as families build trust with services, they may be willing to share more. Emphasise that the information helps improve services for everyone.

Can we use the same data for local and national reporting?

Yes, and this is the most efficient approach. Collect data once to a standard that meets both DfE and local requirements. Avoid creating separate data collection processes for different audiences.

What systems does the DfE recommend?

The DfE does not mandate a specific system but does specify the data fields and formats required. The choice of system is a local decision. Purpose-built platforms like Plinth are designed to meet DfE requirements natively, while generic tools require configuration.

How do we measure outcomes for drop-in services?

Formal outcome measurement is difficult for casual, drop-in contacts. Focus outcome measurement on structured programmes where pre-and-post assessment is feasible. For drop-in services, track attendance patterns (repeat visits suggest families find value) and collect periodic satisfaction feedback.

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Last updated: February 2026

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