Why Charities Struggle to Collect Impact Data (And How to Fix It)

Every charity knows impact data matters. Funders require it, trustees expect it, and beneficiaries deserve to know the services they rely on are working. Yet across the UK charity sector, data quality remains stubbornly poor. According to the Charity Digital Skills Report 2025, 31% of charities describe themselves as poor at or not engaging with collecting, managing, and using data (Charity Digital Skills Report 2025). That is nearly one in three organisations openly admitting they cannot do this well.

The problem is not that charities do not understand why data matters. It is that the people closest to the work — youth workers, support workers, volunteer coordinators, community organisers — experience data collection as admin that pulls them away from the people they are there to help. When data collection feels like a burden, it gets done badly, done late, or not done at all. The result is a vicious cycle: poor data leads to weak funding bids, which leads to less investment in the tools and processes that could improve data quality.

This guide explains why traditional approaches to charity data collection fail, what standard best practice actually looks like, and why a fundamentally different approach — one where data collection does not feel like data collection — is the real answer.

What you will learn:

• Why frontline staff resist data collection and why their resistance is rational
• What standard best practice recommends (and where it falls short)
• How AI-powered tools eliminate the data entry bottleneck entirely
• Practical steps to implement low-burden data collection in your organisation

Who this is for: Charity managers, programme leads, monitoring and evaluation officers, and frontline staff who want better data without more admin.

The Real Reason Charity Staff Resist Data Collection

The resistance is rational, not irrational. Frontline staff resist data collection because, in most organisations, it genuinely does take them away from the work they were hired to do.

A youth worker running an after-school session has 90 minutes with the young people in the room. Every minute spent filling in a register, completing a monitoring form, or logging attendance on a system is a minute not spent mentoring, teaching, or building trust. When that youth worker was recruited, they were told they would be working with young people — not doing data entry.

The Charity Commission's annual report for 2023-24 noted that there are over 170,000 registered charities in England and Wales (Charity Commission register, 2024), the vast majority of which are small organisations with limited staff capacity. For many of these organisations, a significant share of project budgets goes on monitoring, evaluation, and reporting — time and money diverted from direct delivery.

The problem compounds at every stage. When frontline staff rush through data collection, the data is incomplete. When the data is incomplete, the monitoring and evaluation team cannot produce meaningful reports. When reports are weak, funders lose confidence. When funders lose confidence, they reduce grants or add more reporting requirements — which means even more data collection burden on frontline staff.

According to the NCVO UK Civil Society Almanac, the voluntary sector workforce comprises approximately 978,000 paid employees and around 14.2 million volunteers (NCVO Almanac 2024). The sheer scale of people involved in frontline delivery — most of whom have no training in data collection — makes this a sector-wide structural challenge, not an individual performance issue.

What Standard Best Practice Recommends

Before exploring a fundamentally different approach, it is worth understanding what the sector typically advises. These recommendations are sensible and worth implementing — but they address symptoms rather than root causes.

Make forms shorter and simpler

The most common advice is to reduce the number of fields on data collection forms. If your monitoring spreadsheet has 40 columns, cut it to 15. If your beneficiary registration form takes 20 minutes, redesign it to take 5. The Institute for Voluntary Action Research (IVAR) recommends that funders reduce the burden of applications and reporting, specifically by asking for less information upfront and trusting organisations to deliver (IVAR, Open and Trusting Grant-Making, 2020).

Why it helps but is not enough: Shorter forms are better than longer forms. But even a short form is still a form. You are still asking a frontline worker to stop what they are doing, open a device, navigate to the right screen, and type information in. The friction is reduced but not removed.

Explain why data matters

Many organisations try to improve compliance by helping staff understand what the data is used for. "We need these attendance figures for the Big Lottery Fund report" is more motivating than "fill in this spreadsheet because I said so."

Why it helps but is not enough: Understanding the purpose does improve motivation — temporarily. But knowledge of why data matters does not change the fact that collecting it is still tedious. Staff understand why the fire alarm needs testing too, but it does not make the noise any less annoying.

Provide training on data systems

Some organisations invest in training to ensure staff know how to use the CRM, the monitoring database, or the spreadsheet template. Digital confidence matters — the Charity Digital Skills Report 2025 found that 69% of charities cite strained budgets as the biggest barrier to digital progress, followed by 64% who struggle to find funds for infrastructure and tools (Charity Digital Skills Report 2025).

Why it helps but is not enough: Training addresses digital confidence but not the underlying friction. A worker who is fully trained on a system can still find using it burdensome. The issue is not "I don't know how to enter data" — it is "I don't have time to enter data and I'd rather be doing my actual job."

Dedicate time for data entry

Some organisations schedule specific time for data entry — "Friday afternoons are for updating records." This ensures data collection is treated as part of the job, not an optional extra.

Why it helps but is not enough: Dedicated time helps with timeliness but creates its own problem. By Friday afternoon, the youth worker has run four sessions since Monday. Details are fuzzy. Names are half-remembered. The data entered is worse than data captured in real time. Memory decay is well-documented: Hermann Ebbinghaus's research on the forgetting curve shows that most information is lost within hours if not reinforced.

Traditional vs AI-Powered Data Collection

The following comparison illustrates why conventional improvements to data collection — while valuable — cannot match the step-change that AI-powered tools offer.

The difference is not incremental. It is a fundamentally different approach to how data enters the system.

What If Data Collection Did Not Feel Like Data Collection?

This is the core insight that changes everything. The problem with charity data collection is not that staff need better forms, more training, or greater motivation. The problem is that the act of entering data into a system — any system — is inherently in tension with the act of delivering services.

The solution is to remove the data entry step entirely.

The most effective data collection is the kind that frontline workers do not even recognise as data collection. When a youth worker photographs a register they were already using, the organisation has not added work — it has subtracted it.

Plinth takes this approach across three key areas:

Photograph a paper register and AI extracts attendance data

Your youth workers already use paper sign-in sheets. Many community centres, after-school clubs, and drop-in services have used paper registers for decades — and with good reason. They are fast, they do not need Wi-Fi, they do not need charging, and everyone knows how to use them.

The traditional approach says: replace the paper register with a digital check-in system. But that means tablets, Wi-Fi, user accounts, and training. For a busy youth club with 30 young people arriving in a 10-minute window, a tablet sign-in is slower and more disruptive than paper.

With Plinth, the youth worker keeps the paper register. At the end of the session, they photograph it with their phone. AI reads the handwriting, identifies names, cross-references with the participant database, and populates the attendance record automatically. The youth worker's total additional effort: taking one photograph.

This means the 14.2 million volunteers across the UK voluntary sector (NCVO Almanac 2024) — many of whom are digitally excluded or uncomfortable with technology — can continue working exactly as they always have while generating structured, reportable data.

Voice-record a conversation and AI generates a structured case study

Every funder wants case studies. They bring the numbers to life and show the human impact behind the statistics. But writing a case study is time-consuming. A support worker needs to recall the conversation, structure it into a narrative, identify the key outcomes, and write it up in a format suitable for a funder report. Most case studies take 30-60 minutes to write — time that could be spent with another beneficiary.

With Plinth's AI case notes, the process is completely different. A support worker has a 5-minute conversation with a beneficiary about how the programme has helped them. With the beneficiary's consent, they record it on their phone. AI transcribes the conversation, identifies key outcomes mentioned, and generates a structured case study ready for funder reports. The support worker reviews it, makes any edits, and approves it.

Total time: 8-10 minutes instead of 45-60 minutes. And the case study is more accurate, because it is based on the actual words spoken rather than the support worker's memory of the conversation.

Small charities often struggle most with evidence collection, not because they lack impact but because they lack the staff time to document it. AI-generated case studies directly address this by reducing documentation time by 70-80%.

Photograph an event and AI tags and catalogues it

Charities take hundreds of photographs at events, workshops, and community activities. These photos are powerful evidence of impact — but only if you can find them again. In most organisations, event photos end up in a folder on someone's phone or in a shared drive with no labelling, no connection to the programme that funded the activity, and no way to search for them when writing a funder report six months later.

With Plinth, photos taken at events are automatically tagged with metadata — the programme, the date, the location, and the type of activity. AI analyses the image context and generates descriptive tags that make the photos searchable. When it is time to write a report for a funder, you search for "youth mentoring programme photos Q3 2026" and every relevant image appears, properly catalogued and ready to use.

This turns an existing behaviour (taking photos) into a data collection activity — without any additional effort from the person holding the camera.

How Poor Data Collection Undermines Funding Bids

The consequences of poor data collection extend far beyond untidy spreadsheets. When a charity cannot demonstrate its impact with reliable data, it struggles to secure and retain funding.

UK charitable foundations gave a record £8.24 billion in 2023-24 (Association of Charitable Foundations, ACF Foundations in Focus 2024). Competition for those funds is intense. Funders increasingly expect evidence-based applications — not just descriptions of activities, but measurable outcomes supported by data.

A charity that can show "87% of participants completed the full 12-week programme and 73% reported improved confidence in post-programme surveys" will always be more compelling than one that says "participants attended regularly and told us they found it helpful." Both statements might describe the same programme, but the first is backed by structured data and the second is anecdote.

The Data Orchard report on data maturity in the charity sector found that organisations with higher data maturity are better at communicating their impact, securing funding, and making strategic decisions (Data Orchard, 2022). In other words, getting data collection right is not just about compliance — it is a competitive advantage.

Building a Low-Burden Data Collection Culture

Implementing AI-powered data collection tools is only part of the answer. The cultural shift matters just as much as the technology. Here are the practical steps that make data collection sustainable.

Start with what staff already do. Do not introduce new behaviours. If staff take photos, use those photos. If staff use paper registers, photograph those registers. If staff have conversations with beneficiaries, record those conversations. The principle is to extract data from existing activities rather than creating new ones.

Make the benefit visible immediately. When a youth worker photographs a register and sees the attendance data appear in the system within minutes, they understand the value instantly. When a support worker sees a polished case study generated from a conversation they recorded, they see how AI saves them time. Immediate feedback loops sustain adoption.

Remove the word "data" from the conversation. Frontline staff do not think of themselves as data collectors — nor should they. Talk about "showing what works" or "capturing stories" instead. The shift in language reflects the shift in practice: staff are not filling in forms, they are doing their jobs while the system captures evidence in the background.

Charities that have made this shift report a telling pattern: once they stopped talking about "data collection" and started talking about "showing your work," the same staff who used to leave monitoring forms blank began photographing registers, recording conversations, and taking pictures at events — because it no longer felt like admin.

Use the data to improve, not just report. The strongest motivator for continued data collection is when staff see the data being used to improve the services they care about. If attendance data reveals that drop-off happens after week 4, and the programme is redesigned to address that, staff see the purpose. Connect data collection to outcome measurement so that the information gathered actually drives decisions.

Addressing Common Concerns About AI-Powered Data Collection

Adopting AI tools in a charity context raises legitimate questions. Here are the most common concerns and how Plinth addresses them.

Data privacy and GDPR. Plinth is designed for the UK charity sector and complies with UK GDPR requirements. Beneficiary data is hosted on UK servers with role-based access controls. Voice recordings are transcribed and processed securely, with clear consent workflows built into the system.

Accuracy of AI transcription. Modern speech-to-text engines achieve 90-95% accuracy in clear audio conditions. Plinth's AI case notes are always presented to the case worker for review before being saved — the AI generates a draft, not a final record.

Handwriting recognition reliability. AI handwriting recognition has improved significantly in recent years. For standard printed and semi-cursive handwriting on structured registers (names in rows), accuracy is high. Where a name is unclear, the system flags it for manual review rather than guessing.

Digital exclusion. One of the key advantages of this approach is that it does not require frontline staff or volunteers to be digitally confident. The person photographing the register needs only a smartphone with a camera. The person recording a conversation needs only a phone with a microphone. These are skills that the vast majority of the workforce already have — UK smartphone ownership stands at 94% of adults (Ofcom Communications Market Report 2024).

Cost. With 69% of charities citing budget constraints as their biggest barrier to digital progress (Charity Digital Skills Report 2025), affordability is a genuine concern. Plinth is priced per organisation rather than per user, making it accessible for small teams and scaling naturally as organisations grow. The time savings from reduced manual data entry typically offset the subscription cost within the first quarter.

Five Steps to Fix Data Collection in Your Charity

If you are ready to move from traditional data collection to an AI-powered approach, here is a practical implementation path.

1. Audit your current data collection. List every form, register, spreadsheet, and system that frontline staff are expected to use. Calculate the total time spent on data entry per week across your team. This baseline will demonstrate the scale of the problem and the potential saving.

2. Identify what staff already do. Which activities already generate potential data? Paper registers, conversations, photos, attendance sheets — these are all data sources waiting to be captured without additional admin.

3. Choose one starting point. Do not try to transform everything at once. If attendance tracking is your biggest pain point, start there. If case studies are what funders keep asking for and you cannot produce, start there. Plinth's features are modular — you can adopt them incrementally.

4. Run a two-week pilot. Ask one team or one programme to try the new approach for a fortnight. Compare the time spent on data collection, the quality of the data produced, and staff satisfaction with the old method.

5. Scale what works. Once a pilot demonstrates value, extend to other programmes and teams. Use the early adopters as champions who can demonstrate the approach to colleagues.

Frequently Asked Questions

How do charities currently collect impact data?

Most UK charities use a combination of paper forms, spreadsheets, and database systems such as Salesforce, Lamplight, or Charitylog. According to the Charity Digital Skills Report 2025, 31% of charities describe their data collection capabilities as poor (Charity Digital Skills Report 2025). Common methods include paper sign-in sheets for attendance, post-session feedback forms, and periodic beneficiary surveys. The challenge is that all of these require manual data entry at some point.

Why do frontline charity staff resist collecting data?

Frontline staff resist data collection because it genuinely competes with service delivery for their limited time. A support worker with 15 appointments per week cannot afford to spend 20 minutes after each one on documentation. The resistance is rational — not a training problem or an attitude problem. Organisations that recognise this and reduce the burden (rather than demanding compliance) get better data.

Can AI really read handwritten registers accurately?

Yes, with caveats. Modern handwriting recognition is highly accurate for standard printed and semi-cursive handwriting on structured documents like registers. Names written clearly in rows are recognised reliably. Very poor handwriting or unusual layouts may require manual correction. Plinth's system flags uncertain entries for human review, ensuring accuracy without requiring perfect handwriting.

Is it GDPR-compliant to record conversations with beneficiaries?

Yes, provided you have informed consent from the person being recorded and a lawful basis for processing their data. Plinth includes built-in consent workflows that ensure recordings are only made with explicit agreement. The beneficiary can withdraw consent at any time, and recordings are stored securely on UK servers with appropriate retention policies.

How long does it take to implement AI-powered data collection?

Most organisations can begin using Plinth's AI data collection features within a few days. There is no complex integration required — staff photograph registers with their phones and record conversations with the built-in app. The system connects to your existing Plinth account, so attendance data and case studies flow directly into your impact reports and case management records.

What if our staff are not tech-savvy?

This approach is specifically designed for staff who are not comfortable with technology. Taking a photograph and pressing a record button are skills that almost every adult in the UK already has — smartphone ownership is at 94% (Ofcom 2024). There is no need to learn a new system, navigate a database, or type data into fields.

How does this approach compare to using shorter monitoring forms?

Shorter forms are better than longer forms, but they still require manual data entry. The fundamental advantage of AI-powered data collection is that it removes the data entry step entirely. Instead of asking "how can we make the form quicker?", the question becomes "how can we avoid having a form at all?" Photographing a register, recording a conversation, and taking event photos achieve data collection through activities that are already happening.

Can small charities afford AI-powered data collection tools?

Yes. Plinth is priced per organisation rather than per user, making it accessible for small teams. For context, the time savings alone often justify the cost: if a team of five staff each saves 3 hours per week on data entry, that is 15 hours of additional direct delivery time per week — equivalent to nearly half an additional full-time worker. The Charity Digital Skills Report notes that charities consistently cite budget constraints as a barrier to digital adoption, which is why per-organisation pricing and rapid return on investment matter (Charity Digital Skills Report 2025).

Recommended Next Pages

• What Is Outcome Measurement? — understand the frameworks that give your impact data meaning
• How to Collect Charity Case Studies Efficiently — a deeper look at AI-powered case study generation
• Proving Charity Impact to Funders — how to turn your data into compelling funder reports
• Impact Measurement for Small Charities — practical approaches when your team and budget are limited
• What Is AI for Charities? — a broader overview of how AI supports the charity sector

Last updated: February 2026