A common reporting problem: data lives in five or six places that don’t speak to each other. The CRM has one version of the customer, the accounting system has another, and the operational numbers sit in a spreadsheet someone owns. So a chunk of every week goes on exporting, pasting things together, and hoping nothing broke along the way. By the time the report lands, half of it is already stale. The analytics tools meant to fix this usually come with their own catch, which is that you end up reshaping how you work to fit how the software thinks.
This tends to come to a head at a particular point. Spreadsheet formulae start breaking, version control becomes a daily argument, an audit or a GDPR query exposes how loose the data controls are, leadership wants weekly figures from a monthly process, or you take on another site and the reporting suddenly doesn’t add up across the group. That’s usually when people start looking seriously.
I build analytics and reporting software around how your business runs: the dashboards and scheduled reports you need, connected to the systems you already have, and owned outright. No per-seat subscription that climbs every time you add a person, and no platform with a thousand features you’ll never touch.
Where off-the-shelf analytics tools tend to break down
Tools like Power BI, Tableau and Qlik are good, and for standard sales and finance dashboards they may be all you need. I’ll say so plainly if that’s your situation. Once needs get specific, the same complaints come up:
- Per-user pricing punishes growth. Licences are charged per seat, so the bill climbs every time the team does. Plenty of businesses end up sharing viewer logins to keep costs down, which quietly wrecks their audit trail.
- The data model isn’t your data model. Out-of-the-box structures rarely match real business logic. Commission tiers, approval hierarchies and sector-specific calculations end up stitched together as scattered workarounds that nobody can maintain.
- Real-time often turns into weekly. The live dashboards shown at the demo frequently need expensive infrastructure to deliver, so most teams settle for a daily or weekly refresh.
- Legacy and niche integrations are where it hurts. Standard connectors cover the big platforms. They struggle with older systems, proprietary databases and the smaller SaaS tools, and that’s where the manual reconciliation lives. Third-party connectors also break when an upstream API changes, and the data goes stale until someone notices the numbers look wrong.
- The basics sit on a higher tier. Scheduled distribution, row-level security, single sign-on and white-label embedding are often extras.
- Sign-off isn’t enforced. If finance needs preparer, reviewer and director approval that changes with the value of the figures, a generic tool can’t enforce it, so it happens by email.
- Dashboard sprawl creeps in. Without firm governance, you end up with hundreds of near-duplicate dashboards and no single version of the truth.
- The headline price is rarely the real price. Implementation, custom integration, data migration, training and support tiers routinely push total cost well past the original estimate.
The result is a stack of workarounds, reports nobody fully trusts, and decisions made on last month’s numbers. A lot of businesses end up paying for capability they don’t use while still missing the specific reports they need.
What I build instead
A custom build isn’t the right answer for everyone. It earns its place when your reporting is tied to how your business works rather than how a generic tool expects it to.
Your business logic, modelled once
I spend time understanding how your business runs before writing anything, including the awkward bits: tiered commissions, multi-step sign-off, the sector-specific calculations a generic tool gets subtly wrong. That logic goes into the schema and a single semantic layer, so a metric means the same thing everywhere and reports stay consistent.
No per-seat licence
Instead of an open-ended subscription, you get software you own, with hosting and support costs that are predictable rather than scaling per user. The upfront cost is higher than signing up to a SaaS plan, but you’re not paying more every time you add someone, and the system is an asset on your books.
It connects to what you already run
I connect your CRM, ERP, accounting software, e-commerce platform and operational databases through custom APIs and data pipelines, so the manual copy-paste between systems goes away. Where a source is difficult, such as a heavily normalised ERP or an API with tight rate limits, you hear about it during scoping rather than mid-build.
UK compliance built in, not bolted on
Your solution handles UK GDPR, encryption in transit and at rest, role-based access and audit logging from the start. Where data residency matters, hosting is in UK data centres or on your own infrastructure. For regulated sectors I build to specific needs, such as immutable audit trails, longer retention for financial records, or row-level security so people only see the data they’re entitled to.
It can grow without a rebuild
The system is modular, so adding a data source, a reporting dimension or another permission group later is an extension, not a fresh start.
You’re not locked in
The data model and dashboards belong to you, not a vendor. If your needs change, you decide the roadmap and the priorities, on your timeline.
The person you talk to builds it
I write the code myself and bring in a trusted specialist where a job needs one.
What I typically build in
Every project is scoped to what you need, but most include some version of:
Dashboards
Interactive visualisations with drill-down, built around the KPIs you make decisions on, with period-over-period comparison so trends are obvious. Each KPI gets an agreed written definition before it’s built, and refresh is live only where a decision needs current numbers, with batch refresh everywhere else so you’re not paying for real-time you don’t use.
Data integration and pipelines
Scheduled imports from your CRM, ERP, accounting and operational systems, with the ETL and transformation logic to blend them cleanly. Validation rules catch the discrepancies, such as duplicated customer records or mismatched date formats, before they reach a report.
Self-service and ad-hoc queries
A query layer so business teams can answer their own questions without waiting on IT, governed by the shared semantic layer so the answers stay consistent.
Mobile access
Reports and insights from any device, with role-based permissions and offline access where it makes sense.
Security and access control
Encryption, UK hosting options, single sign-on and access controls granular enough to keep sensitive data where it belongs, including row-level security where one dashboard needs to show different people different records.
Audit trails
A full log of data changes and report access, which you’ll want for compliance, accountability and any audit conversation.
Forecasting and anomaly detection
Where the data supports it, models trained on your own history to project trends, flag anomalies and surface things worth a closer look.
Scheduled and automated reports
Dashboards answer questions people go looking for. A lot of reporting is the other way round: the same pack produced every week or month, by someone copying numbers between spreadsheets. Automating that is often the first thing worth building.
- Templates that match what you send now. Your branding, column order and sign-off fields, so the board pack or client report looks the way your accountants, clients or regulator expect.
- Scheduled and conditional delivery. Reports land in the right inboxes on the right day as PDF, Excel, CSV or a presentation, or go out only when a threshold is crossed, with alerts to email, Slack or Teams.
- Approval before distribution. Preparer, reviewer and sign-off steps, with extra steps when the figures cross a value you set, recorded in the audit trail rather than an email chain.
- Run history and retries. Every run is logged, and a failed overnight run is retried and flagged rather than discovered on Monday.
- Versioned definitions. Report definitions, data source queries, schedules and destinations are versioned, so you can see what changed when last month’s figure moved.
A sensible first release is usually the handful of reports that take the most hours to produce, running against your main data source with scheduling, role-based access and audit logging.
How a project runs
I’d rather get the first phase right than rush it. Analytics projects mostly fail for unglamorous reasons: weak requirements, poor source data, integrations that don’t hold up, and scope quietly expanding to cover every historical report ever produced. The plan deals with each of them.
Discovery and planning
I interview the people who produce and read the reports, watch how the current process runs, pin down the data sources and integration points, and assess the state of the source data. A common surprise here is undocumented business logic buried in spreadsheet cells and legacy reports; finding it early is far cheaper than finding it later. We agree what success looks like and where the project stops.
Development and integration (about a month for the first release)
I build the connectors and pipelines, set up the data model and semantic layer, and develop the dashboards, working in tools like Python, SQL and the right warehouse for your data volume. Core functionality first, extras after, with regular check-ins so you see progress rather than a reveal at the end.
Testing and deployment
Quality assurance covers user acceptance testing, performance checks and security validation. The new system’s figures are reconciled against your existing reports, because data can migrate cleanly and still be wrong. Where it makes sense, the new system runs in parallel with the old one for a short period so you can trust the numbers before you depend on them.
Training and support (ongoing)
I provide documentation and run hands-on sessions, pitched to the role: brief for people who read dashboards, deeper for report builders, full handover for administrators. After go-live you get a 30-day fix guarantee and the first month of hosting free, then an optional monthly plan sized to what you need.
The economics of building versus subscribing
Custom development costs more upfront than a SaaS subscription. The comparison that matters is the total cost of ownership over several years, not the headline licence price.
With per-user BI tools, the licence is only part of the bill. Implementation and onboarding, data migration, custom integration work, training and premium support tiers all sit on top, and per-seat pricing keeps climbing as your team grows. It’s common for the all-in cost to land well above the original estimate.
A custom build inverts that shape. The cost is mostly upfront, then hosting and support are predictable and there’s no per-seat fee. What you trade for that is owning a bit of ongoing maintenance, particularly when a source system changes its API and a connector needs attention.
Where building tends to pay off:
- No per-user pricing, so growing the team doesn’t grow the bill
- No surprise price hikes or features locked behind a higher tier
- Decisions made on accurate, current data instead of last month’s export
- Less manual reconciliation and fewer hours spent producing the same pack every month
- An asset you own rather than a subscription you rent
Most builds cost £2k to £10k and go live within a month, with the price fixed at the end of First Gear, a £495 prototype built on your own data. Where it lands depends on how many systems are being integrated, how clean the source data is, and how much bespoke logic the reporting carries; a multi-source platform with governed self-service is split into fixed-price phases. If off-the-shelf is the better-value choice for you, I’ll tell you.
Where this tends to pay off
Some sectors where custom analytics earns its keep, and the kinds of data and questions involved:
Retail and e-commerce
Online and in-store sales pulled together with inventory and customer behaviour, drawing on POS, e-commerce platforms, payment gateways and your CRM. Useful for product performance, sales by channel, inventory turnover and customer segmentation by recency, frequency and value.
Professional services
Billable hours, project profitability and resource utilisation, blending timekeeping, the practice management or project tool, accounting and CRM. The detail that matters most here, such as utilisation targets, write-offs and revenue per client, is exactly where generic dashboards fall short.
Manufacturing and supply chain
Production yield, defect rates and machine downtime alongside supplier performance, lead times and demand forecasting, drawn from ERP, MES and quality systems.
Healthcare
Patient outcomes, length of stay, capacity planning and revenue cycle reporting, with audit trails, encryption and access controls treated as a requirement rather than an extra.
Financial services
Regulatory reporting, variance analysis, AR/AP ageing and exception reporting, where immutable audit trails and longer data retention for financial records are non-negotiable.
Logistics
Fleet performance, delivery times, shipping cost and route analysis across telematics and operational systems.
Marketing agencies
Campaign performance, acquisition cost and multi-touch attribution, with branded client reporting that can be white-labelled rather than carrying a vendor’s logo.
Construction
Project costing, budget against actual, subcontractor performance and safety compliance, often across multiple sites that need to consolidate cleanly into a group view.
Charities
Donor pipelines, restricted fund tracking, grant reporting conditions and programme impact, produced for each funder in the format they ask for.
Education
Attendance, attainment and enrolment rolled up by year, term, class and student, drawn from the school’s information system.
Each build gets the sector-specific calculations and workflows a generic product doesn’t model.
Common Questions About Custom Analytics and Reporting Software
How does a custom build compare on cost to a BI subscription? ›
A custom build costs more upfront than signing up for Power BI or Tableau, but the comparison shifts over time. Per-user BI licensing climbs as your team grows, scheduled distribution, row-level security and single sign-on often sit on higher tiers, and the headline price rarely includes implementation, custom integration, data migration or training. A bespoke system is a one-off build, most often £2k to £10k at a fixed price, with predictable hosting and no per-seat fee. I won't promise a payback date because it depends on your team size and how much manual reporting you're replacing.
What's a realistic development timeline? ›
Most first versions go live within a month, at a fixed price agreed at the end of First Gear. That first release typically covers one or two priority data sources and three to five core dashboards, then expand from there. That early release means you use it before the whole thing is finished, and your feedback shapes the next phase.
How do you handle updates and changes? ›
The system is modular, so adding a data source, a new metric or another permission group later doesn't mean a rebuild. After the 30-day fix guarantee and a free first month of hosting, support is an optional monthly plan. Custom connectors do need occasional upkeep when a source system changes its API, and that gets priced into scoping.
Can you integrate with our existing systems? ›
Yes. I connect to accounting software like Xero, Sage and QuickBooks, CRMs such as Salesforce, HubSpot and Dynamics, ERPs, e-commerce platforms and proprietary databases, using APIs, direct database links or file-based feeds. Some sources are harder than others. Salesforce API rate limits often mean batched refreshes, and heavily normalised systems like SAP and Oracle need careful schema mapping. I flag the awkward ones during First Gear rather than after.
What about data security and compliance? ›
Every build includes UK GDPR handling, encryption in transit and at rest, role-based access and audit logging. Hosting can be in UK data centres or on your own infrastructure where data residency matters. For regulated sectors I build to specific requirements, such as immutable audit trails and longer retention for financial data, or row-level security so people only see the records they should.
When is off-the-shelf BI the better choice? ›
Often, and I'll tell you if that's your situation. If your data volumes are modest, your reporting needs are standard sales and finance dashboards, and you're comfortable on a mainstream stack, Power BI, the free Looker Studio or Metabase will likely serve you well for less, and an open-source engine like JasperReports suits pixel-perfect printed reports. A custom build earns its place when your business logic is non-standard, your integrations are awkward, compliance is demanding, or you want to stop renting your reporting infrastructure.
Can it generate and send reports automatically? ›
Yes. Reports run on a schedule and land in the right inboxes as branded PDF, Excel or CSV, or go out only when a threshold is crossed. Failed runs are logged and retried, so a broken overnight report is caught that morning rather than discovered at the Monday meeting. Where figures need sign-off before they're sent, a preparer, reviewer and director approval step can sit in front of distribution.