Data is one of those topics everyone agrees is important, but very few startups actually get right.

And when it goes wrong, it doesn’t just slow things down. Poor data can derail fundraising, undermine confidence in the finance function, and in the worst cases, contribute to a business going off track entirely. I’ve seen that firsthand, both in early-stage startups and later-stage scale-ups.

This isn’t a new problem. But it is becoming a bigger one.

As finance leaders, we sit in the middle of data more than anyone else in the business. We rely on it to make decisions, we present it to founders and boards, and increasingly, we are held accountable for whether it’s accurate.

Not the tech team.

Not operations.

Us.

Financial Data Is Only the Starting Point

When most people think about data in finance, they think about the general ledger. Transactions. Revenue. Costs. Cash.

That is obviously important. But it’s only one part of the picture.

As you move into more senior finance roles, non-financial data becomes just as critical. Customer behaviour. Conversion rates. Retention. Operational metrics. Delivery timelines. Marketing performance.

Nearly every startup I work with struggles more with non-financial data than financial data. Not because people aren’t trying, but because it’s fragmented, inconsistently defined, and often taken at face value without enough interrogation.

And that creates risk.

The CFO Owns the Question of Accuracy

One of the biggest shifts I’ve seen over the last few years is this: whether the data is accurate or not is increasingly sitting on the CFO’s shoulders.

That doesn’t mean the CFO generates all the data. Tech systems, ops teams, marketing platforms, and finance tools all produce it. But deciding whether it makes sense, whether it can be relied on, and whether it should be used for decision-making usually comes back to finance.

If you are relying on reports without understanding:

• where the data comes from
• how it is defined
• what assumptions sit underneath it

then you are taking a bigger risk than you probably realise.

This is especially dangerous during periods of growth, fundraising, or international expansion, when decisions are being made quickly and with higher stakes.

Data Is a Leadership Tool, Not Just a Reporting Input

Good data allows finance leaders to do two things at the same time:

1. maximise growth
2. minimise risk

That balance is at the heart of the role.

Accurate data helps you especially, spot trends early,  understand cashflow patterns,  forecast realistically, identify ballooning costs and support strategic decisions with evidence

It moves conversations away from gut instinct and towards a more concrete understanding. And that becomes increasingly important as startups mature, raise capital, or bring in more experienced boards who expect decisions to be backed up properly.

Where Startups Typically Struggle

There are two consistent problem areas.

1. Data quality

Data needs to be accurate, complete, and timely. Most startups fall short on at least one of those.

The most common ways I have seen this is (more often than not!) unreliable customer numbers.  This can then hinder CAC, LTV and even inconsistent revenue reporting.  Unclear cash positions can also be a problem as can constant “true-ups”.

If you are already struggling with accuracy at an early stage, scaling will amplify the problem.

2. Data definitions across teams

This is where things often fall apart.

A classic example is the definition of a customer.  Again, I see this more often than not in early stages.

Finance might define a customer as someone who has paid.
Marketing might define a customer as someone who has engaged.
Tech might define a customer as anyone with an email address in the system.

All three can be valid. But only if everyone understands the difference.

When definitions aren’t clearly agreed and documented, teams end up arguing over numbers instead of discussing decisions. Boards lose trust. Founders get frustrated. And finance ends up firefighting.

Agreeing definitions is unglamorous work, but it’s foundational.  Six months before Funding Circle listed, this was our task and by then we had 4 regions, 30 people in the finance function alone.  It was a mammoth project and the learning is, get these agreed early.

 Systems Help, But They Don’t Solve Everything

Upgrading systems can absolutely improve data quality, particularly when teams are operating in silos.

Moving from a standalone general ledger to an ERP can help bring finance, operations, and customer data into one place. Modern options like Light Inc and Odoo have made this more accessible than it used to be, compared to older, expensive systems.

That said, ERPs are not a silver bullet.

They are expensive to implement, take time, and require expertise. A poorly implemented ERP can create just as many problems as it solves. I’ve seen inventory numbers still be unreliable even after significant investment because the setup wasn’t right.

Technology supports good data. It does not replace the need for clear thinking, ownership, and ongoing review.  The data needs to be defined, reconciled and cleaned first.

Automation and AI Reduce Risk, Not Responsibility

Within finance, automation has already changed the game. Most teams are no longer manually keying invoices, and tools like Dext or Hubdoc reduce human error significantly.  (Note: if you want to see the lastest Finance Tech stack recommendations, see Sept 2025's post here.  Subscribe to see bi-annual updates here.)

AI and automation shift effort away from data entry and towards exception handling and analysis. That’s a good thing.

But again, it doesn’t remove responsibility.  Someone still needs to:

• review exceptions
• challenge anomalies
• sense-check outputs
• understand limitations

If anything, automation increases the need for judgement.

Data Is Not a Quick Fix, and That’s the Point

Making data reliable is not a one-off project. It’s ongoing work.

It requires regular review, cross-team collaboration, clear ownership and a huge amount of patience!

If you ignore data quality in a fast-growth business, it will catch up with you. That might sound harsh, but it’s true. At best, it slows decision-making. At worst, it undermines your credibility as a finance leader.

If you want to build confidence in your role, data needs to be a priority, even when it feels uncomfortable or slow.

Every finance leader I work with knows their data isn’t perfect. That’s normal.

What matters is whether you take responsibility for improving it.

Data underpins every strategic conversation you will ever have. Treating it as a leadership responsibility rather than a technical problem is one of the clearest markers of a confident, capable finance leader.

And it’s one of the areas that will quietly define your career over the next few years.

Want to become a confident, strategic finance leader in a startup within the next 12 months? 

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