Lessons from the journey to IPO: Part 1 (with insight from Figma, Chime, and Once Upon a Farm)

Going public is a defining moment for any company. It brings visibility, credibility, and access to capital. But for finance teams, it also brings a very different level of pressure.

Suddenly, the team is not only responsible for running the business internally. They are also helping shape the company’s story for investors, preparing for auditor scrutiny, producing reliable forecasts, and making sure every number can be explained, defended, and repeated.

I met recently with finance leaders from Figma, Chime, and Once Upon a Farm - all companies that’ve gone public, all using Pigment to help make it happen. 

In this article series, I’ll be exploring the lessons they learned along the way and sharing them to help you, wherever your business is on its journey.

1. Start with the data foundation

Data quality stood out as one of the biggest determinants of whether IPO preparation felt controlled or chaotic.

Strong planning models depend on clean, governed, and well-understood data. When the inputs are unreliable, even the most sophisticated model will produce outputs that finance teams struggle to defend.

Poor data does not just create one-off issues - it creates technical and organisational debt that compounds over time. When metric definitions change, teams with weak data foundations have to rework GL mappings, rebuild models, revise KPI definitions, and revalidate numbers with accounting, investor relations, auditors, and bankers.

That kind of rework is painful at any time, but during the final stretch before an IPO filing, it’s excruciating.

One of the companies I spoke to shared that their planning process had previously relied on multiple Excel files, which obviously could not support concurrent editing. Team members had to coordinate manually to avoid overwriting each other’s work. 

Moving from that setup to an IPO-ready process required a major effort to clean the data, redesign the process, and migrate to a more scalable system.

Practical steps to take early

Before building sophisticated models, make sure the basics are solid. This is the checklist I was given by the IPO experts I spoke to:

Audit your source systems
Understand what data lives in systems like NetSuite, Workday, or other ERPs and HRIS tools. Identify what is accurate, what is incomplete, and what needs transformation.

Standardise master data definitions
Finance, Accounting, and Business Intelligence should agree on core definitions before those definitions are built into Pigment or any other planning system.

Document data lineage
When auditors ask which transaction codes roll up into a KPI, the team needs a clear, fast, and defensible answer.

Create a single source of truth for financial metrics
Avoid multiple competing versions of revenue, headcount, ARR, active users, or other key metrics across spreadsheets and dashboards.

Assign ownership for data governance
Someone needs to understand both the technical side and the business meaning of the data. That person becomes especially valuable during audit season.

Common data pitfalls

And here’s a list of issues you want to avoid::

• Inconsistent KPI definitions across finance, product, and sales teams
• Multiple versions of ‘the truth’ across spreadsheets and BI dashboards
• Undocumented GL mapping logic that only one or two people understand
• Late changes to metric definitions that force historical data rebuilds
• Inaccurate Workday or HRIS data that creates downstream errors in headcount planning

2. Define your story before you build your models

The second lesson is that IPO prep is as much about storytelling as it is about the numbers - and the storytelling needs to come first.

Several finance leaders described a similar pattern: key metrics went through many rounds of iteration with finance, legal, product, investor relations, and bankers. Each change impacted S-1 and investor decks, but also data pipelines, transaction codes, model logic, and historical reporting behind those metrics.

One team described changing the definition of a core usage metric multiple times. Each change required data engineering to update which transaction codes rolled into the metric, then finance had to revalidate historical data to make sure the numbers still held together. It’s an enormous amount of unnecessary work.

To avoid this rework, the company needs to agree on the story it presents to the market. Which metrics matter most? What does growth look like in this business? How should revenue be broken down in a way that is useful internally and understandable externally?

How to build your narrative

Start the narrative conversation early, ideally at least 12 months before the target filing date.

Do the early metric exploration outside your core planning system. It is useful to test different cuts of the data before committing to a definition. Once the definition is stable, bring it into your planning tool (Pigment, in the case of everyone I spoke to) as a governed and auditable source of truth.

Be careful about publishing metrics that are useful today but may not define the business in 18 months. Once a company discloses a metric, analysts will track it - which makes it a very important choice.

Also decide what will and will not be included in guidance - you need a clear rationale for the market.

In the next article…

Next week we’ll be publishing part two - which will cover building the IPO team, and making your planning tool IPO-ready.

But if you can’t wait that long, the entire series is available at this link for download as an eBook.

‍