In 2014, I was an FP&A Manager at a fashion retail company. Five years into my career. Clear path to CFO one day.

Then I got my Australian PR, packed my bags, and moved to the other side of the world.

The Australian Reality

I started applying for finance roles in Australia. The problem? No local certification.

Interview after interview: "Great background, but do you have Australian CPA?"

No. Not yet. (I got it later, just to be safe.)

Finance doors kept closing. I had to pivot.

The "Excel and Numbers" Strategy

I stopped searching for "Finance Manager" and started looking for anything requiring:

I found it: "Business Analyst"

I applied.

The Interview

The interview was surprisingly simple.

Question 1: "Can you create pivot tables?" Yes. Had to demonstrate how.

Question 2: They showed me a report. "Analyse this."

I was nervous. Very nervous. I didn't know the word "ratio" back then, so I remember saying "coefficient." Which was also correct.

They hired me the next day.

Pivot tables and a coefficient. That's what got me my first data role in 2014.

The salary was very low, slightly above minimum wage, but I needed to get my foot in the door.

But let me be clear: the market isn't like that anymore.

What I Transferred from Finance

1. Understanding business metrics

Five years in finance meant I understood:

When stakeholders asked for analysis, I knew what they actually needed.

2. Attention to detail

Finance taught me: numbers must reconcile. Always.

When my query returned 148 rows instead of 1.5 million, I knew immediately something was wrong. I also knew never to delete anything from the data, whilst one of the juniors who was working with me kept deleting numbers calling them outliers. Which actually were our VIP customers, and then we couldn't reconcile any report.

What I Had to Learn

1. Technical skills (SQL, Python, visualisation)

This was the obvious gap. I came in knowing Excel. I had to learn everything else.

But these are learnable. Documentation is free. Practice makes you better.

It took time, but I got there. Also, you'd be surprised how many colleagues are happy to help you when you're stuck and answer some questions.

2. Working with messy, ambiguous data

Financial data is clean and structured. Operational data? Disaster.

Nulls everywhere. Inconsistent formats. Vague requirements.

"Analyse customer behaviour" could mean anything.

I had to learn to ask clarifying questions and work with imperfect data.

3. Dealing with uncertainty

Finance has right answers. Data analytics? Everything is judgement calls.

When do you stop cleaning data? When is analysis "good enough"? What if stakeholders disagree with your findings?

These aren't technical questions. They're judgement calls you develop over time.

There's also another path to transition into data—from computer science or software engineering.

What CS Graduates Have to Learn

CS graduates coming into data analytics face their own gaps:

1. Business context

Knowing Python doesn't mean understanding why inventory turnover matters or what good conversion rates look like.

They can write perfect code but not know what question to answer. Or if this code is returning the right data.

2. Stakeholder communication

Explaining technical concepts to non-technical people is a skill. So is managing expectations and handling pushback.

3. Domain knowledge

Understanding retail, healthcare, finance, or whatever industry they're in—this takes years.

They learn the tools faster than I did. But I already knew the business.

Neither Background Is "Better"

Finance/business backgrounds bring:

But need to learn: Technical tools (SQL, Python, visualisation)

CS/technical backgrounds bring:

But need to learn: Business context, communication, domain knowledge

Both paths work. Both have gaps to fill.

If you're coming from a non-tech background (like I did):

What you need to learn:

If you're coming from a CS background:

What you need to learn:

Both paths are valid. Both require work.

For finance/business people learning technical skills:

SQL:

Python for Data Analysis:

And obviously YouTube - there are so many great creators that can teach you anything from scratch; you just need to find whose teaching style you like the most.

For CS/technical people learning business skills:

Business fundamentals:

Industry-specific knowledge:

Communication and storytelling:

Don't try to learn everything at once. Pick ONE resource, commit to 30 days, then build a project. And practice, practice, practice.

Keep pushing 💪,

Karina

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