In the world of data analysis, we often fall into a certain trap. We focus entirely on tools. We learn the newest Python libraries, master advanced SQL joins, build complex models in Power BI, and we believe that is enough to succeed. As a data analyst and data architect with over seven years of professional experience, I can tell you with absolute certainty: that is only half the journey. The true value of an analyst does not lie solely in their technical skills, but in how well they understand the business for which they are analyzing those data.
Financial aspects are absolutely crucial here. While in logistics or e-commerce things are quite tangible—IT systems automatically track traffic, we see inventory levels dropping, and empty shelves practically scream that we have a turnover issue—in finance, the situation is much more complex. Costs have a tendency to slip through our fingers in an incredibly elusive way. If we do not understand basic KPIs (Key Performance Indicators) and do not know how they should be properly calculated, we simply cannot take good care of the business’s growth. This applies whether you are working full-time in a huge corporation or running your own solo business.
Today, I have prepared a breakdown of five essential financial KPIs for you. I will explain why they are so important and how you should look at them from the perspective of someone whose job is to “figure out” reality, rather than just mindlessly refresh dashboards.
1. COGS – Cost of Goods Sold
Let’s start with the basics: COGS. It is a fairly well-known acronym that stands directly for the cost of goods sold. It seems trivially simple. The goal is just to have a hard, reliable baseline to calculate our margin. We need to know exactly how much it costs us to produce what we sell—meaning how much we physically have to take out of our own pocket for the product to reach the customer.
However, what sounds simple in theory often slips out of analysts’ hands in practice. Why? Because COGS is not always exclusively a material cost, like buying plastic to manufacture toys or wood for furniture. Very often, especially in today’s economy based on services and digital products, we have to calculate this metric in the context of time.
Let me give you an example from my own backyard, KajoData. When I create a new online course, I do not pay a factory to produce it. But that does not mean the course costs me nothing. I have to calculate how much my standard hourly rate is. If you combine a full-time job with your own solo business (as many beginners do), you absolutely must value your hourly rate. When I calculate how much time it costs me to create new materials, it guides me perfectly in business predictions. I know how much budget I should have if I wanted to outsource the production of such a course to someone else. I compare the cost of acquiring these goods with what I would normally earn.
And this is exactly where the invisibility of costs, which I mentioned at the beginning, lies. If you have a business where you first put down ten thousand in cash to sell goods for fifteen thousand, the math is simple. But if you don’t see it, and you invest your time instead, you forget about it and miscalculate your margin. You fail to account for the hours you have to burn for something to even be created.
2. Contribution Margin
Another extremely important metric is the Contribution Margin, often abbreviated as CM. Usually, in finance, we are told that we just need to calculate all our costs. However, throwing everything into one bag is analytical suicide. We always try to divide our costs into two main categories: fixed costs and variable costs.
The Contribution Margin is precisely the amount left from sales after deducting all variable costs. Variable costs are those that scale with sales. You sell more physical products—you pay more for couriers and packaging. On the other hand, fixed costs are something we will have to pay regularly anyway, regardless of whether we sold a hundred items or zero in a given month (e.g., office rent, fixed software licenses).
Why is this so important for you as an analyst? Because it is a direct metric that allows you to assess the extent to which a given company can afford to create promotions and safely scale the business. The bigger the promotion we run and the more we cut prices, the more our revenue from each single transaction drops. This means that there is less and less money from this contribution margin left to cover the fixed costs. A poor assessment of the Contribution Margin when planning Black Friday could result in the company generating record-breaking turnover, but simply going bankrupt from a cash flow perspective.
3. OPEX – Operating Expenses
Anyone who has spent even a little time in the broad IT or finance sectors is definitely familiar with the term OPEX. Operating expenses are nothing more than the ongoing costs of running a business. I am bringing this up so that during a meeting with the board, you won’t be surprised when someone says: “our OPEX this quarter is definitely too high.”
Generally, very different things are included in operating expenses: salaries of administrative staff, software subscriptions, cloud tools, accounting, or legal services. The point is that if we, as an organization, manufacture or provide something continuously, we must have a fixed pool of money to keep this whole machine running. This is exactly what OPEX tells us about.
I will not give you one magical mathematical formula for calculating OPEX here, because it will vary incredibly depending on how a given company classifies its expenses. But you must know that OPEX is the silent killer of great ideas. You can have a perfect, profitable product that costs pennies to make, and customers love it. But if the machine in the back—meaning that huge office, incredibly expensive legal services, dozens of software subscriptions nobody uses—is too heavy, OPEX will simply crush you. The idea itself was brilliant, but the infrastructure built to support it turned out to be too costly to sustain.
4. Net Profit Margin
We are moving on to my absolute favorite financial KPI, because it touches the very essence of running any business. The Net Profit Margin tells us what our final net profit is, or essentially—what percentage of our total revenue is the money that actually stays in our pocket. It is the classic ratio of profit to revenue.
We can rub ourselves with various metrics, optimize variable costs, reduce OPEX, and brag to investors about sales growth. But if, amidst all this, our net profit margin stagnates or—even worse—shrinks, then all this scaling makes absolutely no sense. Increasing revenue does not always mean building a healthy business.
Not every company is Uber or OpenAI, which, thanks to massive investor capital, can secure a five-year buffer to burn cash and generate losses in the name of capturing the market. Sure, that leeway is sometimes needed. But if you are analyzing data for a smaller company, an e-commerce store, or helping a solopreneur, the Net Profit Margin is your most important signpost. The lack of a healthy net margin will destroy such a business incredibly fast.
I often see a mistake where people become mesmerized by the return on ad spend (ROI). We generated an amazing campaign, put four thousand into ads, and pulled out sixteen thousand. Great, right? Well, not necessarily. Only when you gather all the data—production costs (COGS), variable shipping costs, OPEX—and subtract them from that sixteen thousand in revenue, will you see the truth. Comparing this profit with the revenue will mercilessly show you how much real profit the company generates from every single dollar earned.
Connecting these metrics technically and from a business perspective is exactly what we teach at KajoDataSpace. If you are looking for a complete path and the right materials to level up and get a job as an analyst who truly understands these dependencies, I highly encourage you to check out this initiative. We have already helped many people build solid competencies and find great jobs.
5. F-V Ratio (Fixed to Variable Cost Ratio)
I saved what is perhaps the hardest metric to map correctly, but analytically the most interesting, for last. The proportion of fixed to variable costs. This is something that large corporations and small entrepreneurs alike can calculate. There is no complicated mathematical formula here. You do not have to divide by ten, multiply by three, and dance on one leg. It is all about a deep, logical understanding of the nature of the company’s expenses.
The analyst’s task here is to reflect on what the ratio of these costs is and what grows faster as the business expands. Let’s assume the business is growing, and sales are going up. What grows along with it? Variable costs or fixed costs?
The healthiest situation is one where, along with the growth in sales volume, our variable costs mainly increase (e.g., we buy more goods to resell), but our fixed costs stay in place or grow very slowly. This means the business is excellently optimized and scalable.
But we often encounter the opposite situation. The company grows, so it hires directors, rents larger offices, and buys more expensive IT systems. Fixed costs skyrocket. And suddenly we fall into a trap. Because fixed costs are rigid (you cannot cancel a lease agreement overnight), the company must constantly “churn out” a massive sales volume just to stay afloat. A toxic pressure emerges: we must grow at all costs, otherwise the fixed costs will kill us.
Mapping this requires excellent communication between the analyst and the business side. You must know whether a given marketing expense is a fixed cost (salaries for the ad agency) or a variable one (commission per acquired lead). Many businesses have gotten into trouble because they misjudged the structure of their own expenses.
Summary
If you are seriously thinking about a career in data analysis, you must understand one crucial thing: limiting yourself only to technical skills is a dead end. Just because you can write a brilliant SQL query, pull data from an API, and join two massive data frames in Pandas, means you only have the tools of a craftsman. The business does not pay you just for clicking on a keyboard.
Business needs a combination of your technical toolkit with communication skills and a deep understanding of finance. Bosses and managers do not want to have to explain to you that a customer has an acquisition cost, that a product has a lifecycle, and that revenue is not profit. Data analysis is actually the fascinating, nuanced process of figuring out the reality around us based on numbers. And that is absolutely the best part of this job.
If you found this text useful and know that it could help someone better understand the financial side of our industry, I would be very grateful if you shared this article on your social media. Knowledge is worth sharing, and I will see you in the next materials!
The article was written by Kajo Rudziński – analytical data architect, recognized expert in data analysis, creator of KajoData and polish community for analysts KajoDataSpace.
That’s all on this topic. Analyze in peace!
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