Most of the advice I usually share on my blog or YouTube channel is aimed at people just entering the IT industry. I try to ensure those tips are structured, ethical, and methodical. I explain step-by-step how to build competencies, learn new tools, and craft a solid portfolio. But this article is going to be different. It will be politically incorrect, slightly cynical, and brutally honest.
I want to show you my actual analytical experience, stripped of any corporate sugar-coating. I am going to give you six tips that, from my experience, will help you get promoted much faster than the tedious, textbook approach of simply collecting more certificates. Let me warn you right away: these tips are controversial. They certainly won’t win applause from analytical purists who believe the only path to success is flawless code and perfect knowledge of statistics.
I will show you the tricks I used in my daily work, which allowed me to transition from a junior analyst to a Data Architect in a relatively short time. Using them requires a specifically calibrated moral compass. I don’t expect you to be the analytical teacher’s pet, but I guarantee that applying this knowledge will make you immune to corporate politics. For beginners, this will provide a harsh look at what real data work—and, let’s face it, competition with other analysts—actually looks like. Because trust me, the people who are good at this game will be using these exact tactics against you.
Excel Is Not a Calculator. It’s a Tool for Selling Dreams
Let’s start with good old Excel. However, I am not going to explain hidden functions, advanced macros, or complex pivots. I want to tell you what I actually used it for when interacting with the board and stakeholders. I used Excel to communicate with business people, but above all—to sell them dreams.
When I dialed into a meeting, shared my screen, and showed a spreadsheet, my main goal was never to prove that I brilliantly understood the math or to show off my array formulas. My goal was to make them feel that, together, we had control over an incredibly chaotic and complex business situation. Business loves the illusion of control.
To achieve this, I had to use massive simplifications at key moments in the meeting. The trick was ensuring that the business didn’t notice these simplifications right then and there. You have to be clever and have a good sense of timing. Some of these mental shortcuts can be refined and described in detail later behind the scenes, while others should simply be silently accepted and swept under the rug for the sake of making a quick decision. You would be surprised how many directors bought into this. It built my reputation as someone who doesn’t split hairs, who is effective, and who speaks their language. In your hands, Excel should be a magic wand that turns corporate chaos into neat, green cells.
Power Query and the Curse of Pedantry: Why You Must Kill Edge Cases
The next case is Power Query and all sorts of data cleaning and automation tools. As we all know, an analyst’s job is 80% preparing and transforming data. However, there is a trap here that can kill the career of even the most talented specialist: pedantry.
The correct, academic school of data analysis says that true business gold lies in the “edge cases.” That it is there we will find the anomalies that will change the fate of the company. Let me tell you right now from a practitioner’s perspective: that’s nonsense.
In any sufficiently large dataset, there will be weird, broken situations that will consume massive amounts of time to explain logically. I run a business myself now, and I have weird clients. They make up maybe 3% of my transactions. Honestly? From an analytical standpoint, I couldn’t care less about them. I am not going to spend a dozen hours trying to understand why the system double-counted something in one highly specific, obscure scenario.
My approach was brutal: I just plowed through the edge cases. I filtered them out, deleted them, eliminated them from the dataset. I noticed where the thin line separating standard processes from business anomalies was drawn, and I threw out everything beyond it. Why? Because in the corporate world, the faster person wins. If I deliver a finished, 97% accurate report in two hours, and my colleague spends three days on it because he’s trying to understand a single system error from last year—I am the one getting the promotion. I will talk about speed in a moment, but remember: perfectionism in data cleaning is your greatest enemy.
SQL as the Theater of Competence: Let Business Think They Will Perish Without You
SQL is the most interesting tool in this context. It’s a tool that the business side completely misunderstands, finds deadly boring, and assumes is incredibly difficult. It is very hard to sell the value of your work by showing pure code. Let’s be honest—hundreds of lines of text, dozens of nested subqueries, fifty joins… Nobody cares, and nobody outside of another analyst will appreciate it.
So how do you use SQL to build your career? You have to show people why they are paying you in the first place. When you are on a call discussing a new report or a data pipeline, your hidden agenda is to build an aura of expertise in the eyes of the decision-makers. To do this, you must present your work as something extremely complex.
This requires an intelligent balance. On one hand, you must explain your findings in simple, business-friendly language; on the other, you deliberately showcase the massive complexity “under the hood.” I often shared my screen showing an incredibly elaborate data model, full of relationships, branches, and tables. Another experienced analyst would look at it and think: “Oh please, it’s just a basic star schema, what’s there to admire?” But remember—other analysts aren’t the ones giving you raises. Raises come from the manager, the director, and the board. In their eyes, that diagram looks like the blueprint for a nuclear reactor.
When showing the results of your work, speak confidently, draw simple conclusions, but at the same time, subtly emphasize how complex the operation was. The business must be aware that the company is facing a giant technological challenge, and you are the only navigator who can guide them safely through it.
Dashboards and Emotion Management
Many analysts consider creating dashboards and visualizations to be the most enjoyable part of the job because it has an aesthetic and creative flavor. However, it is easy to fall into the trap of pleasing yourself (playing with colors or fancy chart types) instead of delivering value to the business. How do you make your visualizations truly grab attention and build your position?
In the corporate world, attention equals money. If you capture the attention of key people, you are important to them, and that directly translates into your earnings. The key to grabbing attention isn’t beautiful design; it’s emotion. What you show on the chart must strike at the most basic instincts.
The strongest impact comes from the contrast between a strong negative emotion and a positive one. The ideal dashboard should show, on one hand, the specter of a massive crisis (e.g., a drastic drop in margin on a key product), and on the other—the promise of a quick victory (e.g., dynamic growth in a new sales channel). It’s a mechanism familiar from video games: you are facing a terrifying boss, but you see its health bar blinking red, and one good hit will give you the win.
The picture you present must be somewhat contradictory. A director looks at it and thinks: “Wait, everything is crashing here, but we’re growing over there. What is going on?” And that is exactly when you step in. Paradoxically, the worst thing you can do for your career is create a report that is 100% self-service and works without your commentary. If that happens, the business will quickly think: “If this report is so clear, why do we need an expensive analyst? Let’s hand it over to AI.” They need to need you. You are essential as the arbiter between these extreme emotions. You have to step in and authoritatively state what is statistically significant, where the correlation is, and what is merely coincidence. Cold numbers should hit hot emotions, and you must be the firefighter who puts it out.
AI, Errors, and the Art of Being First
Artificial Intelligence has revolutionized our world. However, as analysts, we do not work in a vacuum. We work in teams, we have bosses, and we are constantly compared to others. Today, AI should serve you for essentially one purpose: you must be faster than everyone else.
Let’s be honest—sometimes this means you have to accept a higher risk of making mistakes. When using LLMs to generate code or queries, you don’t always fully control the process. You have to trust that your own intelligence and cleverness will defend you if something goes wrong.
If you want to be really good at this profession—or at least as clever as I tried to be—you must master the art of falling softly. This means that when you make a mistake due to rushing, you can use your soft skills to reasonably explain it away, downplay its significance, and move on without damaging your reputation.
Whether you are extremely cautious or just somewhat careful, you will eventually make mistakes anyway. That is the nature of the job. But being first, delivering results faster than any other department in the company, gives you an absolutely massive advantage. The classic movie quote goes: “Be first, be smarter, or cheat.” Cheating in IT ends disastrously—sooner or later, the system will catch you. Being the smartest is hard because our intelligence cards have already been dealt, and not everyone has a Mensa-level IQ. But being first? That is something you have absolute control over. Organize your work so that you always deliver ahead of the deadline.
Stop Just “Doing Tasks”
The biggest tragedy of many analysts I’ve met is that they simply execute their assigned Jira tasks diligently. They close tickets, send emails, and wonder why they haven’t been promoted in years, while others, often technically weaker, move up.
Notice that brilliant people often don’t answer the questions they are asked directly. They answer the underlying problem. It’s the same with being an analyst. Your job isn’t to “do an analysis.” Your main job at work is to deliver hard evidence for why the company should pay you more. Data analysis is just a trade skill, a means to an end.
You can use very different tools for this. It might be your contagious enthusiasm and assurance that the answer is hiding in those messy logs and you will surely extract it. It might be reporting something trivial, but at a pace that puts other departments to shame. It might be deliberately intimidating the business with a complex data model so they realize your value.
Does all this make me an immoral person? Perhaps in the corporate environment, the concept of morality is very fluid. I cared about effectiveness. I cared about being better than others and climbing the ladder. And it wasn’t just about stroking my own ego. In the corporate hierarchy, a simple rule applies: the higher you are, the more comfort, autonomy, and control you have over your life. And controlling the situation is simply increasing your distance from the source of pain and stress. This sets up your professional life in a much more comfortable way than people think.
If this approach intrigues you and you want to build solid analytical foundations to freely apply these “immoral” tips in practice, a great step would be joining KajoData Space. We teach not only tools there but, above all, how to function effectively in the job market.
I hope this article shed new light on how you perceive your career in IT. Please share it on your social media—on LinkedIn, Facebook, or Twitter. Let other analysts learn the rules of this game too!
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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