Whenever I talk to companies about data, I keep hearing the same answers to the same question: “What is your biggest problem when it comes to data?”
We don’t have enough people.
We don’t have enough budget.
Our tools are not good enough.
Or, more recently: we don’t have AI yet.
And almost every time, I feel we are looking in the wrong direction.
Because the problems that actually block companies from using data effectively are very rarely technological. They live one level lower. In how teams work, how responsibility is distributed, how decisions are made, and how organizations think about data in general.
What makes it even harder is that these problems look simple on the surface, but fixing them is anything but easy.
In this article, I want to walk through four of those issues. If you work as an analyst, data engineer, manager, or anyone involved in reporting and decision-making, some of this will probably feel uncomfortably familiar. That’s intentional.
Problem 1: A Misunderstood Single Source of Truth
Single Source of Truth is one of the most overused buzzwords in the data world. Executives love it. Analytics teams repeat it constantly. “We need one source of truth.”
The problem is that in many organizations, this phrase is misunderstood from the very beginning.
The usual ambition looks something like this: let’s take all our data from one place, put it into one system, and all our problems will disappear. If everything lives in one database or one BI tool, it must be consistent. We can finally report sales, margins, costs, KPIs. End of story.
Except that this is not how real companies work.
Modern organizations rely on many systems. Marketing has its tools. Sales has theirs. Finance uses different systems. Warehouses, CRM, advertising platforms, support systems, product analytics. This is normal. Trying to force everything into one “system to rule them all” is usually expensive, slow, and ultimately unrealistic.
The problem is not that data comes from many sources.
The problem is what we do after that.
A Single Source of Truth does not mean a single source of data. It means a single, agreed-upon place where data is collected, combined, modeled, and made available for analysis. It requires a conscious approach to data layers.
First, you have a staging layer, where data is pulled from different systems more or less as-is. Then you have an analytical layer, where you try to connect and transform that data into something usable. Only after that do you build reports, dashboards, and analyses on top.
And this is the critical part: inconsistencies will exist. Always.
A customer buys once on mobile, once on desktop. A transaction system shows something slightly different than a marketing tool. Finance defines revenue differently than sales. These are not system failures. They are a natural consequence of how businesses operate.
The real problem starts when teams refuse to accept these imperfections and instead say: “I’ll just do it better on my own.” Suddenly, you get alternative versions of the truth built on the side. Then another one. And another.
Very quickly, nobody knows which numbers are “the real ones”.
A true Single Source of Truth is not about perfection. It is an organizational decision: we work on one shared data model, we improve it over time, and we trust it more than private, isolated solutions built in spreadsheets or personal databases.
Problem 2: Competition Instead of Collaboration
Competition sounds great. Especially on LinkedIn.
It pushes people to perform better. It rewards talent. It separates the strong from the weak. At least in theory.
In data teams, however, competition can very easily cross the line where it starts doing more harm than good.
In growing organizations, it is almost inevitable that different departments have their own analysts. Marketing analysts, finance analysts, sales analysts. Each team looks at data from a different angle, with different definitions and priorities.
By itself, this is not a problem.
The problem begins when competition becomes personal and political. When bonuses, promotions, influence, and tool ownership enter the picture.
Instead of asking “why do you calculate this metric differently?”, teams start saying “they are doing it wrong”. Passive-aggressive comments appear. Competence is questioned. People start defending their numbers instead of understanding the broader picture.
At that point, the focus shifts from improving data quality to proving that someone else is mistaken.
This is especially dangerous when leadership is not mature enough to handle multiple perspectives. When managers fall into the trap of “I like this person, so they must be right”. Charisma replaces substance. Confidence replaces correctness.
A healthy data culture accepts that different teams may interpret the same data differently. That is not weakness. It is reality.
But this requires conversation. Asking questions. Listening. Aligning definitions where it matters and accepting differences where it does not.
Without that, competition turns into fragmentation. And fragmented data is worse than no data at all.
Problem 3: Tool Chaos
Not that long ago, data stacks were relatively simple. One database. One BI tool. One pipeline.
Today, tools are cheaper, easier to integrate, and everywhere. Add to that a constant fear of missing out. Maybe someone else analyzes things better. Maybe this tool is faster. Maybe that one is more modern. Maybe AI will magically solve everything.
As a result, it becomes very easy to keep adding tools to the stack.
The problem is that every tool comes with more than just benefits. It also brings overhead. Maintenance. Integrations. Costs. Training. Documentation. Edge cases.
When you buy a tool, you are not just buying what it can do well. You are also buying everything it does poorly.
Over time, organizations end up fighting problems on too many fronts at once. And they simply do not have enough people with the right skills to keep everything running smoothly.
This is not an argument against innovation. It is an argument for ownership and governance.
Someone needs to have the authority to say “no”. Someone needs to be responsible for architecture, costs, infrastructure, and long-term sustainability.
Company-wide data policies cannot depend on personal preferences. It cannot be “we use Tableau because our team lead likes it”, “we use Power BI because one analyst is amazing at it”, and “we write everything in Python because programming is cool now and AI helps us code”.
A company is not a collection of personal hobbies. It is a system that must continue working even when people leave, change roles, or join the organization.
Stability matters more than novelty.
Problem 4: Lack of Data Ownership
This is, in my opinion, the hardest and most important issue of them all.
A lack of responsibility.
When numbers do not match, the natural reaction is to look for someone to blame. Someone calculated something incorrectly. Someone integrated data badly. Someone misunderstood a definition.
But data in a company should be treated like a product. And every product needs an owner.
Someone who is responsible for data quality, stability, documentation, and monitoring.
This is difficult, because success in data often looks like nothing happening. Systems run smoothly. Reports make sense. No alarms go off. And that creates the illusion that everything works by itself.
It does not.
Stable data is the result of continuous, often invisible work. Monitoring. Small fixes. Preventive actions. And if organizations do not value that work, people stop taking responsibility.
Then, when something breaks, everyone distances themselves. “Not my problem.” “It came from another system.” “That’s not my area.”
You cannot build a data culture on “congratulations, nothing broke again”. Stability, consistency, and trustworthiness need to be explicitly valued and rewarded.
Because at the end of the day, decisions are made based on this data. And the quality of those decisions directly impacts the success of the business.
Conclusion
These four problems are rarely visible at first glance. It is much easier to buy a new tool. Much easier to hire another analyst. Much easier to say “we need AI”.
It is far more difficult to define ownership, encourage collaboration, control complexity, and build a healthy data culture.
If this article resonates with your experience, there is a good chance that the real issues in your organization are not technological at all. They are foundational.
And foundations, even though you rarely see them, determine whether everything built on top actually makes sense.
If you believe this perspective could help others think differently about data in organizations, feel free to share this article on your social media. The more we talk honestly about real data problems, the better our chances of actually solving them.
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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