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Articles on analytics engineering, data and artificial intelligence — no jargon, focused on business decisions.
Data consulting: when to hire and how to choose the right partner
Not every data problem is solved by one more tool or one more hire. Here is when it is worth bringing in a data consultancy — and how to tell a good partner from a report factory.
Read articleAn MVP in 30 days: what you can build (and what you can't)
Promising an MVP in 30 days has become an agency cliché. The truth is more useful: some products fit that timeline comfortably, others don't fit at all — and knowing the difference before you hire saves months and tens of thousands.
Read articleHiring a Data Engineer: full-time, freelancer or allocation? How to decide
Hiring a data engineer is expensive, slow and risky — and the wrong model costs months. Here is how to choose between a full-time hire, a freelancer and allocation, based on your moment.
Read articleData Warehouse: what it is, when it is worth it and how to start
You keep hearing about a data warehouse, but are not sure your company needs one? Here is, without jargon, what it solves, the signs it is time, and how to take the first step.
Read articleHow much does it cost to build a custom system in 2026
The honest answer is not "it depends" — it is a price range you can actually explain. We compare freelancers, traditional software factories, in-house teams and AI-assisted on-demand development, and show what really drives the price.
Read articleAI process automation: where to start (without an endless project)
Automating processes with AI sounds complex and expensive — but the best gains come from starting small, with a boring, repetitive process that steals hours from your team every week.
Read articlePower BI vs Tableau vs Looker: which to choose (without the hype)
All three are great BI tools — and the "best" one depends less on technology and more on your context. An honest comparison so you can decide with judgment, not by trend.
Read articleAI agents for companies: use cases that pay off (and the ones that don’t)
Every company wants an AI agent, few know what for. The result is a flood of "pilots" that dazzle in the demo and die in a drawer. Let us separate what pays off from what is just hype.
Read articleHow much does a Power BI dashboard cost? It depends on what it saves you
It is one of the first questions we hear — and a fair one. But the price of a dashboard tells you almost nothing without the other half of the math: what it gives back.
Read articleWhat is dbt? The tool that turned data transformation into real engineering
If you follow the data world, you have run into the acronym dbt. It shows up in job posts and in nearly every modern analytics project. But what does dbt actually do — and why did it become the standard?
Read articleHow to build a data team (without hiring a whole department)
Your company has grown and now "needs a data team" — but building one from scratch feels expensive and risky. The good news: you can start with a business question and a clear roadmap, not a full payroll.
Read articleAnalytics Engineering: the missing link between your data and your decisions
Your company has been collecting data for years, yet still decides on gut feeling? The problem is rarely a lack of data — it is the absence of a layer that turns it into something trustworthy. That is where analytics engineering comes in.
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