Stop losing money on data chaos

This is not filler marketing copy. This is an explanation of why data chaos hits profit harder than a failed ad campaign — and why a proper data model fundamentally changes how a business makes money.

Most companies don’t have a data model. They just have a pile of tables.

Most companies are convinced their analytics works. They have BI tools, BigQuery or Postgres, dashboards, data marts, and reports. On paper — everything exists.

But ask one simple question:

Suddenly everything goes quiet. Marketing shows one number. Finance shows another. Product shows a third. The CEO trusts none of them and makes decisions by gut feeling.

The reason is simple and painful: the company has no data model. Only a historical collection of tables, scripts, and queries. And this costs real money every single month.

Data chaos is the most expensive business problem — because it disguises itself as “this is normal”

Companies adapt to the pain. They get used to:

• dashboards that never match, but “that’s expected”;
• SQL logic living in two analysts’ heads and nowhere else;
• “it worked yesterday, broke today — we’ll hotfix it”;
• the same metric showing different numbers in different reports;
• growing BigQuery / Postgres bills without profit clarity;
• adding a new country or source turning into a week of firefighting.

Everyone starts believing this is just how data-heavy businesses work. It’s not. This is not normal. This is the direct result of missing data modeling.

Data Modeling is the foundation that determines whether a business is controllable

When we say “data modeling”, we’re not talking about another layer between DWH and BI. We’re talking about the foundation on which all analytics and decisions are built.

A proper data model gives a company:

• a clear structure of entities: users, orders, deposits, sessions, campaigns, products;
• explicit relationships between product, marketing, CRM, and finance;
• a single metric layer that doesn’t change from dashboard to dashboard;
• data marts that stay fast and stable as volumes grow;
• an architecture that scales 3–10× without rewriting everything.

This turns data from chaos into a system where every KPI has a strict, unchanging definition and can be reliably reproduced.

Why this directly impacts profit

Without a data model, you cannot honestly answer fundamental business questions:

• which channels actually make money — and which burn it;
• which marketing combinations are profitable versus just familiar;
• whether specific user segments ever become profitable;
• when a customer truly breaks even — if ever;
• how LTV differs across segments and why;
• which products pull the business forward and which drag it down.

The difference between a company with a data model and one without it is like the difference between smartphone navigation and a map drawn on a napkin. There is information on the napkin — but you cannot manage movement with it.

What changes once a data model exists

One critical shift happens:

When a CEO sees profit:

• by channel,
• by segment,
• by funnel combinations,
• by product,
• by geography,

they can cut loss-makers, double down on what works, and justify decisions with numbers instead of intuition. This is the leverage that separates fast-growing companies from those stuck in place.

Why we work with teams that want results — not pretty presentations

We don’t do cosmetic fixes. We don’t build “just another dashboard”. We don’t repaint reports.

We build architecture that:

• explains your business in data form;
• survives growth in volume and complexity;
• provides clarity on profit — not just clicks and impressions.

What this means in practice

• Profit transparency: you clearly see where money is made and lost — based on facts.
• Stable and fast analytics: marts stop breaking weekly, BI stays responsive.
• Scalable growth: new countries, products, and sources connect without chaos.

Who critically needs proper data modeling

Data modeling is essential wherever data volume is high and the cost of mistakes is real:

• eCommerce and D2C with many SKUs, channels, and segments;
• iGaming, betting, casino businesses with massive event streams;
• SaaS and subscription products where LTV and cohorts matter;
• fintech and financial services;
• lead generation and performance-driven businesses;
• marketplaces and aggregators.

What you get in outcomes — simplified to numbers

• −40% data costs (DWH, queries, maintenance);
• ×3–5 faster BI and reporting;
• −70% metric errors and contradictions;
• +100% trust between marketing, product, and finance;
• profit growth driven by fact-based decisions.

Ready to see where your business is losing money?

If you already have data — you already have profit growth points. The only question is whether you can see them.

Describe your situation in two or three paragraphs: what business you run, what data sources you have, how analytics works today, and what frustrates you most. We’ll come back with:

• an assessment of whether a dedicated data modeling project is needed;
• a plan of quick wins and larger structural changes;
• clear timelines, budget ranges, and risks.