Clean data, no decisions

A scene from several sales conversations: the CFO asks where the company is losing money. The CMO responds with campaign performance data — reach, engagement, attribution. The CFO nods politely and cuts the marketing budget by twelve percent in the next quarter anyway. Not because he didn't understand the data — but because it isn't in his language. Marketing speaks in campaigns, the company thinks in accounting processes. Whoever doesn't solve this problem ends up with clean data that still doesn't count in the quarterly review.

What data sovereignty means in the marketing tech debate

Data sovereignty has become a standard vocabulary in the marketing tech debate. Platform vendors, consultants, analysts — everyone talks about it. What is usually meant: the ability to keep one's marketing data structured, comparable, and reliable. Clean naming conventions. Consistent taxonomy. Planned-versus-actuals matching at line-item level.

This is the technical half of sovereignty. It is necessary — anyone who cannot structure their campaign data has it, but cannot turn it into statements. An Abacum study 2025 documents the scale: 78 % of finance decision-makers admit that they base planning decisions on assumptions rather than data. The main cause they cite: fragmented data landscapes, regional silos, lack of comparability between markets.

Whoever solves this problem has built themselves a tool. The data becomes readable.

And it still often changes nothing about the decisions.

The second half that has no established name

The BARC study 2024 makes the phenomenon empirically tangible: 58 % of senior business professionals surveyed report that their companies base the majority of business decisions on gut feeling or experience — not on data. An Alation study from 2020 with three hundred data leaders in the US, UK, Germany, Sweden, Norway, and Denmark reaches the same finding: two thirds of CEOs rely on gut feeling. 91 % of companies consider data-driven decisions critical — 57 % actually practice them.

These numbers don't show decision-makers who lack data. They show decision-makers who have data and decide against it anyway. The gap is broad, persistent, and almost never named in the marketing tech debate.

It has two causes that are usually treated as one.

The first cause is the technical one — the data isn't clean enough to become a basis for decisions. That's the half everyone works on.

The second cause is different: the data is clean, but it isn't in the language in which the company makes its decisions. A CFO who cuts a marketing budget doesn't ignore the marketing data out of malice or gut feeling. He makes his decision in his professional language — margin contributions, cashflow effects, capital allocation logic. Marketing data in campaign language reaches him as background noise, not as a basis for decisions. Not because he doesn't understand it. But because it isn't formatted in his logic.

What happens when only one half is in place

Technical data quality without accounting anchoring is dashboard theatre. The reporting runs, the data is clean, the visualizations are clear — and nothing changes about the actual decision reality. Marketing produces ever more refined reports that the CFO politely acknowledges, because the reports don't enter his decision logic.

Accounting anchoring without technical data quality is instinct with conviction. The CFO knows he can override the marketing data because marketing itself cannot describe what the data shows. This is the reality in mid-market companies that never imposed data-driven discipline on themselves — and it works surprisingly long, until a market shock or growth standstill makes the fragility visible.

Only both halves together produce what marketing operations as a function should be: a discipline in which marketing spend is managed in the language in which the company makes its decisions. Marketing isn't taken seriously because it doesn't speak the language in which the company thinks — and that is a statement about marketing as a function, not about a single tool. It is therefore rarely named, but it doesn't change through being unspoken.

Three questions to assess your own setup

If the diagnosis above describes your setup, three questions are pragmatic enough to create clarity before you think about tools or consultants.

Question one. Can your marketing operations team compare the top-3 campaigns of the past twelve months by the same metrics across all markets? If the answer is "in principle yes, but each market calculates somewhat differently", the technical half is not in place. Cleanliness of the data isn't enough — it must exist in the same form across countries, otherwise comparability is theoretical.

Question two. When your CFO cut a marketing line in the last budget round: was that decision justified with marketing data or with industry experience, margin logic, or cashflow pressure? If not with marketing data, accounting anchoring is missing. The marketing data exists — but it isn't formatted in the logic in which the decision was actually made.

Question three. Is there a person or function in your setup whose explicit task is to translate marketing spend into accounting language — that is, not just thinking in campaign performance but in margin contributions, cashflow effects, and capital allocation? If not, the mechanism that would bring both halves together is missing. This is the most common of the three gaps, because it usually doesn't belong to the job description of any existing role.

What the resolution is not

Anyone who takes the diagnosis seriously quickly arrives at the wrong solution: another tool. A more integrated dashboard. A better attribution platform. An AI system that automatically links marketing data with financial data.

These solutions fail systematically because they address the technical half again — on the assumption that the bridge to business logic will then emerge automatically. It does not. The bridge between campaign language and accounting language is human translation work, not a tool feature. It requires someone who can describe marketing spend in business logic and vice versa.

What Mykorisa does at this gap

Mykorisa works at the intersection between marketing operations and accounting logic. Not as a tool vendor addressing a technical half, and not as a strategy consultant talking about brand positioning — but as a consulting function that embeds marketing spend into the business logic in which CFOs and management actually decide.

The work behind it is description work. Which marketing processes run in which form, which spend hangs on which activities, which activities are reflected in accounting processes, which are not. From this emerges a bridge that makes marketing performance readable in CFO language — and thereby a basis for actual decisions.

From this description work, Kiritsu was built — the tool that operationalizes the anchoring in multi-country setups, where the complexity of translating between campaign language and accounting language becomes exponential. Kiritsu is not a replacement for the description work. It is what becomes possible after the description.

If you have the impression from the three questions above that one or both halves are not in place in your setup, a thirty-minute conversation is the pragmatic next step.