Data Insights — Data Maturity & Strategy
Control your data without bureaucracy
A pragmatic governance framework for growing organisations
Monday morning, management meeting. The sales lead opens his spreadsheet, the financial controller opens hers. Revenue according to Sales: €1.1 million. Revenue according to Finance: €980,000. The next forty minutes aren't about strategy — they're about who has the right numbers. This is what happens when nobody has defined what "revenue" actually means. And it costs more than meeting time.
What does it cost when nobody owns the data?
The problem is recognisable in virtually every SME that uses more than one system. The symptoms vary, but the pattern is always the same.
The "Data Cowboy." Every department cobbles together its own reports. The sales lead has his spreadsheet, the operations manager her own pivot table, the financial controller his accounting system. Well-intentioned, but detrimental to consistency. What Sales calls "revenue" differs from Finance's definition. Nobody is lying — they're measuring different things.
The definition war. The management meeting isn't about strategy but about the numbers. "Does this include returns?" "Does this order count at the time of signing or delivery?" Every week, the same debate. The cost isn't the debate itself — it's the decisions that aren't being made while the debate continues.
The AI risk. Colleagues enter business-sensitive data into public AI tools without realising this could constitute a data breach. Supplier terms in ChatGPT. Customer lists in a free analytics tool. Without rules, there's no boundary — and no visibility into what's leaving the organisation.
Each of these problems is costly on its own. Together, they form a pattern that scales with the organisation: more people, more systems, greater chaos. The solution isn't more technology. The solution is agreements.
How do you set up data governance without bureaucracy?
Everything you read online about data governance is written for organisations with 500+ employees, a Chief Data Officer and a twenty-person IT department. That's not the reality for SMEs. Governance needs to fit alongside the daily work — not above it.
Agile data governance is an iterative approach where data management rules are introduced step by step, starting with the data that carries the most risk or the most value. Unlike traditional governance — which tries to regulate everything at once — the agile approach installs guardrails where the risk or value is greatest. You don't lock everything down. You start where it hurts.
The traffic analogy is apt. Because we've agreed that red means "stop" and green means "go," we can drive past each other at a hundred kilometres per hour without colliding. Without those agreements, everyone grinds to a halt. That's exactly what happens in many organisations: not too little data, but too few agreements about that data.
Den Otter Solutions uses a three-pillar framework designed specifically for SMEs that don't have a dedicated data department.
Pillar 1 — People & Roles: from function to ownership. You don't need a full-time Chief Data Officer. What you need is clearly assigned ownership within your existing teams. Divide responsibility into two levels. The Data Owner is strategic — typically a manager such as the Sales Manager or Finance Director. This person is ultimately responsible for the quality and use of data within their domain. The Data Steward is tactical — the subject-matter expert who monitors daily quality, such as the Sales Controller or Senior Marketer. They ensure new fields are used correctly and resolve errors at the source. Governance only works when stewards have the mandate to say "no" to contaminated data entry.
Pillar 2 — Definitions: the Single Source of Truth. The core of governance revolves around defining the business language. What exactly is "gross margin"? Does it include discounts? Exclude returns? Does an order count at the time of signing or delivery? Instead of putting this in a document that nobody reads, we embed these definitions in the technology: the semantic model in Power BI. Define your KPIs and calculation rules once in the platform. Mark verified datasets as "Certified." Anyone building a new report must use this dataset. This way, the definition of margin is always identical — regardless of who creates the report. Read more about this principle in the article on the Single Source of Truth.
Pillar 3 — Processes: integrate into your rituals. Don't add tedious governance meetings to the agenda. Integrate checks into existing rituals. The "Data MOT" is a monthly moment in the management meeting — discussing not only the numbers but also their quality. Are there notable deviations or missing values? And with every process change in the ERP, the standard question: "What does this mean for our dashboard?" This prevents a technical update from blinding your insights.
Why is governance mandatory for SMEs in 2026?
The thought might be: "Things are going fine as they are." That can change quickly. The market is increasingly demanding transparency, and the rules are changing faster than most SMEs realise.
Supply chain integration and CSRD. Major clients will inevitably ask SME suppliers for data on CO2 emissions, raw material origins or labour conditions in the supply chain. Those who cannot provide that data in a structured and validatable way risk their position in the chain. CSRD reporting is no longer optional — it is the "licence to operate" for the coming years.
AI Readiness. Everyone wants to "talk to their data" through tools like Microsoft Copilot. But AI is an amplifier — it amplifies what you feed it. Imagine Copilot summarising your CRM. But 30% of your customer records haven't been updated. The summary sounds convincing. The conclusions are wrong. And your team acts on them. Governance is the foundation under your AI strategy. Without clean, unambiguous data, AI isn't worthless — it's dangerous, because it produces convincingly wrong answers at speed.
The organisations that take their data maturity seriously now — however small the first step — are building the foundation for everything that comes in the next five years.
Four steps to minimum viable governance
Don't wait for a grand master plan. Governance works best when you start with one concrete pain point and build from there.
These four steps don't take months and don't require a budget. They take two weeks of attention and the willingness to document agreements. The result is a foundation you can build on — from reliable reports to CSRD compliance to AI readiness.
Governance is not a goal in itself — it is the prerequisite for everything that follows. Reliable dashboards, AI you can trust, CSRD data you can deliver, meetings that focus on strategy instead of number debates.
The organisations that make three agreements now — who owns the data, what our definitions mean, who can see what — save themselves months of repair work later. Not because governance is exciting. But because everything you build without governance is built on quicksand.
The Data Maturity Scan by Den Otter Solutions assesses governance as one of the seven dimensions of the Coleman Data Maturity Model. In 28 questions, it becomes clear whether the data foundation is solid enough for the next step — or whether rules need to come first.
Last updated: April 2026
