Using Data Strategically in SMEs

Many companies today have more data than ever before – and use less than 5  %of it

At first glance, this sounds like a statistic from a consulting pitch designed to impress potential clients. However, it is far more than just a number and reflects everyday reality that we regularly experience with many companies, law firms, craft businesses, and service providers.

What comes with this is the way customers handle their data – and it almost has to be put this way:in most cases, data is treated rather negligently, barely prepared, and only rarely analyzed with precision. In short: a great deal of potential is being wasted.

It is striking that customer data is often managed across three different systems and is therefore inconsistent. Analyses are still mostly carried out manually in Excel. Decisions are made based on gut feeling rather than on fact-based data analysis. Somewhere in the background, there may even be an AI tool running, but no one really knows which data it uses or what exactly it does with it.

The problem is not the lack of technology. The problem is the lack of clarity. In fact, companies can use their data strategically – without major transformations, without costly experiments, and without unnecessary dependencies.

What “using data strategically” really means

The term “strategic use of data” may sound unwieldy at first. In practice, however, it turns out to be far more tangible and transparent than it initially appears – and therefore all the more effective.

Using data strategically by no means implies building an entire data science department or introducing a data lake infrastructure. No. It means asking the right questions:

• hich decisions do we make as a company on a daily basis – in the interest of all stakeholders – and what are they based on?
• Which data do we already own that could improve these decisions?
• Where is this data located – and why are we not simply accessing it?

Companies that can answer these questions have overcome the initial hurdle. Everything else is nothing more than structured implementation.

Our philosophy is very simple: we make existing know-how visible, structure it, and put it to effective use. Only when the available internal resources have been fully leveraged do new tools come into play.

The four most common data mistakes in medium-sized businesses

. Data silos instead of a unified data foundation

A CRM system here, an ERP system from another provider there, Excel spreadsheets used by the sales team for years, and the accounting data held by the tax advisor – at first glance, all of this is simply data with no tangible value.

If this data is not consolidated and remains unstructured, it won’t make anyone any wiser. The first step towards a data strategy for small and medium-sized enterprises is therefore simply to take stock: where is the data located, who owns it, and who has control over which types of data?

2. Poor data quality is a barrier to growth

Data that is out of date, duplicated or inconsistent provides a flawed basis for decision-making. The danger here is that anyone who builds AI or automation on a weak or inadequate data foundation will accumulate errors rather than resolve them.

The fact is: data quality is not just a technical detail – it is one of the most important prerequisites for every subsequent step.

3. Technology without a process

A tool does not solve a data problem. However, it does facilitate a clearly defined process. Anyone wishing to make the most of their data first needs to be clear about who needs what data, when, for what purpose and in what format.

Only then does the use of technology make sense.

4. Viewing data protection as an obstacle – rather than as a framework

Many companies still struggle with GDPR-compliant data usage and therefore prefer to do nothing. That is the wrong approach.

Organising data in a structured, transparent and GDPR-compliant manner not only ensures security but also builds trust – among customers, employees and partners.

What a data strategy actually looks like in an SME

At first glance, data strategy might seem like a topic that is only relevant to large companies. Yet it is precisely in small and medium-sized enterprises – with flat hierarchies, short decision-making processes and manageable systems – that a clear data strategy can be implemented quickly and pragmatically.

The following five steps have proven effective in practice:

1. Taking stock: What data is available, and where? Which data is reliable? Which data is actually being used?
2. Prioritisation: Which decisions do we make most often – and where is the greatest leverage?
3. Structuring: Breaking down data silos, ensuring data quality and clarifying responsibilities.
4. Pilot phase: A clear use case with a measurable target as a controlled launch.
5. Scaling: Building on successful approaches, understanding why others have failed, and adapting accordingly.

Five steps that sound like common sense – and that’s exactly why they work.

Data as the foundation for AI – not the other way round

AI consulting is one of the most common topics of discussion in initial meetings. At the same time, we repeatedly find that companies want to use AI without having a robust data foundation.

After all, a car with an empty fuel tank or battery won’t go a single metre.

AI relies on reliable, structured data. Without this foundation, the results yield no added value – and lead to frustration among staff who are forced to work with incorrect outputs.

That is why there is a clear order:

• First, clarify the data foundation – quality, availability and responsibilities.
• Next, define the use case – in concrete, measurable and realistic terms.
• Then choose the right technology.

AI is meant to lighten the workload, not create more work. This will only work if the foundations are right.

Greater clarity is the best solution – not ‘even more tools’

The surprising thing is that companies which use their data strategically usually don’t have a bigger budget than their competitors. They simply know which data is truly relevant – and why.

Let’s be honest: this isn’t a six-month project. It’s a structured process that begins with an honest assessment of the current situation and ends with a noticeable reduction in the workload of day-to-day business operations.