Data analysis in SMEs: From a mountain of data to better decisions

What is data analysis?

Data analysis means: Existing data is processed in such a way that it can be evaluated in order to identify trends, patterns and relationships. The goal is always the same — make well-founded decisions instead of relying exclusively on intuition.

In SMEs, this involves very specific questions:

• Which products sell particularly well and when?
• Where do production bottlenecks occur?
• Which customers have the highest churn risk?
• Where is there hidden efficiency potential in processes?

It is not necessary to immediately understand complex algorithms or set up a data science team. Modern tools and AI-based solutions make data analysis possible even without previous knowledge.

‍

The four levels of data analysis

Data analysis is not a uniform process, but can be divided into four levels — from simple reviews to complex forecasts.

Descriptive analysis: What happened?

The basis of each evaluation describes what has already happened. The focus is on past data sets, which are summarized: What was the turnover in the last quarter? How many customers have bought? Which product groups went well?

This form of analysis provides an overview and is often the starting point for more in-depth questions. Dashboard tools show key figures at a glance and make developments visible.

Diagnostic analysis: Why did it happen?

Now it's getting more exciting: The analysis goes one step further and asks why. Why did sales in product group X plummet? Which factors have led to a particularly large number of returns?

This is where correlation analyses and comparisons come into play. Various data sources are linked to identify relationships. Perhaps it turns out that returns are particularly high when certain suppliers were involved, or that sales slumps are linked to delayed delivery times.

Predictive analytics: What's going to happen?

Now it's becoming really valuable: Historical data is being used to predict the future. Which customers are likely to migrate? How will sales develop in the next quarter? Where are there a risk of bottlenecks in production?

Many medium-sized companies already see added value in data science and expect to use data with significantly greater added value in the coming years. Predictive analytics is the key to this. The use of machine learning creates precise predictive models.

Prescriptive analysis: What should we do?

This step results in concrete actions. The system not only analyses what is going to happen — it suggests how to react to it. Which marketing measure brings the best ROI? How should resources be distributed to reduce costs?

This form of analysis often combines multiple methods and uses optimization algorithms. It is particularly valuable for complex decisions involving many variables.

‍

How AI is transforming data analytics

Artificial intelligence is fundamentally changing data analysis. What used to take weeks and required specialists, is now done by AI tools in minutes — even for non-technicians.

Automate routine tasks

In the past, data scientists spent the majority of their time preparing data — cleaning, harmonizing, formatting. AI turns this relationship around: It automates processing so that the focus can be on interpretation and decisions.

Modern AI data analysis tools automatically recognize patterns, identify outliers and suggest remediation steps. This saves time and reduces errors.

Pattern recognition in large amounts of data

AI finds connections that remain hidden from the human eye. Cluster analyses automatically group similar customers. Anomaly detection sounds an alarm if there are unusual values. Correlation analyses show which factors are really related.

Many companies use big data analyses for precise sales and demand planning. Almost half successfully analyze machine data and production volumes. This would hardly be possible without AI, because processing such large amounts of data requires powerful technologies.

Natural language processing makes analytics accessible

There is no need to master programming languages or write complex queries. Modern AI tools understand natural language. A question such as “Show me the sales development by product group for the last quarter” immediately provides a visualized answer.

This democratization of data analysis is a decisive factor for SMEs. Not only IT experts, but all employees can work based on data.

‍

Specific deployment scenarios in medium-sized companies

Does the data analysis still sound pretty abstract? Here are practical examples of how medium-sized companies use them in various areas:

Marketing and sales: Customer segmentation identifies the most profitable target groups. Churn prediction shows which customers are at risk of emigration. Campaign Analytics precisely measures the success of measures.

Production and logistics: Predictive maintenance detects machine failures before they happen. Process analyses reveal inefficiencies. Demand forecasts optimize inventory levels.

Personnel and HR: Fluctuation analyses show risk factors for terminations. Recruiting analytics improves the selection of applicants. Employee feedback is systematically evaluated.

Finances and Controlling: Forecasting predicts sales and cost developments. Budget analyses show potential for optimization. Risk assessments are based on data.

The exchange with decision makers from various industries shows that precisely such use cases form the basis for measurable success. Companies share their experiences on the SME stage at the data:unplugged festival — from theory to practice.

‍

An overview of the most important analysis methods

In-depth knowledge of statistics is not a requirement. However, a basic understanding of the most important processes helps to ask better questions and correctly classify results.

Regression analysis explores relationships between factors: How does the number of promotional activities influence sales? This technique is suitable for predicting and identifying causal effects.

The cluster analysis discovers similarity structures: Which customers behave in a similar way? Clustering helps companies better understand their business outcomes and customers.

The outlier detection identifies unusual behavior — from financial fraud to medical diagnoses to production monitoring.

The time series analysis follows developments over time and predicts trends. Perfect for sales planning or budget forecasting.

Sentiment analysis automatically rates customer feedback, social media comments, or product reviews as positive, negative, or neutral.

These techniques form the basis of modern business intelligence and data mining — they help to obtain usable information from raw data.

‍

The first steps: Start with data analysis

Using data analytics doesn't have to be complicated. These steps move companies forward:

Step 1: Define specific questions

The start should not be in technology, but with business issues. What should be found out? What decisions are pending? Where are better bases needed for decision-making?

Good questions are specific: “How can the return rate in product category X be reduced?” instead of “What can be improved?”

Step 2: Check the database

Is the necessary data available to answer the questions? Are they up to date, complete and of high quality? If not, go back to the previous step — build data readiness.

Step 3: Select suitable tools

An enterprise solution isn't immediately required. Many modern tools offer intuitive ways to get started:

• Excel or Google Sheets with integrated AI features are often sufficient for simple analyses and visualizations
• Business intelligence tools such as Power BI or Tableau offer more options with manageable learning costs
• AI-supported platforms such as ChatGPT for data analysis, specialized tools or decision intelligence solutions automate complex tasks

Step 4: Start with Quick Wins

A manageable project to start with is ideal. For example, analyze customer satisfaction from existing surveys or evaluate sales data from a product group. Successes motivate and create acceptance within the team.

Step 5: Build competencies

Training pays off. Teams must understand how to interpret results and avoid mistakes. Experience in exchanging ideas with SMEs shows that investing in data competence is the decisive lever for successful data analysis.

In the master classes at data:unplugged festival Your team of experts will learn how to work based on data step by step — from basics to advanced analysis methods. Exchange with other medium-sized companies at work level is also particularly important to us in order to benefit from our experience.

‍

Avoid the biggest mistakes

Many data analysis projects fail due to avoidable challenges:

Lack of data culture: If management does not set an example that data counts, the team will also not work based on data. Decisions must be based on facts rather than intuition.

Too complex goals: Anyone who starts with predictive analytics and machine learning often takes over. It is better to start with descriptive analyses and gradually increase complexity.

Poor quality: The best analysis is useless when it comes to bad data. “Garbage in, garbage out” also applies in the age of AI.

Lack of interpretation: Numbers alone are useless. Someone must classify them, draw conclusions and derive actions. Analyzing data is not an end in itself.

‍

Conclusion: Analyzing data means making better decisions

Data analysis transforms a carefully built database into real business value. It shows what works and what doesn't, uncovers hidden potential and provides the security of making important decisions on a solid basis. Especially in SMEs, the use of big data analyses and AI opens up new opportunities to increase efficiency and secure competitive advantages.

The shift to a data-driven organization is a major transformation that many companies are only experiencing at the beginning. It is not necessary to become a data expert yourself, but understanding the appropriate questions, methods and tools is crucial to exploit the full potential of the data. With the right knowledge and the right partners, data analysis can be used effectively to drive sustainable innovations and better decisions in the company.

You can find out how other SMEs are successfully moving from data to data-driven decisions on the data:unplugged festival 2026 on March 26 & 27 in Münster. Here, companies from the e-commerce, industrial, retail, manufacturing and logistics sectors share their implemented use cases for data analysis, predictive analytics and AI-based decision-making: from marketing to production to finance. At the Mittelstands-Stage and a further four stages, we are creating space for exchange on well-founded practical examples of data and AI in order not only to understand the benefits of the technology, but to actively shape them.

Data analysis affects all areas of the company: For effective implementation, it is crucial to involve key people in your company, train them and positively prepare them for deployment. data:unplugged stands for a broad and well-founded transfer of knowledge — from which the entire business team benefits. Get a ticket for yourself and your core team now!