Collecting, structuring, using data: The practical guide for digitization in SMEs

Why data readiness is driving digitization in SMEs

Technologies are rarely the real obstacle to digitization in SMEs. Most tools, platforms and systems already exist and are affordable even for medium-sized companies. The key difference lies in the quality and availability of your data. The challenge is real: While 82 percent of SMEs regard data analysis as strategically important, 75 percent do not have a systematic data strategy.

What good data makes possible

Clean, structured and accessible data opens up new opportunities for your company.

New systems work with complete and accurate information. Automations deliver reliable results. Evaluations lead to well-founded decisions. AI tools can reach their full potential because they learn from a solid foundation.

The investment in good data quality pays off in several ways: in faster implementation, better results and sustainable success. From our experience in exchanging ideas with decision makers, we know that data readiness is the lever that turns nice-to-have digital initiatives into a real competitive advantage. The Federal Ministry of Economics is also taking action here and has been focusing its network of medium-sized digital centers on the availability and processing of high-quality data since 2024.

Anyone who invests properly here right from the start benefits in the long term: Implementation runs more smoothly, systems work more efficiently, and the basis supports all future initiatives. The good news: Data readiness is structured work that can be tackled systematically.

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From inventory to strategy: The five steps to data maturity

To create a solid database, you need a structured approach. The following five steps form the basis for successful digital transformation and show how you can systematically build up your data maturity.

Step 1: Identify and evaluate data sources

Before data can be structured or used, it must be clear: What data is actually available? Where are they located? And how relevant are they to your business goals?

Discover the potential of data

Most companies have significantly more valuable data sources than they realize. ERP systems, CRM software, Excel spreadsheets, email inboxes, production systems, inventory management, time recording, website analytics, social media accounts — the list is long and the potential is huge.

A systematic inventory is the start. A list of all systems and tools in which data is created, stored or processed provides an overview. Important: Involve teams from the specialist departments. Employees often know of data sources that are not even known to the IT department.

Decisive questions here:

• Where is transaction data generated?
• Where is customer data?
• Where is process data collected?
• Where is product data?
• Where can financial data be found?
• Where is information collected from communication?

Systematically assess relevance

Not all data is equally important for your digitization. Some are business-critical, others are nice-to-have, and others are completely irrelevant to strategic goals.

An evaluation of each identified data source according to three criteria provides clarity:

Business relevance: How important is this data for core processes, decisions and customer relationships? Data directly related to sales, customer satisfaction, or operational efficiency is a top priority.

Timeliness and completeness: Is the data maintained regularly? Are they complete? Data sources with sporadic maintenance or major gaps need fundamental improvements.

Accessibility: How much effort is required for integration and use? Is data stored in modern systems with interfaces or in outdated legacy systems? Are they digital or partly still on paper?

This review gives you an initial overview: Where are your most valuable data sets? Where is the biggest backlog? Where should you start first? By exchanging ideas with companies, you will find valuable answers to these questions.

Data architecture is becoming a central management task. At the data:unplugged festival, you will find exactly this group of people responsible — Decision-makers from other medium-sized companies, as well as tech and legal experts who speak openly about how they implement, secure and scale data sovereignty, cloud sovereignty and AI governance in their companies.

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On data:unplugged, you will find a platform to boost this transfer of knowledge.

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Step 2: Evaluate and improve data quality

The quality of your data determines how successful processes based on it work. High data quality is the key to enabling digitization projects to develop their full potential.

The starting position is definitely positive: According to the Federal Network Agency, 62 percent of German SMEs have at least a basic level of digital intensity. It is now a matter of further expanding this basis through high data quality.

The six dimensions of data quality

completeness: Have all required fields been filled out? Is there important information available? A complete customer data set with email address and telephone number enables effective communication in marketing and sales.

Accuracy: Are addresses, names, numbers correct?

Consistency: Is the same information the same in different systems? When your CRM and ERP share the same customer address, processes run smoothly and efficiently.

Timeliness: Some data (such as inventory levels) require real-time, while others require daily updates. Current data enables well-founded decisions at the right moment.

Uniqueness: Every customer once in the system with a correct name — this makes data a reliable basis for digitization in medium-sized companies.

Formato: Structured data fields in standardized, machine-readable formats enable effective use for AI applications and automation.

Specific measures to improve quality

For each important type of data, you should determine: Which fields are mandatory? Which formats are allowed? What are the rules for submissions?

Validation during recording ensures quality right from the start. Quality assurance right at the time of entry saves time and effort later on. Mandatory fields, format checks, drop-down menus instead of free text fields — all of this creates clean data right from the start.

Regular data audits reveal potential for optimization. At least on a quarterly basis, you should check on a random basis: What is the data quality? Where are there opportunities for improvement? Which systems or processes are running particularly well?

Automated cleansing helps with large amounts of data. Tools can identify duplicates, standardize formatting, or correct inconsistencies. Important: Automation complements work on the root cause and makes processes more efficient.

That is exactly what we hear again and again from experts and decision makers from medium-sized companies who share their experiences in our data:unplugged podcast.

That is exactly what we hear time and time again from experts and decision makers from medium-sized companies who work in our data:unplugged podcast share their experiences.

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Step 3: Structure and integrate data

Once you've identified all data sources and improved the quality, the next important step comes: How do you bring different data sources together? How do you make them comparable and maximally usable?

Master data management as a foundation

Master data management means: There is a central, reliable source for the most important master data in your company. A customer, a product, a supplier — each with a unique ID that is the same in all systems.

In practice, this opens up new opportunities: Clear assignments, unique references and comprehensive evaluations are possible. Master data management creates a single source of truth. All systems access the same master data or synchronize with the central source.

Define standardized data formats

You should define standards for each type of data: How are dates formatted? In which unit are quantities recorded? What categories are there for product groups? How are customer statuses documented?

These standards provide clarity and enable seamless collaboration between systems. When all systems use the same formatting, merging becomes easy. Implement the standards consistently: in new systems from the start, in existing systems through migration or mapping.

Create interfaces for integration

Most systems don't have to keep all the data themselves, but they do need to be able to access it when needed. This works via interfaces — APIs, data exports, synchronization mechanisms.

Modern systems usually come with APIs. With older systems, you can rely on file exports. What is important is that automation enables scalable, efficient processes.

A central data warehouse or data lake can help bring together various data sources and make them available for evaluations or AI applications.

Step 4: Regulate access and ensure security

Making data usable also means giving the right people the right access. Data governance creates the necessary structure and security for your company.

Implement role-based access rights

Everyone receives exactly the data they need for their work. This is true for data protection reasons, but also for practical reasons: Focused access rights enable efficient work and increase security. Role-based access rights mean: You define roles (e.g. sales representative, buyer, controller) and assign specific rights to each role. New employees are then given the appropriate permissions for their role.

This makes administration easier, more transparent and more secure. And it makes audits easier: You can always see who has which rights and why.

Data protection and compliance

The GDPR provides a reasonable framework for handling personal data. For digitization in SMEs, this means in concrete terms:

Data minimization as a principle: Only collect the data that is really needed. More data means more responsibility, more storage costs, and higher risks.

Note earmarking: Use data only for the purposes for which it was collected. Customer data from sales must not simply be used for marketing campaigns if the customer has not agreed to this.

Define deletion concepts: Determine how long which data must be stored and when it is deleted. Lean databases are more efficient and secure.

Implement technical and organizational measures: Encryption, backups, access protocols, security training — the basic package of security measures must be in place.

Establish governance

Clear structures ensure reliability:

• Identify responsibilities: Who is responsible for which data? Who can grant new access rights? Who monitors compliance with standards?
• Documenting processes: How are new employees accepted into the system? How are access rights adjusted when changing roles? How is offboarding going?
• Carry out regular reviews: Check at least once a year: Does everyone only have the rights they need? Are there any orphaned accounts? Are safety standards being met?

Step 5: Build and establish a data culture

The best data infrastructure is useless if people in your company don't understand why data quality is important, how they should deal with it, and the value of good data. Establishing a data culture is the last — and often underestimated — step towards successful digitization in SMEs.

What is data culture?

Data culture means that data-based decisions are becoming the norm. That employees see data quality as their responsibility. That questions are asked: On what basis is the decision made? What data is available on this? How valid is this information?

It also means: Openness to what data shows — even if it contradicts your own intuition. Willingness to learn from data instead of ignoring it. Understanding that good data requires time and maintenance.

How can you build a data culture?

Leadership with a good example: If decision makers make gut decisions instead of working based on data, so will the team. When people ask for figures, remind them of data quality and make decisions based on facts, this sends a clear signal.

Make the value of data visible: Specific examples show where better data has led to better results. Where a decision based on data was more successful than a decision based on gut feeling. These success stories motivate.

Train team: Not only in the technical use of systems, but also in understanding why data quality is important, how data is correctly interpreted and which errors must be avoided. At data:unplugged, this transfer of knowledge is specifically promoted - in exchange with other decision makers from SMEs and for the entire business team.

Create transparency: Make data accessible — within the framework of defined access rights. Anyone who sees how their own work is reflected in figures often develops a greater understanding of correlations.

Establish feedback loops: When data is used to improve processes or make decisions, the results should be communicated back to the teams that collect that data. This is how they see that their work is making a difference.

Appreciate good data management: Not financially, but through recognition. Teams or employees who pay particular attention to data quality should be visibly appreciated.

Overcome typical challenges

Change habits: New processes for data collection or maintenance feel unusual at first. Patience, clear explanations and the presentation of concrete advantages help here. When teams understand how they benefit from better data, adoption increases.

Trust through transparency: Some are initially uncertain how more data is being used. It is important to communicate: It is about better decision-making bases and process optimization. Transparency creates trust and shows added value for everyone.

Communicate clearly: When data topics are discussed too technically, some lose touch. Communication should remain practical and understandable. Specific use cases and tangible examples make connections clear.

Efficiency through good systems: In intensive phases, every move must be right. It is therefore important that data management is simple and intuitive — through user-friendly systems, smart automation and clear processes. Well-designed systems save time instead of costing it.

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Self-assessment: Where do you stand in terms of data readiness?

An honest evaluation helps to assess your current level of data maturity. Central questions on five areas provide guidance:

• For data sources: Complete lists of all systems, knowledge of business-critical sources and documented responsibilities
• Data quality: Defined standards, input validations, regular audits and knowledge of error rates
• Structure and integration: Master data management, ability to combine various data sources, documented standards and implemented interfaces
• Access and security: Role-based rights, concepts for sensitive data, GDPR compliance, and processes for onboarding and offboarding
• Data culture: Decisions based on data, employees understand the value of good data quality, training takes place, good data management is valued

The more of these points you can answer positively, the better your data readiness is.

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Conclusion: The foundation for successful digital transformation

Collecting, structuring and using data is not a one-off project, but a continuous process and central management task. Your digitization is built on this foundation — and the more stable it is, the more successful all initiatives based on it will be. A systematic approach in five steps creates a sustainable database. Data quality is more important than quantity of data. Technology is only half the battle — establishing a data culture is at least as important to your success.

Working on the database is an investment that pays off several times over: in better decisions, more efficient processes, happier customers and ultimately in greater competitiveness. From our experience in exchanging ideas with decision makers, we know that the shift towards data-based decisions is central to the future of SMEs and the successful application of new technologies such as artificial intelligence.

You can find out how other SMEs have actually taken this path on the data:unplugged festival 2026 on March 26 & 27 in Münster. Here, companies from the areas of e-commerce, industry, trade, production and logistics share their self-implemented use cases on data readiness, data culture and data sovereignty: from finance to marketing, from IT to legal. At SME Days 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 AI technology, but to actively shape them.

Data readiness 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 now, one of your core team!