Data maintenance comes before AI: Why good master data quality is the basis of every AI strategy

The foundations are crumbling: Why expensive AI experts often fail

Many companies hire highly qualified AI and digitization experts — and then realize that the foundation on which they should work is not yet stable enough. The expectation is rapid success through innovative technology. The reality is often different.

The problem is rarely a lack of know-how or a lack of tools. What is often missing are people who are willing to do the basic work. After all, data maintenance is also hard work — and before you can automate it, data must first be viewed and understood. If you find the right people, you create the basis for successful collaboration: without technological gimmicks, with a clear focus on clean master data.

Master data quality: The underestimated success criterion

The vision for the future: Master data quality becomes invisible. Small software services clean up the background, automatically correct incorrect entries — and users do not notice when poor data quality has entered the system.

Until then, the reality remains: Master data is the basis of all digital processes. Product data, customer data, supplier details — if this information is incorrect, incomplete, or inconsistent, the problems run across all systems. The online shop shows incorrect prices. The warehouse management does not know items. CRM provides useless evaluations for decision-making.

Master data management is becoming a decisive competitive factor, particularly in retail, where product ranges include thousands of items and data sets from various sources converge. Whoever takes a structured approach here creates the basis for automation, better decisions and, ultimately, for the successful use of AI applications.

Clarity over complexity: The right approach

What distinguishes successful digitization projects from failed ones? Often it is not the technology but the attitude. Clarity beats complexity, action is more important than talking, and structured work entails more than endless concept phases.

These principles are reflected in successful projects in medium-sized companies. Companies that understand that their own data must be structured, cleaned and standardized before using AI systems achieve sustainable success.

In concrete terms, this means that companies must identify their data sources, define standards, clarify responsibilities and set up processes. It is then followed by consistent implementation — without abbreviations, but with systematic maintenance of the database.

From Excel chaos to scalable solution

Excel isn't the problem. Excel is already the only solution for complex master data management. Many companies are struggling with hundreds of Excel spreadsheets that serve as data sources. Each department maintains its own version. No one knows exactly which is current. Should errors occur, the time-consuming search for the cause begins.

The way out of this dilemma is through structured systems and automation. When repetitive tasks in data maintenance are carried out by software, employees have more time for value-adding activities. AI can help — but only if the data quality is right.

The development of the terms is interesting: What is now referred to as AI or machine learning was called data mining years ago, then big data, then data science. The technologies continue to develop, but the basic principle remains: Without clean master data, none of these approaches work — not even modern AI applications.

We will discuss how medium-sized companies can actually make the leap from Excel chaos to structured data processes in the podcast episode detailed — including specific success stories and typical stumbling blocks.

Focus as a success factor: Why specialization counts

One insight runs through the entire conversation: Focus pays off. It is not about serving all industries at the same time or trying to solve every problem. Instead, it makes more sense to choose a clear specialization and get really good at it.

The data factory has deliberately focused on retail. The challenges in this industry are specific enough to build real expertise, but big enough to enable scalable growth. This focus helps with product development, sales and building industry expertise.

The same applies to medium-sized companies: It is an advantage to achieve data readiness in one area first, as launching half-hearted digitization initiatives everywhere at the same time. Prioritization creates clarity — and clarity is the basis for success.

Technical depth creates trust

Another success factor from the discussion: The combination of technical expertise and sales strength. When sales managers have a deep understanding of their own technology and the topic of data, other discussions arise with customers and suppliers.

If you ask interviewees who talk about AI a bit deeper, you will quickly notice whether there is real understanding. This technical depth creates trust and enables substantive discussions instead of superficial keyword discussions. It is not about shining with technical terms — but about being able to answer critical questions in a well-founded manner.

The data:unplugged 2026 festival offers an exchange with decision makers who combine technical depth with strategic thinking. At the SME Stage, they share their experiences from master data management to AI strategy — the exchange on equal footing between C-level, heads of from a wide range of fields and data scientists makes the festival a unique format for SMEs.

Product Market Fit: The Road to Scale

For startups in the data sector, Product Market Fit is the decisive milestone — the point at which the product fits demand, the market, so well that customers come by themselves and want to use the product. Felix vividly describes this moment in the podcast: When demand exceeds supply and the team barely keeps up, this point is reached.

The key to scaling lies in standardization: developing software that can be sold multiple times — with slight onboarding adjustments, but with a standardized core. This balance between standardization and customer-specific flexibility is one of the biggest challenges in the B2B software sector.

The appeal: Start instead of planning

The most important message from the conversation is pragmatic: create clarity, deal with the topics in detail — and then start implementing them. Don't plan endlessly, build PowerPoints or maintain data catalogs, but actually get started.

This approach is typical of successful digitization in SMEs. It's not about theoretical concepts that disappear into a drawer, or about tools that no one uses. Instead, the focus is on concrete improvements that are noticeable in everyday working life.

Data maintenance comes before AI. Anyone who understands this and consistently implements it creates the basis for all other digital initiatives - from data analysis to a comprehensive AI strategy (here CTA to analysis article).

Conclusion: The foundation is decisive

Master data quality is not the most prominent topic in digitization — but it is one of the most important. Because without clean data, there is no functioning automation, without structured processes there is no successful AI, and without consistent data maintenance, there is no scalable digital transformation.

The good news: Work on the foundation can be planned. It requires a systematic approach and no complex technologies. Anyone who creates clarity, defines standards and sets up processes can then start implementing them. Anyone who now deals with data readiness and master data management creates the basis for all future innovations.

You can find out how other medium-sized companies go from a data basis to successful AI implementation on the data:unplugged 2026 festival on March 26 & 27 in Münster. At the SME Stage and four other stages, decision makers share their specific experiences, from master data management to AI use cases to digital transformation — in a practical and well-founded way.

For effective implementation, it is recommended to involve key people in your company, train them and exchange ideas. Get your ticket now for yourself and your core team!