Artificial intelligence is seen as a key technology for the future. But it can only reach its full potential if the data is correct. Clean and structured data is becoming more important. Before using AI tools effectively, companies must practice data excellence in their daily work. This is the foundation for successful AI use.
Almost nothing drives today’s business world like artificial intelligence. Companies around the globe apply AI to improve their business models and streamline processes. Banks and insurance firms use AI algorithms to assess risk and detect fraud. E-commerce leaders such as Amazon and Zalando create highly personalized shopping experiences with AI. They also use AI to forecast demand and optimize logistics.
Even though AI seems magical, we must remember how it works. AI gives results based on probability and complex statistics. To get useful results, it needs one main thing: a lot of good, clean, and useful data. That’s why data excellence is now very important for companies. Without data, AI cannot work.
But just having lots of data is not enough. The quality of the data must be high. Only high-quality data can bring value. If the data is messy or wrong, the results will also be bad. This is true even in the age of Big Data. If you put in bad data, you get bad results.
Many companies have a difficult relationship with data. At the start of every data journey, people imagine a powerful use for that data—like a pot of gold at the end of the AI rainbow. Usually, employees take the first steps. They enter, clean, or manage the data in different ways.
But this is where problems start. Everyone agrees that AI projects are important. They know a clean and organized data pool is necessary. However, when they need to act, their behavior does not match their beliefs.
Data Competency Checklist
Map your systems and processes in detail. Gain as precise an overview as possible of your organization’s system and process landscape: Which data travels along which paths, from start to finish of every core process chain?
Demonstrate data usage and value. Show how data is used across the company and the concrete benefits it delivers— the more specific, the better!
Treat data management as a marathon, not a sprint. Embedding good data practices takes time and repetition—regular reinforcement is key.
Make data literacy a core part of employee training. Establish data competence as a fundamental element of your internal learning and development programs.
Encourage correct data collection and processing. Support both quantitative and qualitative best practices—ensure that the wrong methods are never rewarded.
Break down silos—get every data-focused team on board. Involve all data-centric departments to foster collaboration and prevent siloed thinking.
Some people don’t collect data at all. Others store it in the wrong way. Duplicate entries appear. Departments don’t connect their data with others. These issues block progress. Experts call this the "Attitude-Behavior Gap." People think something is important, but they act differently. We see this clearly in environmental protection. The same gap appears again and again in data management.
One cause of the attitude-behavior gap lies in psychological distance people feel in many situations. We define psychological distance as how far we sense an object or event on four dimensions: time, space, social connection, or hypothetical possibility. Temporal distance refers to events in the distant past or the far future.
Spatial distance refers to places that lie far from where we are now. Social distance refers to people who stand outside our own social circle, such as strangers or public figures. Hypothetical distance refers to events that might happen or might not. When people perceive a large psychological distance, they struggle to think in concrete terms.
Their thinking shifts to abstract ideas and loses relevance for actual decisions. In contrast, when we sense psychological closeness, we focus on specific thoughts and real needs. That closeness makes our thinking more concrete and more useful for concrete decision situations.
We all know this situation in everyday life. On New Year’s Day, we decide to start exercising regularly. But by the second week of January, we skip the gym and reach for leftover Christmas chocolates instead.
Data feels abstract, and distractions feel easier
The same thing happens when people deal with data. Most employees see data as something abstract. They also find data analysis hard to understand. So, instead of working on updating or organizing data, people often choose easier tasks. They reply to emails or prepare for the next meeting. These tasks feel more real and show quick results.
The goal of using clean data to do useful analysis feels too far away. It feels unclear and difficult. That’s why employees focus on simpler tasks with fast results. In many cases, employees don’t even use the full data themselves. They only provide small parts of a bigger dataset. The real fun of discovering insights goes to the data scientists. These experts usually earn high salaries and do the final analysis.
It becomes even harder because employees must do the work now, but the benefits come much later. These benefits seem unclear and feel far away. People often don’t see how their own actions connect to the results. So, working with data can quickly feel useless or unimportant.
Many think, "This won’t make a difference anyway." So, they stop being careful. They enter wrong or messy data quickly, and this bad input gets saved deep inside the system. Right now, people often choose the quick and easy way to collect or handle data. But that is usually the wrong way. For example, companies often measure time or quantity to track tasks instead of checking the quality of the results.
Creating a duplicate record is often faster than searching for and updating the correct one. This creates bad systems and poor processes. People might end up using two or even three systems at the same time to do just one task.
False Ownership Claims Lead to Chaotic Structures
Some organizations still work with a silo mentality. Each department wants full control over "their" data, even though all data belongs to the company. When teams collect data with effort or buy it using their own budget, they feel a strong sense of ownership. They believe the data only belongs to them.
This leads to messy and confusing storage systems. Even team members often struggle to find or understand the data. Outsiders find it even harder. As a result, teams don’t share data. They also avoid collaboration, even when it could bring new opportunities.
How Can We Solve This Problem?
To fix this issue, teams must understand the real value of data. One good way is to show clear benefits of using data in real projects. For example, showcase successful AI projects that used company data. These examples help people see how useful data really is.
Show how specific data helped create results. Explain why certain data formats made sense. Make it clear that everyone’s data contributions matter. Even if teams haven’t started their own data projects yet, they can learn from others. Podcasts like “Datenaffaire” from Spotify share inspiring examples, even from unrelated fields. These examples help build understanding and interest.
Regular data meetings, called Data Jour Fixes, can also help. In these meetings, teams share success stories. This builds a strong data culture. It also shows why good data management is important. Once this mindset is in place, it becomes easier to start big and ambitious AI projects.
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