Data Culture: Why data creates no value without the human factor

Today, data is one of a company’s most important resources. Yet even a modern data platform, high data quality and comprehensive analytics or AI capabilities do not create business impact on their own. Only when employees understand the available data, trust it and actually use it in their daily decisions and processes can real value be created for the business.

The importance of this factor is demonstrated by a recent Gartner survey: 60 per cent of data and analytics leaders surveyed in March 2026 cited cultural resistance as the main reason for the failure of data governance initiatives. By contrast, only 21 per cent cited a lack of funding as the biggest obstacle. Technology and budgets alone are therefore not enough if data responsibility and data-driven working are not embedded within the organisation.

And that is precisely the goal of a genuine data culture. A strong data culture emerges when working with data is not solely the responsibility of IT, data teams or individual specialists, but becomes a natural part of everyday work across the entire organisation. With the increasing use of generative and agentic AI, this factor is becoming even more important. After all, AI needs more than reliable data. Companies need people who can correctly interpret data and AI outputs, take responsibility and understand when they can trust these results – and when they cannot.

What is data culture?

Data culture describes the shared behaviours, skills, responsibilities and decision-making principles of an organisation when working with data. A strong data culture exists when employees can not only read and interpret data, but naturally use it to make decisions, improve processes and measure results. At the same time, they understand where data comes from, how reliable it is and the limitations of its interpretation.

A key prerequisite for this is data literacy. It describes the ability to understand and interpret data in its respective context, critically assess it, communicate it and use it for specific tasks.

In the age of AI, another skill is becoming increasingly important: AI literacy. Employees need to understand how AI uses data, how results are generated, what uncertainties exist and which decisions still require human oversight.

Today, data culture therefore encompasses three closely interconnected dimensions:

• Data literacy: People can understand, interpret and use data.
• AI literacy: People can understand, evaluate and use AI outputs responsibly.
• Data ownership: Responsibilities for data quality, definitions, use and governance are clearly defined.

Only when these skills and responsibilities come together can available data actually create business value.

Why is a data culture important?

A data-driven organisation is not defined by collecting particularly large amounts of data or providing particularly large numbers of dashboards. What matters far more is whether data actually forms part of decisions and business processes. Technology creates the necessary foundation. The organisation, however, determines whether these opportunities are used.

This includes, for example, whether employees trust a central data source or continue to maintain their own Excel spreadsheets, whether decision-makers review decisions using transparent metrics or primarily rely on experience, and whether data quality issues are actively addressed or simply passed on to other departments.

Data culture is therefore not an isolated change topic, but connects technology, data management, governance, processes and people.

Establishing context and understanding data correctly

A decline in revenue within a product segment is initially nothing more than an observation. To derive a decision from it, different relationships need to be understood: Has demand changed? Is the product available? Have prices increased? Have competitors introduced new products? Are there problems with product content, product availability or customer service?

The relevant information may come from entirely different data domains and systems. A strong data culture ensures that employees understand these relationships and do not consider data in isolation. Domain expertise and data literacy complement each other: data provides transparency, while people establish the business context and derive actions from it.

AI does not make this obsolete. On the contrary: the more AI automatically analyses information and generates recommendations, the more important the ability to assess results in their business context becomes.

Deriving and communicating insights

Data only creates value when insights are translated into decisions and actions. For this to happen, relevant information must be accessible across departmental and system boundaries. Connecting product, customer, marketing, sales and commerce data, for example, creates a much more comprehensive picture than looking at individual metrics in isolation.

At the same time, employees need to be able to communicate insights clearly. Data literacy therefore means more than simply being able to read data. It also means being able to explain its significance to other people and business functions.

A data-driven organisation establishes shared definitions, transparent metrics and appropriate communication channels for this purpose. This creates a common information base on which different areas can make decisions.

Creating responsibility for data

A modern data culture requires not only skills, but also clear responsibilities. If nobody is responsible for the quality, definition or use of specific data, even modern data platforms and governance tools can only provide limited support. Companies therefore need to define who is accountable for data from a business perspective, who defines quality standards and who makes decisions when problems arise.

New roles such as data owner and data steward help to embed these responsibilities within the organisation. The Gartner survey mentioned at the beginning also demonstrates the importance of the close relationship between data governance and data culture. Rules, roles and technologies only work if they are accepted within the organisation and actually put into practice. Data governance must therefore become part of everyday business processes. Employees need to understand why certain rules exist and how they themselves contribute to the quality and reliability of enterprise data.

Fostering innovation and creativity

A strong data culture does not only help to make existing processes more efficient. It also creates the foundation for innovation. When different data contexts and business perspectives are brought together, new relationships can become visible. Employees can test hypotheses, identify causes more quickly and develop new solutions.

Consider again a decline in revenue within a particular product segment. There may be numerous reasons for this: seasonal fluctuations, the launch of a competitor product, problems in product communication, lack of product availability, delivery issues, negative customer experiences or changing customer needs.

Only by connecting relevant data from product development, customer service, marketing, sales, supply chain and e-commerce can an information base be created that allows decision-makers to systematically investigate potential causes.

AI can provide additional support in this process by analysing large volumes of data, identifying patterns or suggesting hypotheses. However, the evaluation of these insights and the decision on appropriate measures remain embedded in the business context.

Data culture as a foundation for AI readiness

With the growing importance of generative and agentic AI, data culture is gaining an additional dimension. Many companies are already investing significantly in AI readiness – and often focus solely on technology, data platforms, models and data quality.

However, employees also need to understand which data an AI system uses, the quality of that data and how reliable an output is for the respective use case. They need to be able to distinguish between situations in which AI can perform tasks independently and those in which human review, approval or decision-making is required.

At the same time, roles are changing. People will spend less time manually searching for, preparing and analysing information and increasingly focus on evaluating results, establishing relationships and making decisions. Data literacy and AI literacy are therefore becoming increasingly intertwined. A strong data culture creates the foundation for people and AI to complement each other effectively rather than work independently of one another.

How can a strong data culture be established?

A genuine data culture does not emerge from isolated training programmes alone; it develops through the interplay of skills, responsibilities, technology and everyday practice. Companies should first understand how data is actually used today. Which decisions are based on data? Where is information missing? Where are parallel Excel structures being created? Which data is not trusted? And which skills are missing in the respective roles?

Based on this understanding, concrete measures can be developed. These include role-based data and AI literacy programmes, clearly defined data ownership, understandable governance rules and straightforward access to relevant data.

Leadership behaviour is equally important. When decisions are transparently justified using data, metrics are used consistently and data quality issues are taken seriously, data-driven working becomes part of everyday organisational practice.

To measure the extent to which a data culture has become established within an organisation, companies should not focus solely on completed data literacy training programmes. What matters far more is whether behaviour and business outcomes change: Is data used more frequently? Are decisions made more quickly? Is data quality improving? Are problems identified earlier? And are data and AI creating measurable improvements in business processes?

Conclusion: Technology makes data available – people create the value

A modern data platform can provide information. A data catalogue can make it discoverable. Data governance can define rules. Analytics can reveal relationships, while AI can analyse data, generate content and increasingly perform tasks autonomously.

The human factor remains decisive in determining whether data actually creates business value: companies need employees who understand data, take responsibility and place insights within the right business context. Data culture is therefore not an addition to a modern data strategy, but one of its central prerequisites.

Particularly in the age of AI, the role of people is changing at an ever-increasing pace: from manually processing data towards evaluating, contextualising, managing and responsibly using data and AI.

Our experts therefore consider data strategy, data governance, data quality, technology, processes and organisation together in every project, creating the conditions required for data and AI to have a tangible impact on day-to-day business.

Frequently asked questions about data culture

What does data culture mean?

Data culture describes the shared behaviours, skills and responsibilities of an organisation when working with data. In a strong data culture, data is naturally used to make decisions, improve processes and measure business outcomes.

What is the difference between data culture and data literacy?

Data literacy refers to an individual’s ability to understand, interpret, communicate and apply data. Data culture, by contrast, describes the organisational environment in which these skills are encouraged and actually applied in everyday work.

What does data culture have to do with data governance?

Data governance defines, among other things, responsibilities, rules and standards for working with data. A strong data culture ensures that these rules are understood, accepted and applied by employees in their daily work. Governance therefore requires not only technology and processes, but also the appropriate culture.

Why is data culture important for AI?

AI requires reliable and contextualised data. At the same time, employees need to understand AI outputs, critically evaluate them and use them responsibly. Data culture as well as data and AI literacy are therefore important organisational prerequisites for the successful use of AI.

How can a company develop a data culture?

A data culture emerges through the interplay of leadership, data and AI literacy, clear responsibilities, data governance, accessible data and the consistent use of data in everyday business processes. It is important to view data culture not as a one-off change project, but as a continuous development.