Data should provide valuable insights and support decision-making in the best possible way. In order to remain permanently competitive, companies must continuously develop their data management and data usage. In this context, artificial intelligence and automation are becoming increasingly important. However, the basics of handling data are even more important: security, quality and governance. This insight is revealed by the BARC Data, BI and Analytics Trend Monitor 2025, which examines which topics in data, BI and analytics are crucial to a company’s success. The survey shows what is most important in a data-driven approach: a balanced relationship between technological and human aspects.
Companies must protect their data from theft, manipulation and destruction – in their own interest, but also because legal regulations are becoming increasingly strict. For example, the EU NIS 2 directive prescribes specific measures to protect against cyber attacks. It applies to companies from a total of 18 sectors that are essential for the smooth functioning of the economy and society. The aim is to increase the general level of security and ensure the permanent availability of facilities.
There is undoubtedly an awareness of the importance of data security/data protection. This is evidenced not least by the fact that the BARC Data, BI and Analytics Trend Monitor has once again ranked it number one. What is lacking, however, is the implementation and coordination of adequate technical, physical and organizational measures. Many companies complain that they simply lack the time and personnel resources to do so.
The basis for the right business decisions is consistent, correct and quality-assured data. In addition, high data quality increases the flexibility of business users and strengthens their trust in the existing data pool. Harmonized (master) data is crucial for collaboration between company divisions, as it enables consistent reporting and promotes data-driven processes.
Critical success factors for sustainably high data quality include clearly defined roles and responsibilities, established and functioning processes for quality assurance and continuous monitoring of data quality. It is at least as relevant that all employees are aware of the negative consequences of poor data quality.
A data-driven culture is characterized by the fact that data is not only available, but also actively used to ensure business success and drive innovation. It is an absolute prerequisite for data-driven companies that are able to make strategic decisions, increase operational efficiency and achieve competitive advantages based on the comprehensive use of data and analyses.
A living data culture must involve all employees. Six fields of action are relevant in order to establish it: data strategy, leadership, governance, competence, communication and access. First and foremost, it comes down to people, who must be prepared to change their behavior and mindset and integrate data use into their daily activities.
Data governance provides the guiding principles for the implementation of a company’s data strategy by formulating guidelines and framework conditions for the maintenance, monitoring and protection of data. It takes equal account of people, processes and technologies. The data strategy in turn controls how data is used in business processes in order to increase efficiency and generate innovations.
As far as the design of the underlying data architecture is concerned, decentralized and federated approaches to data management are currently on the rise. One example is the domain-driven data mesh, which transfers responsibility for data and data quality to the business departments (domains). Data is treated as a product that the specialist departments promote throughout the company.
Corporate decision-making is increasingly based on data. Accordingly, data literacy has become a crucial skill for employees in all functions and departments – far beyond the role of traditional data experts. According to BARC, data literacy is the fundamental ability to work with data and includes not only analytical skills, but also requires an understanding of data models and data sources as well as knowledge of the available software tools. A company can only become a data-driven organization when all employees have the necessary data skills to derive insights from data and turn them into business value.