The increasing use of artificial intelligence (AI) to automate processes and decisions is further boosting the already enormous importance of data for business success. To ensure their long-term competitiveness, companies must continuously develop their data management skills and be able to transform data into insights, thereby creating a stable basis for decision-making.
The BARC Data, BI and Analytics Trend Monitor 2026 summarizes the topics that companies should put on their agenda in order to become data-driven organizations. The results of the survey, which included nearly 1,600 participants, underscore that a trustworthy, carefully managed database continues to form the basis for innovation and business value. Despite the emergence of AI and automation trends, fundamental aspects such as quality, security, and governance remain the top priorities for companies. Without them, long-term success in the use of data and AI is simply inconceivable. We take a look at the five most important trends from the BARC Data, BI and Analytics Trend Monitor 2026.
The importance of high-quality data for business success is clear: Informed decisions can only be made if they are based on reliable, consistent information. Data quality is becoming even more relevant in light of the rapid spread of AI systems and autonomous AI agents. They must be trained with high-quality data so that they can deliver accurate forecasts and appropriate recommendations for action. This now also includes unstructured information, whose quality and consistency have become crucial for reliable AI results.
Companies have a whole range of measures at their disposal to improve data quality. These include defining relevant quality parameters and validation rules, automated quality monitoring, anomaly detection, as well as data cleansing and targeted enrichment. Once a high level of quality has been achieved, the aim is to maintain it in the long term – with clear responsibilities, stable control and security mechanisms, continuous data integrity checks, and a strong awareness among employees of the relevance of high-quality data.
Although data security and privacy are among the most important issues in the context of data & analytics, many companies have significant shortcomings in this area. In practice, these range from outdated risk assessments and insufficient contingency plans to a complete lack of adequate security measures. This is a dangerous situation given that cyber attacks on companies are not only becoming more frequent, but also more sophisticated.
Consequently, protection mechanisms should also be continuously developed. Data security encompasses all measures that protect information from unauthorized access, manipulation, and loss. Basically, the measures can be divided into three areas: prevention, detection, and response. Access restrictions and encryption effectively protect data. In the event of a cyberattack, it is crucial to detect it quickly and prevent or limit damage as much as possible. An effective security and emergency plan includes methods for identifying attackers and measures for restoring data.
Those who operate in a data-driven manner benefit from well-founded decisions, rapid responsiveness to change, and long-term competitive advantages. However, the comprehensive, systematic use of data and analytics does not only have a technological component. Rather, it also depends on the mindset of employees. Companies must undergo a transformation—towards a data-driven culture. The aim is to permanently embed this way of thinking in all departments of the company.
As skills related to data usage and artificial intelligence become increasingly important, it is essential to empower employees to use data effectively. Clear structures in terms of data governance (see trend #4) and a high level of data security and protection create trust. Modern concepts for information availability and data access promote transparency and ensure that data can be used smoothly in everyday work – for example, through self-service and independent data preparation by users in the business departments. Such elements create a data-driven culture in which data is not simply available, but actively contributes to greater innovation, improved efficiency, and sustainable growth.
In order for companies to derive added value from their data, they need a fundamental data strategy. This strategy controls how the business strategy is translated into data- and analysis-based measures. Data governance, in turn, acts as a control instrument for the data strategy. This involves a comprehensive, cross-system perspective on data usage – beyond individual applications. With the increasing use of data products and domain-driven data mesh structures, decentralized, federated data governance approaches are currently becoming more and more prevalent.
Because artificial intelligence has become an integral part of business processes and decision-making in many companies, AI governance adds an additional layer to the organizational framework for data management. It defines how AI models must be developed, monitored, and controlled in order to adequately address criteria such as fairness, transparency, and accountability. Together, data governance and AI governance provide uniform guidelines that effectively organize the interaction of people, processes, and technologies in the use of data and AI.
Data and AI skills are becoming essential for employees in almost all departments and industries. They need to be able to understand, interpret, and work with data and artificial intelligence. This includes analytical skills, knowledge of data models and data sources, and the effective use of tools and technologies to support informed decision-making.
Those who fail to embed the necessary skills throughout their organization will find it difficult to extract relevant insights from data and use them for corporate management. It is therefore a matter of empowering your own employees to work with data and integrating a data-driven approach into their everyday work – a worthwhile investment on the path to better decisions, greater agility, and sustainable business value.