The traditional model of official statistics developed in an era of scarce data, when targeted surveys were the primary information source. Questionnaires, censuses and sample surveys long provided the necessary inputs to describe economic and social processes. Today, however, that model increasingly fails to meet user demand for timely and detailed information: conventional surveys remain resource-intensive and typically deliver aggregated results with delay.
A changing data environment and new challenges
The digital data environment has made administrative and alternative data sources more available and often faster than traditional surveys. These sources are nevertheless heterogeneous in volume and type, which raises the importance of structuring, linking and interpreting data. In this context, "data assets" are not merely the totality of available datasets but the structured knowledge derived from exploiting them collectively.
Technological opportunities: automation and machine learning
Advances in data technologies are another key factor. Automation can streamline the data-processing chain, reducing time and resource demands while improving consistency and reproducibility of results. Machine learning methods help detect complex patterns and anomalies in data and contribute to forecasting. These tools can support a reorganization of statistical production processes but require appropriate methodological and regulatory frameworks.
Methodological transparency and the role of artificial intelligence
While market practice increasingly relies on so-called "black box" AI solutions, official statistics must preserve methodological transparency and reproducibility. For KSH, the use of artificial intelligence should be understood as a support to professional decision-making rather than a replacement: deployment must ensure models remain professionally verifiable and statistical quality is maintained.
Need for organizational and institutional change
The technological transformation of data processing also implies the need to change organizational practices. Beyond acquiring new tools, statistical agencies must rethink institutional arrangements and attitudes toward data-asset management. A central direction is the development of integrated data platforms that link administrative and new data sources, allowing structured management and multi-purpose use.
Data assets as a factor in national competitiveness
Effective data-asset management goes beyond internal statistical operations: it increasingly determines national economic competitiveness. Countries that can manage and integrate their data sources, and rapidly produce reliable decision-supporting knowledge from them, will be advantaged. Competitiveness thus depends not only on production capacity, energy supply or financial resources, but also on the ability to transform available data into strategic knowledge and decisions.
Conclusion
Consciously managing data assets is a technological, methodological and institutional challenge. Official statistics, including KSH, must assume a central role in the data ecosystem: only then can data-asset management underpin the renewal of statistics and serve as a key pillar of long-term economic adaptability and competitiveness.
