Datapõhised

Datapõhised is an Estonian term used to describe approaches, decisions, or analyses that rely on data and evidence rather than intuition or tradition alone. It denotes a shift toward making choices that are supported by measurements, metrics, and rigorous examination of available information.

The concept is employed across many domains, including business, government, healthcare, education, and research. In practice, datapõhised work involves collecting relevant data, ensuring its quality, and applying analytical methods to extract insights. This often includes statistical analysis, data visualization, dashboards, predictive modeling, and, in some cases, experimental designs such as A/B testing and causal inference.

Key elements of datapõhised workflows include data governance and data literacy. Data governance involves managing data quality, privacy, security, and access, while data literacy emphasizes the ability of staff to interpret data correctly and use it in decision making. The overall goal is to increase transparency, accountability, and repeatability of decisions.

Benefits of datapõhised approaches include improved accuracy of conclusions, better alignment with measurable outcomes, and the ability to monitor performance over time. They can also support accountability and explainability in policy and business processes. However, challenges exist, such as ensuring data quality and representativeness, protecting privacy, avoiding biases in data and models, and requiring appropriate infrastructure and skills.

Datapõhised practices are often contrasted with intuition-based approaches, though many organizations strive for a balance by combining data with domain expertise to interpret results in context.