Tag: Data integrity

In the modern digital age, countless terabytes of raw data are generated every second from various sources like web interactions, sensors, and transaction logs. The raw method, in its simplest form, refers to using or analyzing data or materials in their original, unprocessed state to unlock maximum detail and flexibility before transformation.

A 2025 LinkedIn article highlighted that poor data quality costs companies millions annually, underscoring the critical need for a reliable Data Quality Index (DQI). The DQI is a metric that rates the overall health of an organization's data, empowering data professionals to measure trustworthiness, enhance decision-making, and improve operational efficiency. Calculating this index involves defining key dimensions, setting rules, and aggregating individual scores into a composite metric.

According to Gartner, businesses lose an average of $12.9 million annually due to poor data quality, a metric that underscores the financial impact of inaccurate values. In science, business, and everyday life, an accurate value is one that correctly reflects the true, real-world state of what is being measured or recorded. It is a foundational component of reliable data and sound decision-making, distinguishing itself from the concept of precision.