In information systems and data analytics, data must be acted upon in a meaningful way before it becomes useful to decision-makers. What essential operation must occur to convert raw data into information?

Introduction / Context:
Organizations collect vast amounts of raw data from transactions, sensors, surveys, and logs. However, raw data by itself does not guide action. It must be organized, summarized, and interpreted so managers and analysts can make decisions. This question checks the foundational concept that converting data into information requires deliberate processing steps.

Given Data / Assumptions:

• “Data” refers to unorganized facts, figures, or signals.
• “Information” is data that has been processed so it is timely, accurate, relevant, and understandable.
• Typical environments include DBMSs, spreadsheets, and business intelligence tools.

Concept / Approach:

The classic DIKW (Data → Information → Knowledge → Wisdom) hierarchy emphasizes that information is produced when data is processed to add context, classification, calculation, and structure. Processing may include validation, aggregation, filtering, enrichment, and formatting to align with user needs and decision contexts.

Step-by-Step Solution:

Verification / Alternative check:

In reporting pipelines, ETL/ELT flows and SQL queries process data into tables, views, and dashboards. Users consume the processed outputs (information), not the raw logs.

Why Other Options Are Wrong:

Transformed: Related, but too broad and not always required; “processed” is the standard term.

Changed: Vague; change can be arbitrary and not value-adding.

Engineered: Refers to system design or features, not the generic conversion step.

None of the above: Incorrect because “processed” precisely describes the requirement.

Common Pitfalls:

Confusing storage with understanding. Simply saving data does not create information until processing adds structure and meaning.

Final Answer:

processed