Big data refers to extremely large and complex datasets that require advanced analytics to extract insights. Effective visualization is a crucial step in making big data analytics actionable.

Let’s analyze the options:

A. Big data is often structured.

Incorrect. Big data can be structured, semi-structured, or unstructured. Many sources of big data (e.g., social media, sensor data, emails) are unstructured, making analysis more challenging.

B. Big data analytic results often need to be visualized. ✅ (Correct Answer)

Correct. Due to its complexity, big data analytics results must often be visualized using dashboards, charts, or graphs to communicate insights effectively.

Examples of visualization tools include Tableau, Power BI, and Google Data Studio.

C. Big data is often generated slowly and is highly variable.

Incorrect. Big data is typically generated rapidly and continuously (e.g., social media posts, IoT sensors, financial transactions). This relates to the "velocity" characteristic of big data.

D. Big data comes from internal sources kept in data warehouses.

Incorrect. Big data comes from both internal and external sources, including social media, cloud applications, and sensors. Additionally, data warehouses store structured data, whereas big data is often unstructured and stored in data lakes.

IIA GTAG – Auditing Big Data Analytics – Explores best practices for analyzing and visualizing big data.

COSO ERM Framework – Technology & Data Risk – Discusses the need for big data governance and visualization.

ISO/IEC 27032 – Cybersecurity and Data Analytics – Covers big data security and interpretation.

IIA Standard 2120 – Risk Management in Big Data Analytics – Focuses on internal auditors' role in overseeing data-driven decision-making.

IIA References: