Big data and AI are two distinct concepts, but they are related and often work together in a project that develops data-driven solutions.

The diagram in the previous section offers a description of what constitutes big data. Let’s look at what defines AI and how it interacts with big data.

Definition: While there is no globally agreed AI definition, in principle, AI involves creating algorithms that allow computers to perform tasks that typically require human intelligence. It encompasses machine learning, deep learning, natural language processing, and more.

Purpose: AI aims to create systems capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

Components: It includes algorithms, models, and systems that are designed to simulate human intelligence and learning.

Big data and AI often work hand-in-hand. AI algorithms, especially in machine learning, require large amounts of data (big data) to learn from patterns and make decisions. Conversely, big data analytics can be enhanced using AI technologies to derive more complex and intelligent insights.

Data Utilisation: AI uses big data to become more intelligent, to “learn” and to make decisions. The large datasets derived from big data are analysed and utilised by AI algorithms to develop models, make predictions, and enhance automated decision-making.

Enhanced Analytics: With the use of AI, big data analytics can be more accurate and provide deeper insights, as AI can detect patterns and correlations that might be too complex for traditional data-processing software to identify.

While they can serve different purposes and functions, big data and AI complement each other and, when integrated, can immensely enhance the capabilities of analytical and decision-making systems.