The difference between Artificial Intelligence (AI) and Data Science – and why it matters

Artificial Intelligence, AI, data science, machine learning, automation, large language models… if you’re starting to see all of these terms being used interchangeably, you’re not alone. AI and data science in particular cause some confusion, and so we’re taking the opportunity to deep dive into what artificial intelligence and data science have in common, and how they are different.

Before we do that, keep this simple truth top of mind: AI’s effectiveness is closely tied to the quality of data within an organisation because AI models learn from the data they are fed. High-quality data leads to accurate predictions and decisions by AI models, whereas poor quality data can result in inaccuracies and failures. Successful AI implementations require data that is accurate, comprehensive, and representative.

This makes good data quality essential for training AI models effectively, as each incorrect data point can mislead the model, leading to unreliable outcomes​. So, while we highlight what each is, remember that they work together, and you cannot have a great AI strategy without a strong data science component.

What is the difference between data science and Artificial Intelligence (AI)?

Data science and Artificial Intelligence (AI) are both instrumental in modern, digitally transformed businesses, but they serve distinct purposes and are built on different principles and methodologies.

Data science is fundamentally about extracting useful insights from data. It’s about identifying patterns, making predictions based on historical data, and aiding decision-making processes. Tools commonly used in data science include Python and R, focusing on data analysis, analytics, and predictive modelling​​​​.

Artificial Intelligence (AI), on the other hand, is about creating systems that can perform tasks requiring human intelligence. AI is characterised by its ability to mimic cognitive functions such as learning, reasoning, and self-correction. It’s divided into two main types: generativeAI, which handles tasks like speaking and translating, and applied AI, which includes technologies like autonomous vehicles.

The key differences between data science and AI can be summarised as follows:

• Data science is primarily concerned with analysing data to discover insights and trends that inform decision-making. In contrast, AI focuses on creating algorithms and models that enable machines to perform tasks without explicit human instruction.
• While there is an overlap in the tools and techniques used, data science relies heavily on statistical analysis and visualisation tools to understand data patterns. AI, however, leans more towards machine learning and deep learning frameworks to build models that can learn and make predictions or decisions based on data.
• The outcomes in data science are often specific and aimed at answering particular questions or solving problems with data-driven evidence, while AI outcomes are aimed at replicating or surpassing human cognitive abilities in various tasks.

Both fields offer promising career paths with high demand across industries. The choice between data science and AI should be based on an individual’s interests, career goals, and the specific aspects of technology and analysis that excite them most. Whether analysing data to uncover insights or developing intelligent systems that can think and act autonomously, both paths offer the opportunity to be at the forefront of technological advancement and innovation.

For organisations, it’s important to have a range of skills and expertise within the business and to avoid assuming that someone with data science experience and skills is an AI expert and vice versa.

How to leverage AI and data science

Organisations looking to leverage AI tools effectively need high-quality data. Here are five strategies that can help improve the quality of data:

1. Implement data governance policies which will significantly improve data quality over time​​.
2. Offer training programs focused on data quality management can equip employees with the necessary skills to handle data responsibly.
3. Keep detailed documentation, including data lineage and transformations applied to prevent misunderstandings that may lead to errors in data interpretation​​.
4. Introduce checks toto ensure the accuracy and consistency of the data being collected and to reduce the amount of incorrect or inconsistent data entering your systems​​.
5. Regularly utilise data cleansing tools to help identify and rectify errors in datasets,for early detection of quality issues, allowing for prompt corrective actions​​.