AI and data analytics – What is the connection
AI and data analytics have been used in conjunction for some time, to the point where people rarely distinguish between the two terms. However, as data analytics and AI capabilities become more widespread and applied to different business operations like marketing and supply, it’s essential to understand the difference between the two and what role they will play in business operations. That is my intention for this blog post: To explain the connection between analytics and AI.
How is AI and data analytics defined?
Before explaining the connection between AI and data analytics, we need to take a moment to define the terms. AI or Artificial Intelligence is technology designed to emulate the human mind, particularly in areas such as analysis and learning. AI is designed to draw conclusions on data, understand concepts, become self-learning and even interact with humans.
Data analytics refers to technologies that study data and draw patterns. However, the function can vary based on the type of technology. For example, descriptive analytics can study data to describe what is happening, while predictive analytics can predict what will happen based on current occurrences. Furthermore, when it comes to data analytics, it is not a single product. It is a rich ecosystem of programs ranging from the basics like descriptive analytics and BI to more advanced programs such as data mining, forecasting and pattern matching.
The connection between AI and data analytics
AI and data analytics are connected because the former boosts the capabilities of the latter to deliver deeper and better insights beyond what human analysts can do. The point is best demonstrated through an example. A supermarket chain offers a loyalty credit card program that allows customers to accumulate points when they use the credit card. The points can be spent on a day at the golf course.
Descriptive analytics reveals that, out of 10,000 members, over 1,500 cashed in their points for a day at the golf course and all of them were middle-aged men. Predictive analytics reveals that with a 10% increase in advertising, the supermarket chain will see a 20% increases in the number of middle-aged men cashing in on their reward points.
However, with AI, the bank will discover the number of members who live near a golf course, those who like the sport but don’t go, if any female members are interested in golf and set parameters for golf season in certain climate zones. Using this information, the bank can then develop specific, targeted programs for single men, single women, married couples, families and more. Therefore, AI enhances the capabilities of data analytics tools to deliver granular, micro-targeted insights that were not possible before.
Besides enhancing analytics capabilities, AI also improves the data analysis process. Since data comes from structured and unstructured sources, it must be cleaned and organised before it’s ready for analysis. Data analysts spend 80% of their time cleaning and organising data. AI can be used to accelerate this process, thus saving data analysts time and making the process more efficient.
AI and machine learning can also enhance the capabilities of data analytics models beyond their current capabilities. An excellent example is fraud prevention in insurance or banking. With machine learning, analytics models can identify fraudulent transactions in real time. The analytics models can identify fraud as it happens because data analysts feed data on past fraud incidents to the analytics models. The AI can study data, learn the patterns that make up a fraudulent transaction to identify future fraud transactions. If fraudsters change their methods of attack, machine learning can adapt by picking up on these new methods.
Key takeaways
AI and data analytics are often used together because the former boosts the functionalities of the latter. With AI, analytics technology can conduct more in-depth analysis paving the way for micro-targeted insights that are not easily found by human analysts. Complex analysis with several variables can be done quickly and efficiently with AI.
AI in data analytics also makes it easier to clean data – a vital step in the analysis process. It’s important to understand that AI and analytics are not the same and should not be considered as such because AI is part of the analytics ecosystem. Companies must understand the difference and be willing to use the technology if they wish to gain an edge over their competitors.
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