3-Minute Nutshell: Artificial Intelligence vs. Predictive Analytics and ML | Pecan AI

3-Minute Nutshell: Artificial Intelligence vs. Predictive Analytics and ML

Discover key differences between artificial intelligence vs. predictive analytics in just 3 minutes. Bonus: explore data science and ML.

In a nutshell:

  • Predictive analytics, data science, machine learning, and artificial intelligence are related but distinct concepts.
  • AI is the foundation for predictive analytics, machine learning, and data science.
  • Machine learning includes various techniques for recognizing patterns in data, while predictive analytics focuses on business-oriented use cases.
  • AI encompasses broader efforts to simulate human intelligence, while predictive analytics and machine learning are types of AI.
  • Predictive analytics helps businesses understand what is likely to happen in the future and guides decision-making.

How does artificial intelligence compare to predictive analytics, data science, and machine learning? 

If you’ve tried to research this question, you’ve probably gone down a rabbit hole of Venn diagrams combined with some awkward tree and branch metaphors.

Differentiating among predictive analytics, machine learning, and artificial intelligence is definitely doable for non-experts. However, it can be confusing without the right balance of simplicity and technical detail.

Let’s break the question into three parts to identify the differences clearly.

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Is predictive analytics the same as data science?

Sort of! Overall, data science is a broader term than predictive analytics. Another blog post recently addressed how descriptive, predictive, and prescriptive approaches to data can be used.

All in all, each of those uses represents a different end goal. Respectively, those are to understand the past, to predict the future, or to find the best course of action.

Data science is the foundation for computers to execute all three of those approaches. Data science does this work more rapidly, comprehensively, and with less bias than manual analysis.

By combining statistics and computer science, data science practitioners apply techniques that accomplish descriptive, predictive, and/or prescriptive goals. For example, using data science, they might: 

  • look for significant trends in the success of past upsell offers
  • project those trends into the future to forecast customers’ interest
  • determine how best to allocate marketing and sales resources to boost upsell offers’ success

BI tools largely cover what businesses need to know about their past data without using data science techniques. However, they don’t offer a future-looking perspective or suggestions for future action.

Chiefly, predictive analytics focuses on the future. It helps business teams understand what’s likely to happen and guides decisions about what to do.

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Is predictive analytics the same as machine learning?

There’s overlap, but predictive analytics isn’t quite the same thing. Machine learning includes a variety of mathematical techniques for “teaching” a computational process to recognize a pattern in data.

Predictive analytics certainly uses techniques from machine learning. They’re usually applied to numerical (tabular) data, like you might find in a spreadsheet. 

However, machine learning can refer to a variety of methods used to analyze and make predictions on a whole range of data types. Additionally, data can include images and large quantities of text.

Predictive analytics can also use methods usually associated with statistics. For example, it might include time series analysis for forecasting. 

Finally, predictive analytics usually refers to business-oriented use cases. In contrast, machine learning encompasses all kinds of theoretical and research-focused applications as well.

Is predictive analytics the same as artificial intelligence?

Again, there’s an overlap between AI and predictive analytics. Predictive analytics and machine learning could be considered types of AI.

These methods teach a computer how to make decisions with outcomes similar to the outcomes humans would select if they were given the same data. And, of course, that would only work if humans had a much greater ability to interpret large quantities of complicated data. 

With machine learning, data practitioners can teach a computer to recognize words in a voice recording, or to predict customer churn based on transaction data — among many possibilities. But those abilities are quite limited in scope.

Meanwhile, the field of AI includes much broader efforts to simulate human intelligence. AI researchers combine many techniques and even invent different kinds of hardware. 

Photo by Michael Dziedzic on Unsplash

Bonus: What's the difference between predictive analytics and generative AI?

Predictive analytics typically uses historical, numerical data to find patterns and output predictions about trends and outcomes. It's the best tool for making predictions about the future with most business data.

On the other hand, generative AI is all about creating content, such as images, text, code, audio, and even video. It turns your input, such as a text prompt, into new content. it carries out this creation based on its knowledge of other existing content on which it was trained.

Pecan's Predictive GenAI represents a synthesis of predictive analytics and generative AI. We use the creative, code-generating power of generative AI to help you define and build your predictive model. Then, our patented, automated machine learning systems take over, providing you with trustworthy predictions about the future that can shape more informed business decisions.

As a whole, AI is a fascinating initiative that tries to help computers imitate some tasks that our humble little brains can complete with relatively little effort — and some tasks we humans could never accomplish.

Ready to see what AI can do with your business’s data? Get in touch for a personal tour.