3-Minute Nutshell: Predictive Analytics vs. ML vs. AI
Welcome to our 3-Minute Nutshell series, where we’ll answer FAQs about predictive analytics in just a few minutes of your time! Get up to speed on key ideas you need to know to start your business’s journey toward AI success. Catch up on our first post, What Is Predictive Analytics?, if you missed it!
Is predictive analytics the same as data science, machine learning, and AI? 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.
Is predictive analytics the same as data science?
Sort of! Overall, data science is really a broader term than predictive analytics. In our last 3-Minute Nutshell article, we talked about how it’s possible to use descriptive, predictive, and prescriptive approaches to data. 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 data science techniques. But they don’t offer a future-looking perspective, nor do they offer suggestions for future action. Chiefly, predictive analytics focuses on the future. It helps business teams understand what’s likely to happen and guiding decisions about what to do.
Predictive analytics usually refers to business-oriented use cases.
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. Furthermore, predictive analytics can also use methods usually thought of as belonging to statistics. For example, that 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.
The field of AI includes much broader efforts to simulate human intelligence.
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.
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! And watch for more articles in this blog series to learn more about predictive analytics, how it works, and how it can benefit your business.
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