Is big data dead?

How LLMs have impacted the need for big data; Increase your productivity with these AI tools

Welcome to another edition of what we’re determined to make the best damn newsletter in AI. Here we’ll break down AI topics that matter, open your mind to use cases, and keep you ahead of the curve.

Our #1 goal is to be useful. So please shoot us an email 📩 if you have questions or feedback, and especially if you implement something we share!

Here's what we're covering today:

  • Is the hype over big data gone thanks to large language models?

  • A use case focused on increasing your productivity with AI

  • And a look at a few of the huge announcements in the AI space over the last few days

... and if someone forwarded this email to you, thank them 😉, and subscribe here!

Let’s get to it! 👇


Is big data even helping anyone?

“We’ve been collecting every piece of data about who buys our products, what they look at, where they come from, and more for years. But we haven’t even started getting the value out of it. And now there’s this whole new AI thing.”

The story above is one we’ve heard a lot.

Now that we have AI, is big data a waste of time? How will it help me in the next wave?

First - big data itself is most certainly not dead.

Building AI systems like ChatGPT requires a massive amount of training data. In fact, some researchers are worried about an eventual data shortage.

But the type of data needed by language models like ChatGPT is different! It’s text, not necessarily tables, databases, and data lakes.

This doesn’t mean that big data has no value. In fact, we’re going to be covering how to combine big data and generative AI very soon - if you have questions or ideas, reply and let us know.

What it does mean is that having a lot of things written down might put you ahead in this next wave. Think about the fact that if you already have a robust CRM, using Hubspot’s Chatspot, or Salesforce's Einstein GPT, has a lot more value.

We are likely entering a world where text becomes the next big data.

But your data might not need to be as “big”

One of the under-explored areas of generative AI right now is - if you can generate coherent stories, can you generate valuable data?

While there are a lot of types of data that you would need to be 100% correct, there are some early use cases emerging where generated data can be just as useful. (We’ll cover that below)

The promise of big data is to be able to (1) uncover game-changing insights, and (2) hyper-personalize your offers to your customers.

Getting there may require a mix of data analysis and machine learning, and now the barrier is much lower:

  1. Don’t have enough rows in your dataset? Use GPT to generate more.

  2. Don’t have enough columns that help you predict? Use GPT to infer what new columns could be, and generate them.

  3. Have messy data like product reviews that you aren’t using to their fullest? Use GPT to extract structured data, like sentiment.

  4. Don’t want to build a full-scale machine learning system to test out an idea to add product recommendations? Use GPT to make a lightweight recommendation engine.


Transcribe, summarize, and even present notes from your meetings to others

With virtual meetings becoming the norm, most of us are continually looking for ways to improve our note-taking and retention of important points post-meeting.

That's where AI can help!

Brynn Bendixen, CEO & Founder of Transformate Group, uses, Quillbot, and to transcribe, summarize, and communicate key takeaways for important meetings.


For your reading list 📚

Some big announcements in the past couple of days...

And if you're really nerdy...

Member-only links (more info on joining here!)

That's all!

We'll see you again on Tuesday. Thoughts, feedback and questions are much appreciated - respond here or shoot us a note at [email protected].

... and if someone forwarded this email to you, thank them 😉, and subscribe here!


🪄 The AI Exchange Team