INBOX INSIGHTS, April 4, 2024: AI Data Governance, Generative AI Language Tasks

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How Good Is Your Data Governance?

Last week, Harvard Business Review asked the question, “Is your company’s data ready for Generative AI?”

Because I work with a variety of clients, I would say a resounding, “No!”

You can read the full article here.

You need at a minimum, decent data governance before you can use your data in a generative AI system.

Before we get into that, let’s all get on the same page about where generative AI fits into the Data Analytics Hierarchy.

Say, what?

You’ve never heard of the Data Analytics Hierarchy? That’s surprising. The name totally just rolls off the tongue.

For those that don’t know, the hierarchy consists of five stages: Descriptive, Diagnostic, Predictive, Prescriptive, and Proactive. Think of it like rungs of a ladder. You start on the bottom rung and climb your way up to the top.

This is what it looks like. Forgive me, I’m not a designer:

Hierarchy of analytics

Here’s why I say that your company’s data is not ready for generative AI. Most companies, whether they recognize it or not, are still at the bottom of the hierarchy. They cannot confidently say, “What happened?”. If they can say what happened, they need to determine if they can also say, “Why did it happen?”. Generative AI doesn’t enter the chat until the at least the third rung.

“But I’ve used generative AI to analyze my data!”

Using generative AI to make sense of your data is not the same as having solid data governance.

I get it. This is the not-as-fun stuff. Think of it like eating your vegetables so you can have dessert. Data governance is Brussels sprouts. You either love them or hate them. Either way, they are really, really good for you and you’re better off if you eat them.

(Stops to dig around in the produce drawer of the fridge for a snack)

Back to the point. If you don’t have good data governance, you won’t have good data analysis. How do you set up good data governance? I’m so glad you asked!

The 5P Framework!

  • What is the Purpose of collecting and using the data?
  • Who are the People responsible for and that have access to the data?
  • What is the Process for collecting, cleaning, and maintaining the data?
  • What are the Platforms use for data collect, storage, and analysis?
  • What is the Performance of the data? Are we confident in it?

Think about your Google Analytics 4 data as an example. I’ve heard numerous complaints from users that the data is different from Universal Analytics and they don’t believe the numbers. If this is the case, why are they still trying to make decisions from it? Generative AI won’t help. You have to get to the root of the issue, and that’s your data governance.

Once you have a solid foundation, the rest should come “easily”. I put that in quotes because maintaining and analyzing your data for insights isn’t easy. If you have repeatable processes in place and people who can maintain your systems, you can ensure data quality.

The moral of the story is eat your vegetables.

Well, the real moral of the story is to have a strong data foundation through your governance. When you have that piece in place, you can keep moving up the hierarchy and confidently bring generative AI into your company.

Quick plug – if you want help with your data governance, we can do that. Give us a shout!

Is your company’s data ready for generative AI?

Reply to this email to tell me or come join the conversation in our Free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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Data Diaries: Interesting Data We Found

In this week’s Data Diaries, let’s go over an important distinction about generative AI, large language models, and language tasks.

One of the pieces of advice we regularly follow is to try using AI for everything, especially language tasks. Language models, unsurprisingly, are really good at language tasks such as writing, classifying sentiment, summarization, and many others.

Equally unsurprising, they’re not particularly good at tasks that aren’t language tasks, like math. Many of today’s tools get around challenges like this to some degree by writing code in the background that can do math, executing the code, and returning the math results to us as though they had done the computation.

This distinction is important when deciding what type of AI to use for any given problem. This week, I had the pleasure of speaking with the Lab Products Association membership about generative AI and doing a hands-on workshop with them. One interesting question I was asked highlights our language/non-language distinction.

One of the members asked, “We want to set up a recommendation system with generative AI so that if you put something in your cart, it recommends other things from our product catalog.” On the surface, this seems like a language task, right? There’s a product, the product has a name, the name is made with words, and other products have names and words too.

Except it’s not a language task. It’s actually a symbolic task – a task where the language doesn’t really matter. What this member cares about is associating one entity with another, and that’s symbols, not language. In some cases, language will work; after all, if I look at the phrase “peanut butter”, jelly is likely to be associated with it in language texts. This works when you have very common pairings. When you have infrequent pairings, this doesn’t work.

And when you have a product catalog from your specific store, what you care about as a store owner is recommending more things from your store that other customers buy, not necessarily generic word associations.

If you took the language away – if peanut butter was instead written as B07KWH27FN, you would still be able to do associations using classical AI. You’d know that B074J9RHF8 was most closely associated in other purchase carts with B07KWH27FN and be able to make that recommendation no matter what these products were – that’s the nature of a symbolic task.

So what? Why does this matter? Because generative AI is computationally very expensive compared to older classical AI techniques. It takes a long time – comparatively – for models to parse and return results. Using it for a task it’s not well-suited for could be a large, expensive, unproductive undertaking, so understanding the kind of task you’re doing is an essential part of requirements gathering. When you understand the nature of the task, you can fit it to the appropriate technology and spend the right amount of time, effort, and money on it to get the result you want.

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