Jessica asks, “Which concepts or tools to be developed will inform the future of marketing data science?”
The biggest changes will be on the technology side of marketing data science. Many tasks, like data cleaning and imputation, will benefit from what’s happening in AI.
- Transfer learning
- Massive pre-trained models for things like images, text, and video
- Tools like IBM AutoAI and other AutoML tools
- Better tools for exploratory data analysis
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, Jessica asks which concepts are tools to be developed will inform the future of marketing data science? Hmm.
When we think about data science, we have the four major categories, right business skills, scientific skills, technology skills and mathematical skills.
Mathematics, at least as it pertains to data science is not advancing terribly rapidly, because a lot of the concepts are already pretty well known.
We know, for example, how to do linear regression or curve fitting or any of the algorithms that have become very common in data science.
And so there doesn’t need to be a whole lot of emphasis placed on how are we going to improve these algorithms, where the big changes will occur is on the technology side.
The technology that powers data science Right now when you think about what requires the most effort, what requires the most, it was the greatest challenge to data science, it is a lot of the data cleaning and data preparation.
And this has been the case for Gosh, decades.
The greatest amount of time you will spend in data science is taking your data and preparing it for usage.
And that process of taking it, cleaning it, analyzing it, looking for outliers, errors, etc.
And sometimes having to restart the whole process when you find out that Yep, a data source or a data set isn’t any good, is time consuming? It’s not super high value.
And it is substantially error prone.
And that’s where the tools that are available will really help to turn this into something more helpful.
So there’s four areas I think that you’ll see a logical innovation that will improve data science.
Number one is transfer learning transfer learning is from machine learning and artificial intelligence.
And transfer learning is when you take a working model of some kind and you port it to a different domain where it needs to be retrained only a little bit, you will see this real simple example from a human side.
If I teach you to catch a ball, I throw a little like, a little tennis ball at you, right, and you’ll learn how to catch this thing.
It takes minimal retraining to do this with the baseball, right? It takes a little more free training to do it with, say, a basketball or a soccer ball.
But fundamentally, you understand that you’re catching a big round thing and you have to do it in a certain way.
Right? You’re not going to attempt to use probably not going to have to use your feet, you know or catch it in your mouth.
You’re probably going to use your hands and so The ability to transfer that skill across different domains is an area where data science will benefit because again, if you’re taking in and cleaning a numerical data set for, say population data, it’s not terribly hard to rearrange that for, say customer data.
This also is where as a data science test, you’re going to see a lot of easy wins early on, because you’ll be able to find models and techniques and algorithms that work really well in one domain and move them to another domain with minimal relearning.
So transfer link both of the technical perspective and for your personal skills.
The second area is a branch of transfer learning and that is what’s called tuning, model tuning.
And what used to be the case in machine learning as you would get the source code for a major model or algorithm and you would take your own data You’d build your own training data set, fine tune it, retrain it, fine tune it, etc.
And this was extremely expensive, extremely time consuming, and had the potential how things go really wrong.
The new trend is to take an existing massively pre trained model like GPT, two for text, or wavenet, or image net models, and take those massive pre trained models and only just fine tune it for your specific data set.
This is how you can get, say, an AI powered chat bot up and running sooner rather than later.
You can do this by having these pre trained models, and then just fine tuning.
Again, when we’re talking about things like understanding a large corpus of data, having a pre trained model that that understands the entirety of the English language would be awfully handy and save you a lot of time having to reinvent the wheel.
So pre trained models second Third thing is the rise of auto AI and auto ml.
So auto AI is and IBM Watson Studio Auto ml is the category overall, these are tools that do a lot of the grunt work on processing data.
To give you some, some conclusions about mostly classical machine learning outcomes.
So give it say, your marketing automation data set and ask it what drives lead score and I’ll perform probably a regression analysis if your lead score is numeric, otherwise I’ll do a classification of it’s like you know, A, B, C, D, or E, lead scores, and then do all of the combinations, all the permutations, all the cleaning, and give you things like feature importance, which variables seem to matter the most to the outcome you care about these techniques.
They’re limited in scope right now to things like regression and classification.
But they offer a very powerful potential future for us.
Because for us to do the same thing, yes, you absolutely can.
You can fire up, you know, the modeler of your choice or the regression algorithm of your choice.
And there’s cases where you’ll want to manually choose the algorithm.
But these tools will give you the opportunity to have a bunch of things tested and to accelerate that part of the data science process.
So that you can verify Yeah, for this data set, you know, a gradient boosting algorithm was the right choice with this one.
Here, lasso ridge regression, or lasso or ridge regression was the way to go.
And they also take care of things like hyper parameter optimization, which is a lot of fumbling around with, if we think of baked goods, right, you think I’m making a cake and you’re baking a cake in the oven the ingredients and how you tune the cake are the parameters, your hyper parameters would be the time it takes and you know what temperature you set the oven at.
And hyper parameter optimization is essentially baking a million cakes at every you know, every degree Have temperature between 205 hundred to see which cake comes out the best.
That’s time consuming.
And in the data science world, something you want to try to avoid.
So having machines that can do all that testing for you and tell you, you know, this cake with this ingredient bakes at 284 degrees for 47 minutes to achieve the the ideal outcome is the the type of optimization these tools do.
And the last area where there’s room for substantial improvement is on exploratory data analysis.
Again, many data scientists have their own favorite techniques and their own favorite libraries.
But these tools continue to advance as they should continue to advance and ultimately deliver a really good sense of of what’s in your data set.
Those libraries need to continue to improve because exploratory data analysis is very time consuming, having a preset, you know, pile of techniques that you can run, semi supervised and come back later and see what it came up with.
will be a huge time saver for data scientists to be able to make the most of their data.
So, good question.
There’s a lot of technological improvement that will accelerate the drudgery, parts of data science, leaving the humans all of us to be able to focus on what really matters, which is the insights, the analysis, and ultimately the strategy and the tactics that we choose to act on from the data itself.
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