Denis asks, “What is the big promise that AI holds when it comes to data? What types of solutions do you see emerging from this that will help marketers?”
Look at the data science lifecycle. Every repeatable choice along this lifecycle has at least some portion which is a repetitive, predictable process. Where we’ll see the earliest, easiest wins is in the first third. Watch the video for full details.
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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, Dennis asks, What is the big promise that AI holds? When it comes to data? What types of solutions do you see emerging from this that will help marketers
win? When we think about data and marketing, one of the biggest gaps is that we frankly, aren’t using enough of it. According to a relatively recent statistic by IBM, up to 90% of data within a corporation goes unused. And
there are a lot of marketers who are still what I would call data resistant, they are not doing as much with data as they could be, they have systems that are collecting data, but they don’t really use it, they don’t look at it, they don’t
they don’t use it to change behavior or to to, to improve marketing. Real simple example of this. Yeah, think about just how much information is in Google Analytics? And how much do you actually look at? How often do you look at it, the answers usually are not as much as I should look, and not as often as I should look, right, because it can be overwhelming for some folks who, again, are not living in in their data
more broadly, when you look at the data science lifecycle, and you and you see this,
this rhythm of data, define, ingest, analyze, repair, clean, prepare, augment, explore, compare, predict, prescribed, model, validate, refined, deploy, observe almost every step, and the data science lifecycle has at least some portion of the tasks in it,
that is repetitive work. And anytime you’ve got repetitive work, you’ve got a candidate for robotic process automation, or machine learning, or potentially deep learning, depending on how complex the problem is, and, and the technologies that are available to you.
And so the opportunity for marketers is, as they become more data driven as, as their companies demand that they become more data driven, and that they use machine learning and artificial intelligence to accomplish those tasks. And where the early wins are going to be is all in that first third of the life cycle. When you’re looking at bringing in data ingesting data from multiple sources. A big part of that is identifying where is the date, and there are packages of software, IBM Cloud lift as an example that you can deploy within the enterprise. And it will essentially do digital sniffing around and pull helping you pull data out of heterogeneous incompatible systems and get it at least in one place. The same is true for analysis, repair, and cleaning of data. There are a number of excellent libraries in the various programming languages that do amputation, which is computing missing values, there are anomaly detection libraries that can help you figure out up there are some oddities in this data that we need to clean up. And of course, in data preparation, there are any number of tools that can help you prepare your data for
analysis for use for building models from for in the machine learning realm. And so that’s where that’s where the opportunity is, the opportunity is for marketers really early on is all about how do we get at our data? How do we prepare it for us? And then how do we start building
building solutions with that data
in terms of what those solutions look like for the most advanced marketers, they’re already in place. And those are marketers who either all are also data scientists or, and machine learning specialists or have those capabilities somewhere within their company where the average marketer is going to see benefit is in the various products and services that they already use. Most of
most companies that are in that providing marketing technology are moving in this general direction at at varying speeds. We see some hints of this in tools like Google Analytics intelligence, where if you’ve ever seen the little blue icon in the upper upper right hand corner of the analytics screen, you tap it and you can ask your analytics questions. You see this in Watson Analytics by IBM where you can literally just ask it questions by typing questions into the interface like hey, what makes this work
here we see a tremendous amount of machine learning happening in it’s at the CRM level. So Salesforce with its Einsteins software, we see it with HubSpot and number of its packages. And all these are really about helping us make better use of our data. That is, that is the key if we’re, if we’ve got the data and we’re not using it, we’re missing a massive opportunity to be like owning an oil field and never drilling a well or yet you drilled one well with a spoon and any drinking straw,
it’s not going to go very well.
So those are the solutions that you’re going to see you’re going to see that built into a lot of folks now, where there are going to be gaps is that many of the point solutions that are out there, such as you know, Einstein, and Salesforce and stuff can only work with the data that they can see
there is still a massive need for and an a gap of governance when it comes to the data that you have within your corporation. And to this I would look towards standards like ISO 38 500, which you can see a martek version of on the Trust Insights website,
which detailed This is how your corporation is supposed to work together it and marketing and technology
to run the system as well. And to help people work together and combine disparate systems,
many of the obstacles within the companies are around the people in the processes less about the technology the technology has, most packages have things like APIs that allow you to connect systems together. But the bigger obstacle is in the way people work together or don’t work together within a company
who has access to what data who’s permitted access to what data was working with my one company where the marketing department was not permitted to access the company’s own data, they had a large data platform, and for some interesting reasons, they were not allowed to, to access their own data. And so obviously, they were really hamstrung as to what they could effectively do.
And so it’s it’s regulatory, it’s legal, it’s process internally that very often locks up the data within companies. And then that is what withholds AI and machine learning from being able to provide the kind of improvements that we all want the better ROI, the more impactful campaigns, the better marketing. So
the big promise helping us make use of that data, but we have to get out of our own way and look for products and services and software that work well together as we go through that data lifecycle. As always, if you have questions, please leave them in the comments. You can submit them on the forum and the link below. Subscribe to the YouTube channel until the newsletter and I’ll talk to you soon. Take care
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