Heidi asks, “Are companies challenged in their adoption of AI by siloed data?”
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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.
Christopher Penn 0:13
In today’s episode, Heidi asks, are companies challenged in their adoption of AI by siloed data? I would say that it is certainly a contributing problem.
But I think siloed data is a problem period, right? Regardless of whether we’re talking about AI or not, if we think about artificial intelligence really is just like a fancy kitchen appliance.
And it does really cool stuff.
Think about what siloed data would look like, suppose you were trying to cook in a kitchen, and there were some ingredients you had.
And there are some ingredients that were in another part of the kitchen and you weren’t allowed to go in that part of the kitchen, right? Like, the the stuff in the refrigerator, you’re not allowed to go in there, you can’t go in the refrigerator.
Like my supposed to make bread if I can’t get it, you know, the eggs or the milk or the yeast or things like that, I’ve got flour here, and then you start getting protective? Well, if I can’t go in the frigerator, you can’t come in my pantry.
And you know, you can’t have flour, sugar or salt.
Obviously, it’s going to be a really rough day in your, in your kitchen.
If you start having these silos, these organizational divisions that prohibit people from sharing ingredients, data is an ingredient.
And again, AI is just a fancy appliance, which means that if you have those silos, you’ve got problems that AI won’t solve, right? If you have, it doesn’t matter how fancy your new blender is, if you’ve got if you’re not allowed to go in the refrigerator, there’s a lot of things you can’t cook period, blender or no.
And so from a, a structural perspective, companies need to get rid of silos as much as possible within practical and regulatory limits, if they want to be able to make use of the data for anything, not just for use with AI, but for anything at all.
If you’re building a data warehouse, or a data lake, if you’re doing even basic, you know, pre machine learning stuff, if you’re just doing basic statistics, exploratory data analysis, you need to have access to the data to do that.
And if there are silos, it’s going to be it’s going to be a rough time, there were there will be things like you know, if you’re not allowed in, in the, in the freezer section of your of your houses, kitchen, there’s gonna be some things you’re not allowed to do that.
And that you will need, you will need access to.
So here’s where artificial intelligence can help.
Sometimes if if we make an AI project, fancy enough and flashy enough, you know, it’s the shiny new object in the room.
Sometimes that can help break down organizational resistance.
If AI is a strategic priority your company, you can go to somebody and say, oh, yeah, I know, you know, normally, we’re not going to access your, your sales data, or whatever.
But for this project, we’d like to make an exception.
And depending on the benefit to that division of the company, depending on the visibility at an executive or stakeholder level, sometimes you can use AI as an excuse to dig into those other silos of data and get stuff out of them.
This happens a lot.
We’ve had this happen a lot with analytics projects, big analytics projects, where ironically, as a consulting firm Trust Insights would have access to more of the company’s data than any individual one department did.
Because we were an outside neutral third party.
And so we’re just like, oh, yeah, we’re just gonna use this data for this project.
Meanwhile, we had better visibility into the entirety of of what was happening at a company and be able to share back with those divisions, hey, here’s what else is going on at the company.
It’s kind of like, kind of like being the data and AI equivalent of a bartender right? Everybody comes to the bartender and confesses their their woes individually.
And you as the bartender, you have, you know, hear everybody’s stories and go, yeah, and you’re thinking, Oh, Ralph here has got the exact same problems as Bob over there.
And she was always there, she’s got her problems and stuff.
And you all don’t know that you each have the solutions to each other’s problems.
Because you don’t talk to each other, you just talk to the bartender.
So AI can be used as an excuse to get into other silos.
And then ideally, what you do is you show benefit to sharing data that goes above and beyond the scope of the AI project itself.
So that it persuades those those silos those departments like hey, if you share your data, things will be a lot easier for both groups, both groups will benefit.
The worst case I’ve ever seen of this was just blew my mind.
We had A B2B tech company a few years back as a client, and we were called in to build a model of their marketing data,
Christopher Penn 5:10
combining marketing and sales data to help them essentially figure out which channels mattered the most.
When we got in there, we were told, here’s the marketing data, for regulatory reasons, some data that we can’t get about our own company that we can’t like the market department, and we can’t get it.
So not that we won’t share it with you, we can’t get a hold of it, can you see if you can get it from the outside, we were able to do that.
And then we asked for the sales data so that we could calibrate the marketing data with the sales data to say, Okay, if you have all these things, and this is the outcome you’re after, and the VP of sales is like, Nope, can’t have that data.
We’re like, why not? Because marketing is not allowed to see sales data.
Like, how do you get anything done? Then like, how do you communicate to marketing? Hey, you know, these programs are driving leads are not driving leads, and like we don’t know, like, so.
You just wing it? And except whatever leads marketing sends you and it’s like, no, no, we don’t we don’t do that.
Whatever marketing sends is incremental, our sales guys all just cold call everybody all day.
Like, I feel like, I feel like that might not be the best way to do things.
It turns out, this came out.
After our engagement, that sales was so poor at its job, their closing rate was so bad, that they didn’t want anybody to know just how bad things were internally, there’s their sales closing rate for, you know, good sized commercial enterprise deals was something like about 1% of every out of every 100 opportunities that were teed up, there’s the sales tour and closed one of them.
And so there was a lot of obviously house cleaning and personnel changes and things.
We didn’t have anything to do with it, because we were long gone to that point.
But I remember reading in the news about this company, because it’s pretty well known company that they had run into some revenue issues.
And I’ve had a few quarters.
And I’m like, Huh, I wonder that is because y’all are flying blind and have no idea what you know, the left hand has no idea what the right hand is doing.
So there are definitely challenges posed by siloed data AI is no different than any other function or any other technique used to turn data into insights.
It is hampered more by missing data.
But if a company’s got siloed data and rigorous boundaries between departments, it’s got problems already, right.
And AI will not solve those problems.
It will just make those problems.
bigger and faster.
That’s what AI does makes things go faster and bigger.
And you know, if you have solutions that will make your solutions faster and big, if it makes you have problems it will highlight and make your problems faster and bigger too.
So really good question.
Thanks for asking.
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