Today’s question comes from nearly everyone who’s interested in doing business with Trust Insights, my company:
“How do we know we’re ready for data science/AI/machine learning?”
The answers aren’t technical as much as they are attitudinal. Watch this video to learn who’s ready and who’s not.
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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, a question asked by a number of people actually, typically as part of the input process for when they’re looking at doing the data science project with my company trust insights. The question is, how do you know that you’re ready for data science and machine learning or artificial intelligence? And there are a number of answers to this
to start there are with classical measures of readiness. Certainly, on the vendor side, IBM pioneered the what’s called the band framework back in the 60s, which is, you know, budget authority need timeframe Do you have budget, you have the authority to make decisions to have a defined needing you have it define the timeframe, but that’s more for like specific projects, though, the neat part is important is Do you have an actual need to have a problem that you need to solve the bigger issues? The bigger questions to answer is our one Do you have the the the data infrastructure for such a project? Do you have a strategic outcome that you want? And most importantly, are two attributes, I think, to personality attributes of you, and have your executive team and all that stuff.
Number one, are you curious? Do you want to know the answer? In a lot of companies? There are people who are in curious, you’re like, just Just get me something that tells me this answer.
Some of the worst abuses I’ve seen to this are in market research, where someone will commission a market research from say,
Get me a survey, get me findings that reflect the answer that I want. That is the height of in curiosity, it is unethical. In cases it may be illegal, and it is clear that attitude is one in which you’re not ready for data science. You’re not ready for machine learning, you’re not ready for artificial intelligence, because what you will create will at best, be garbage worst be actively dangerous to your company. If you have an answer that you want to get like, I want I want the I want you to tell me that give me 8% growth for the next year, five years. It’s not how that works.
So being curious, being legitimately curious, I want to know the answer to this was, what will the growth be over the next five years?
What could we do to be more profitable? What would deliver a better health outcome? All these questions that are open ended that say, I would like to know the answer to this curiosity is so important.
And the second
attribute of a company that is ready for data science ready for machine learning, ready for artificial intelligence? is
you have to be comfortable with two kinds of answers. I don’t know. And an answer that you don’t like. The second one and answer you don’t like is also a common thing that market research terms. Look in foreign clients. My friend Tom Webster Edison research says this is you have to be comfortable with an answer. That
wasn’t what you wanted,
right? So you may commissioners today, but you may
like yeah, I would like this to say x, you know, you don’t insist on but it like to say, and then it comes back saying completely the opposite. You’re like,
And so being able to be comfortable with that an answer. You don’t like my friend and handling calls and being comfortable with being uncomfortable, you’re out, you get an uncomfortable answer back and you’re like, Okay, how can we interpret this? Or how can we make use of this and still get value out of this answer? The other answer that’s an important one is I don’t know, you may get back and answer that may be the equivalent of I don’t know, or there may not be there there.
Say you commission a, an attribution analysis of your marketing. And what comes back is it’s not clear it’s not clear what the answer is.
And there can be a number of reasons for that dirty data, incomplete data, biased data, there may be, but there may be something in the ingredients that is wrong enough that any answer you get is going to be statistically insignificant or statistically wrong. And thus,
the answer will be I don’t know, I don’t know what the answer is. Your question is because things went wrong in the precursors that that essentially created a flawed answer. An ethical data science firm and ethical machine learning from an ethical artificial
intelligence firm or a practitioner, if you’re working with a person
be up front, you’re going to get an answer you don’t like sometimes that’s going to happen, you’re going to get an answer. It’s unclear, you’re going to get no answer at all, sometimes, or you’re going to get an answer like, well, I could have guessed that Well, yeah, of course, you could have guessed that it’s your data, you should have some sense of what’s in your data already. You may not be statistically significant, it may not be correct, but you have a general feeling because you know, your business probably better than any practitioner unless they’ve worked at your company for a number of years, you know, your business and, you know, the human side of the business, the the non quantifiable part of the business and so answers like, I don’t know, or I already knew that or
this is the answer that you weren’t expecting. You’ve got to be comfortable with being uncomfortable with those kinds of answers.
those are the meta attributes, the bigger picture attributes that you look for in yourself, in your company, in your colleagues and in your vendors to identify whether or not you are ready for a Data Science Initiative some kind
as always, if you have comments or questions, please leave them below and subscribe to the YouTube channel the newsletter and I’ll talk to you soon. Take care what help solving your company’s data analytics and digital marketing. This is trust insights.ai today and let us know how we can help you
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