Optima Partners’ Post

Name: Credit Shift with Danilo Blagojevic and Javier Campos | Optima Partners posted on the topic | LinkedIn
Uploaded: 2024-03-01T11:21:41.940Z
Duration: 2 min 25 s
Channel: Optima Partners

Optima Partners

1,771 followers

8mo

Our Director of Decision Sciences and Machine Learning, Danilo Blagojevic PhD, and Webio CSO, Paul Sweeney, recently had the pleasure of hosting Javier Campos of FENESTRA, on the latest episode of the Credit Shift podcast! Dan, Paul and Javier draw from their wealth of experience and share their thoughts on the importance of aligning AI initiatives with business goals. This is an episode you do not want to miss, as it explores topics such as: 💬 Leveraging AI to Refine and Enhance Your Dataset 💬 Understanding How Your Data Aligns with Your Business Goals 💬 AI Regulations …and much more. Get a sneak peek with a short clip below and catch the full podcast here: https://lnkd.in/djxMcRyU #AI #BusinessGoals #DataScience #AIRegulations #DecisionSciences #MachineLearning

Transcript

Don't focus on this is company to that on the data you have now. Focus on the word you want the AI to do what you want to achieve and then see whether you have the right data or not and then clean it or clean it or bring it and and that that notion of, I mean it feels like sometimes you can stumble on a data source or then it proves to be really, really helpful. But in the absence of stumbling on data, you how how do we? How do businesses know where that missing data might come from? Any any ideas on? Yeah. Where? Where there's exogenous factors, where? Where businesses should be looking for them for me, I always say. Really, really upset about the business value. You know, the data is is something secondary which you know, I know. And again, I'm going to be a big controversial here. So we can, we can have a bit of discussion. You know, some people are saying no, no, the first thing you have to do is absolutely forget about. I do spend a lot of money cleaning your data just in general. So for me, I would say, well I wouldn't do that. Why? Because not all data has value and you can spend a lot of money on doing things with my money on, it is much better to say. You know once you understand how AI works, what what is, you know, the centralizing human decisions, what are the things you cannot at the moment, the world like that and you cannot value once you know what you want to do, which is at the point is understanding the what the business value you know, what do you can you for your customers that you don't do today. What Once you know that, go there, see what data you have and then. You might need to go and explore. Or there. So for example you might look at the open data, you know I talk about it in the book, you might get a third party vendor, you might change your processes your you start collecting all the data but the but it should be that way it's start from the business. You know if I do this in business value so if I am able to help my customer in this way, I can reach my customer one week before he's about to change you know all these visit then you quantify how much value would be for me. And then worry about, OK, do I have the data, is the data good? And then if you have the data but the data is not good, that's when you spend money and say now I'm going to clean it, I'm going to do master data, but now I have a very clear objective with a clear value and and then you do it like.

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ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML) IN DECISION-MAKING
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The folks at Optima Partners are going deep into all corners of AI to ensure that our clients face into the critical and core foundations - in this case data so that experimentation can happen with purpose and can then be scaled. It's only #coolshit if you can drive scalable value and you won't be doing that unless you get the core foundations in place.

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My interview on AI.

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ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML) IN DECISION-MAKING

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