Meet The Former IBM Employee Who Wanted to Grab Palantir Shares in 2008.

Sean Troy is no stranger to Palantir. As a former IBM employee and someone who actively works in the data analytics space, he feels Palantir is a special company.

Below is an except from an interview that anyone interested in Palantir MUST listen to. Read his thoughts below when asked why he is excited for Palantir – the full interview is here. You can follow Sean on Twitter and YouTube.

Amit: So why are you excited for Palantir?

Sean: “So I'm gonna I'm gonna soften the blow a bit on that I think that there never has to be one winner. I think there could be a couple of winners who have differentiated value and will oftentimes work together a layer on top of each other my experience working especially with the biggest companies out there is standardization and then there's also some other players for key areas.

I think what's exciting about the way palantir is approaching this is they've been very thoughtful about how they want to go to market here they wanted to be the dominant player in the military and government sector right we know that this this is like the thing that actually stands out to me the most about palliative that gets me excited is apollo.

I think a lot of people get excited about the visualization layers and the operational layers and how they're going to bring it to commercial and military etc but the biggest challenge in 2022 is almost all enterprises out there government private sector whatever they have disparate data that lives on legacy old systems.

They've got it on brand new systems like aws as your etc and they need it to all basically be

brought together in one place so if you think about the way architecture works in and around analytics and ai right.

We've got your company's different data sources right do we need a connectivity layer do we need an operational layer.

Then we need to sort of like deploy those things out every company's got that data layer you can then treat apollo as a standalone singular company almost that acts as that connectivity layer and that's the piece for me that is the absolute biggest challenge

hands down the commercial space and if you're solving for that.

That's where i start to get pumped up because the ability to connect to public and private clouds gov level clouds heavily governed spaces as well as excel sheets and data warehouses that live on a computer that layer gives you the ability to then really scale at speed on you know foundry is that next level for enterprise for example so when i think about this and I approach it with customers you know this is just my perspective i don't work for them.

Let's really embrace all of your data no matter where it lives and then you know what executives oftentimes say is well it's bad data in 20 years I'm yet to ever see good data i'd love to know what it looks like because it doesn't exist because every day some data becomes stale or outdated or someone enters a name wrong or a revenue number wrong or there's a sensor that goes bad so there's always bad data.

But the question is how fast can we prepare that data and look and find those anomalies that live within it and by having a strong ai tool to recognize in workflow in the flow of what it's doing in flag here's a problem there's a problem this doesn't fit what we can then start to think about is performing ai at a data level and we haven't really gotten to this yet.

This is the cool stuff that absolutely no one talks about because they don't see it right um

at the data level we're connecting and we're merging and we're joining these things it's a big process it doesn't have to be hard but it is a big process.

What we're starting to see a lot of companies do that's really cool is they're applying ai machine learning excuse me at the data flow level so things like real-time sentiment analysis of survey comments as they come in and metric metrics metricing those against other kpis we're looking at the ability to time series forecasting or missing values because something came over from accounting or finance or an erp system which is like a transactional system right.

It doesn't have all the values in it right so we can start to predict and prescribe what those values should be to help us still get to that end point and that ability to start to apply

ai at a data level at an operational level at an output level this is where that intelligent blanket

starts to come in and all points are operating off of the same vision of the same customer right because at the end of the day what we're really working towards is this complete 360 of our customer

Whether it's the government or a plane or a net new sale for your company right like we want that 360 to understand everything we can know about that customer from how they buy to how they consume to how they would get retained and that's what this whole piece of it really comes down to for me is that platform approach."

Continue the full interview here - with timestamps in the description.