5 ms·

Can anyone please give a non-vague answer to what Palantir does?

by nthitz 11y ago

BinaryIdiot 11y ago
As someone who has worked with Palantir and used their products in various, indirect and proxy contexts it gets confusing as to what you can talk about and what you can't depending on god knows what 15 papers you signed to be on the contract which has access to Palantir. I suspect this is a big part of why people don't talk about it much and why their website is so incredibly vague and, honestly, really terrible at explaining what they do. I found this Quora question and answer which ultimately doesn't really describe what I have interfaced with before https://www.quora.com/What-specifically-does-Palantir-do https://www.quora.com/What-specifically-does-Palantir-do. Perhaps I simply didn't interact with that part of the product. Sorry for the semi vague and terrible answer. Briefly looking at some of these videos may be of help as it shows Palantir and other software doing some sort of entity extraction from documents https://www.youtube.com/watch?v=3NAVSyfjcDo https://www.youtube.com/watch?v=3NAVSyfjcDo
- nimrody 11y ago
  Thanks for the video link. This video: https://www.youtube.com/watch?v=dVsx4I8gkKk https://www.youtube.com/watch?v=dVsx4I8gkKk is very impressive.
hendzen 11y ago
Palantir has a core team of engineers building out a very general data analysis framework. Their core product consists of a GUI for manipulating and sharing annotated graphs (i.e. edges and vertices). The actual edges and vertices of the graph can have complex types defined by some arbitrarily detailed ontology. The GUI also includes tools for producing visualizations and charts from numerical and textual attributes of the edges/vertices. The core product is very powerful when configured properly and it is used to solve important problems in both government and industry. Palantir also has a much larger corps of consulting engineers (FDEs in Palantir parlance) who actually build the ontologies, import a customer's data in to Palantir, and then provide ongoing support to users. A huge portion of this work is writing scripts to munge the data from arbitrary formats (SQL tables, CSV, Excel, etc) to the format supported by Palantir. A core reason for Palantir's success is that they convince large numbers of top engineering students from MIT, Stanford, Berkeley, Harvard, etc, to do this relatively unglamorous work for below-market pay. Due to the imbalance in headcount between core engineering and the consulting division, Palantir, in practice, operates more similarly to Accenture or McKinsey than to Google or Facebook. However, for some unknown reason, their valuation seems to be calculated like that of a high margin software company, rather than like that of a low-margin services business. In particular, to increase revenue by N dollars, Palantir needs to hire O(N) employees.
- dkarapetyan 11y ago
  Yup, correct on all counts.
- serge2k 11y ago
  > A core reason for Palantir's success is that they convince large numbers of top engineering students from MIT, Stanford, Berkeley, Harvard, etc, to do this relatively unglamorous work for below-market pay. how?
  - lordlarm 11y ago
    https://www.palantir.com/life-at-palantir/ https://www.palantir.com/life-at-palantir/ should give you a hint. They're doing Google style benefits.
    - eru 11y ago
      But Google also gives Google style benefits and better than below market salaries. (I guess, there's no mystery: some people apply at Palantir. Even more apply at Google.)
  - eva1984 11y ago
    Maybe it is the mysteriousness that they succeeded to build along the years. As a company it feels far-fetch, likes to hire young, smart yet egotistical engineers and the bar is relatively high.
  - cmdkeen 11y ago
    In part seemingly by sending them off around the world. I know of people permanently based in Dubai for instance - below market pay but no tax and a very expat lifestyle is appealing to many twenty-somethings. Plus the Accenture/McKinsey model is pretty famous in terms of the jobs people go on to afterwards, lots of people who don't really know what to do but are smart go into them with no expectation of doing more than a few years. I don't know if they've also got that concept going as well though.
  - kqr 11y ago
crdb 11y ago
TL;DR: it's the McKinsey of this century. From my relatively limited experience in the space: most companies have trouble actually doing anything with their data beyond slicing it a couple of ways [0] and making bad graphs with it; these same companies also have a lot of trouble hiring and retaining [1] people capable of doing it (I know because they end up my clients). Palantir will offer large amounts of money (despite accusations of below market pay; although a good portion is in equity) and a half-decent lifestyle-wise, not too corporate environment to young smart engineers to go and help them outsource that function for the kind of companies that do not care about paying large fees to "get the best", and for whom the consultant status helps cut through the red tape. The "products" are, from my limited understanding, more like a loose collection of accumulated knowledge from clients which can help future deployments not reinvent the wheel (particularly since so many of the engineers are young and inexperienced). This is also a selling point. "We get it right because we did it before with all these prestigious clients". Each client has its own infrastructure and schema, and so most data related projects are naturally oriented towards a lot of manual effort - talking to technical staff, reading through code, understanding the data customers and feedback loops... and whilst many companies in the space pretend to offer "plug and play" BI (or data science, or whatever) in practice without a strong data team behind it, these end up failing pretty badly. So I see Palantir's competitive edge as its staff, not its products, and I don't think much (as a data professional) of pure product data companies. In many ways, Palantir is what McKinsey was half a century ago: it offers job variety and a pick of the best clients to the "top ranked" fresh grads and early professionals; it offers these "guaranteed fresh" minds to clients who have trouble attracting them in-house to solve difficult problems that can't in theory be solved by the internal talent pool; it brands itself as the "top" company globally doing this. If it is successful, it will just keep growing its competitive advantage ("you don't get fired for hiring IBM"). And I think their focus on people instead of software is a great idea in an age of exceptional open source products and ever cheaper hardware. Where might its cash flow come from? I see two sources: a. as you get further into a data project, you've built the infrastructure and it is comparatively easy to provide answers. For example, once I'm done with a data warehouse, I can produce a complex report or query answering just about any question management can throw at me in 10 minutes to 2 hours; this will take them two weeks to use. So the time actually spent working, vs the monthly retainer, just keeps going down with time (and of course experience - if you wrote the query for another client you can just change the variable names and output). b. the value add from getting the data right is extremely high. Cf [0] below. For many modern companies it can be the difference between a 50% increase in profits and not being a going concern the following year. As CEOs realize this, and realize that no, you don't get "80% of the quality for 20% of the price" by going for the lowest bidders, prices track up and stay there. [0] Most classically trained managers that I've met are unable to cope with more than 2 variables at the same time. A graph is basically a 1D or 2D model with your brain and past experience being the curve fitting. This becomes an issue if the business model fits 20 or 100 dimensions better (e.g. many trading strategies and even market making) and most of your competitors are very comfortable with it. As an example, I've witnessed two e-commerce companies in the same country with a 10x difference in conversion rate; one had statisticians to manage its marketing spend, the other preferred going by the traditional way of "if its conversion rate is high I'm going to add more budget". [1] I know an exceptional data scientist who changed jobs 4 times in the last year... each workplace bidding him up!
deleted 11y ago
[deleted]
bane 11y ago
They build two different, non-overlapping products (or at least they did when I last looked into them about 3 years ago). 1) A collaborative semantic graph tool [1] -- this is the thing they're best known for. The client tool is a unified interface for both adding new information to the semantic graph as well as searching/exploring what's already in the graph. It does this in a way that scales to a fairly large number of simultaneous users and allows organizations to build and explore collective "knowledge". The input focus is on manually selecting and "tagging" semantic elements in free-text, aligning those with the system's ontology framework and even connecting together different semantic elements with certain constrained relationship types. [2] Over time this aggregates into a hihgly curated knowledge-base that can be mined more efficiently than reading lots and lots of reports and news articles. They support a few other odds and ends (maps, various statistical tools on the semantic graph, different search methods). There's plenty of other vendors that do parts of this, but Palantir brought excellent user interface design and better collaboration tools to the party. These days, their core semantic graph tech could probably be replicated by half a dozen engineers using HTML5 technology and some sort of scalable hadoop-like back-end in a few months. It's not that interesting of a technology. On a personal level, you could do...~70% of what they do with something like yED or Visio. Protege [3] would probably get you another 10% of the way there. A good google docs-style collaboration another 10% and so on. There's nothing uniquely revolutionary about the tech other than they brought it together in the right way. My impression is that most of the money they make is on integrating customer data sources into the tool so users have more things to markup and add to the knowledge base. Enterprise integration is a very lucrative and time-consuming job and a competitor's quick rewrite (like I mention above) won't be able to compete against Palantir's entrenched relationship and large marketing teams. This is where most of their engineering resources are going at the moment. 2) A "big data" statistical exploration tool meant for finance. [4] I don't know much more about it other than they designed a new language for it and if connected up to the right kinds of data sources looked really powerful in the videos I saw of it in action. They have lots of open positions all over the world, which makes me think they have lots of offices all over the world, which means they do lots of sales and marketing globally, penetrating new markets. My guess is that the huge numbers of enormous funding rounds go into marketing pushes into different verticals and locations. They used to have a demo on their website you could play with, but they've taken it down. The tool was nice looking for 2010-ish, but it had a Java JRE requirement that was pretty old and wouldn't run on newer runtimes. 1 - https://www.youtube.com/watch?v=f86VKjFSMJE https://www.youtube.com/watch?v=f86VKjFSMJE 2 - https://www.youtube.com/watch?v=l-UrRJMDGWc https://www.youtube.com/watch?v=l-UrRJMDGWc 3 - http://protege.stanford.edu/ http://protege.stanford.edu/ 4 - https://www.youtube.com/watch?v=U8YMPS_DZPk https://www.youtube.com/watch?v=U8YMPS_DZPk

Can anyone please give a non-vague answer to what Palantir does?

by nthitz 11y ago

Can anyone please give a non-vague answer to what Palantir does?

As someone who has worked with Palantir and used their products in various, indirect and proxy contexts it gets confusing as to what you can talk about and what you can't depending on god knows what 15 papers you signed to be on the contract which has access to Palantir. I suspect this is a big part of why people don't talk about it much and why their website is so incredibly vague and, honestly, really terrible at explaining what they do. I found this Quora question and answer which ultimately doesn't really describe what I have interfaced with before https://www.quora.com/What-specifically-does-Palantir-do https://www.quora.com/What-specifically-does-Palantir-do. Perhaps I simply didn't interact with that part of the product. Sorry for the semi vague and terrible answer. Briefly looking at some of these videos may be of help as it shows Palantir and other software doing some sort of entity extraction from documents https://www.youtube.com/watch?v=3NAVSyfjcDo https://www.youtube.com/watch?v=3NAVSyfjcDo

nimrody 11y ago
Thanks for the video link. This video: https://www.youtube.com/watch?v=dVsx4I8gkKk https://www.youtube.com/watch?v=dVsx4I8gkKk is very impressive.

hendzen 11y ago

Palantir has a core team of engineers building out a very general data analysis framework. Their core product consists of a GUI for manipulating and sharing annotated graphs (i.e. edges and vertices). The actual edges and vertices of the graph can have complex types defined by some arbitrarily detailed ontology. The GUI also includes tools for producing visualizations and charts from numerical and textual attributes of the edges/vertices. The core product is very powerful when configured properly and it is used to solve important problems in both government and industry. Palantir also has a much larger corps of consulting engineers (FDEs in Palantir parlance) who actually build the ontologies, import a customer's data in to Palantir, and then provide ongoing support to users. A huge portion of this work is writing scripts to munge the data from arbitrary formats (SQL tables, CSV, Excel, etc) to the format supported by Palantir. A core reason for Palantir's success is that they convince large numbers of top engineering students from MIT, Stanford, Berkeley, Harvard, etc, to do this relatively unglamorous work for below-market pay. Due to the imbalance in headcount between core engineering and the consulting division, Palantir, in practice, operates more similarly to Accenture or McKinsey than to Google or Facebook. However, for some unknown reason, their valuation seems to be calculated like that of a high margin software company, rather than like that of a low-margin services business. In particular, to increase revenue by N dollars, Palantir needs to hire O(N) employees.

dkarapetyan 11y ago
Yup, correct on all counts.
serge2k 11y ago
> A core reason for Palantir's success is that they convince large numbers of top engineering students from MIT, Stanford, Berkeley, Harvard, etc, to do this relatively unglamorous work for below-market pay. how?
- lordlarm 11y ago
  https://www.palantir.com/life-at-palantir/ https://www.palantir.com/life-at-palantir/ should give you a hint. They're doing Google style benefits.
  - eru 11y ago
    But Google also gives Google style benefits and better than below market salaries. (I guess, there's no mystery: some people apply at Palantir. Even more apply at Google.)
- eva1984 11y ago
  Maybe it is the mysteriousness that they succeeded to build along the years. As a company it feels far-fetch, likes to hire young, smart yet egotistical engineers and the bar is relatively high.
- cmdkeen 11y ago
  In part seemingly by sending them off around the world. I know of people permanently based in Dubai for instance - below market pay but no tax and a very expat lifestyle is appealing to many twenty-somethings. Plus the Accenture/McKinsey model is pretty famous in terms of the jobs people go on to afterwards, lots of people who don't really know what to do but are smart go into them with no expectation of doing more than a few years. I don't know if they've also got that concept going as well though.
- kqr 11y ago

crdb 11y ago

TL;DR: it's the McKinsey of this century. From my relatively limited experience in the space: most companies have trouble actually doing anything with their data beyond slicing it a couple of ways [0] and making bad graphs with it; these same companies also have a lot of trouble hiring and retaining [1] people capable of doing it (I know because they end up my clients). Palantir will offer large amounts of money (despite accusations of below market pay; although a good portion is in equity) and a half-decent lifestyle-wise, not too corporate environment to young smart engineers to go and help them outsource that function for the kind of companies that do not care about paying large fees to "get the best", and for whom the consultant status helps cut through the red tape. The "products" are, from my limited understanding, more like a loose collection of accumulated knowledge from clients which can help future deployments not reinvent the wheel (particularly since so many of the engineers are young and inexperienced). This is also a selling point. "We get it right because we did it before with all these prestigious clients". Each client has its own infrastructure and schema, and so most data related projects are naturally oriented towards a lot of manual effort - talking to technical staff, reading through code, understanding the data customers and feedback loops... and whilst many companies in the space pretend to offer "plug and play" BI (or data science, or whatever) in practice without a strong data team behind it, these end up failing pretty badly. So I see Palantir's competitive edge as its staff, not its products, and I don't think much (as a data professional) of pure product data companies. In many ways, Palantir is what McKinsey was half a century ago: it offers job variety and a pick of the best clients to the "top ranked" fresh grads and early professionals; it offers these "guaranteed fresh" minds to clients who have trouble attracting them in-house to solve difficult problems that can't in theory be solved by the internal talent pool; it brands itself as the "top" company globally doing this. If it is successful, it will just keep growing its competitive advantage ("you don't get fired for hiring IBM"). And I think their focus on people instead of software is a great idea in an age of exceptional open source products and ever cheaper hardware. Where might its cash flow come from? I see two sources: a. as you get further into a data project, you've built the infrastructure and it is comparatively easy to provide answers. For example, once I'm done with a data warehouse, I can produce a complex report or query answering just about any question management can throw at me in 10 minutes to 2 hours; this will take them two weeks to use. So the time actually spent working, vs the monthly retainer, just keeps going down with time (and of course experience - if you wrote the query for another client you can just change the variable names and output). b. the value add from getting the data right is extremely high. Cf [0] below. For many modern companies it can be the difference between a 50% increase in profits and not being a going concern the following year. As CEOs realize this, and realize that no, you don't get "80% of the quality for 20% of the price" by going for the lowest bidders, prices track up and stay there. [0] Most classically trained managers that I've met are unable to cope with more than 2 variables at the same time. A graph is basically a 1D or 2D model with your brain and past experience being the curve fitting. This becomes an issue if the business model fits 20 or 100 dimensions better (e.g. many trading strategies and even market making) and most of your competitors are very comfortable with it. As an example, I've witnessed two e-commerce companies in the same country with a 10x difference in conversion rate; one had statisticians to manage its marketing spend, the other preferred going by the traditional way of "if its conversion rate is high I'm going to add more budget". [1] I know an exceptional data scientist who changed jobs 4 times in the last year... each workplace bidding him up!

deleted 11y ago

[deleted]

bane 11y ago

They build two different, non-overlapping products (or at least they did when I last looked into them about 3 years ago). 1) A collaborative semantic graph tool [1] -- this is the thing they're best known for. The client tool is a unified interface for both adding new information to the semantic graph as well as searching/exploring what's already in the graph. It does this in a way that scales to a fairly large number of simultaneous users and allows organizations to build and explore collective "knowledge". The input focus is on manually selecting and "tagging" semantic elements in free-text, aligning those with the system's ontology framework and even connecting together different semantic elements with certain constrained relationship types. [2] Over time this aggregates into a hihgly curated knowledge-base that can be mined more efficiently than reading lots and lots of reports and news articles. They support a few other odds and ends (maps, various statistical tools on the semantic graph, different search methods). There's plenty of other vendors that do parts of this, but Palantir brought excellent user interface design and better collaboration tools to the party. These days, their core semantic graph tech could probably be replicated by half a dozen engineers using HTML5 technology and some sort of scalable hadoop-like back-end in a few months. It's not that interesting of a technology. On a personal level, you could do...~70% of what they do with something like yED or Visio. Protege [3] would probably get you another 10% of the way there. A good google docs-style collaboration another 10% and so on. There's nothing uniquely revolutionary about the tech other than they brought it together in the right way. My impression is that most of the money they make is on integrating customer data sources into the tool so users have more things to markup and add to the knowledge base. Enterprise integration is a very lucrative and time-consuming job and a competitor's quick rewrite (like I mention above) won't be able to compete against Palantir's entrenched relationship and large marketing teams. This is where most of their engineering resources are going at the moment. 2) A "big data" statistical exploration tool meant for finance. [4] I don't know much more about it other than they designed a new language for it and if connected up to the right kinds of data sources looked really powerful in the videos I saw of it in action. They have lots of open positions all over the world, which makes me think they have lots of offices all over the world, which means they do lots of sales and marketing globally, penetrating new markets. My guess is that the huge numbers of enormous funding rounds go into marketing pushes into different verticals and locations. They used to have a demo on their website you could play with, but they've taken it down. The tool was nice looking for 2010-ish, but it had a Java JRE requirement that was pretty old and wouldn't run on newer runtimes. 1 - https://www.youtube.com/watch?v=f86VKjFSMJE https://www.youtube.com/watch?v=f86VKjFSMJE 2 - https://www.youtube.com/watch?v=l-UrRJMDGWc https://www.youtube.com/watch?v=l-UrRJMDGWc 3 - http://protege.stanford.edu/ http://protege.stanford.edu/ 4 - https://www.youtube.com/watch?v=U8YMPS_DZPk https://www.youtube.com/watch?v=U8YMPS_DZPk