Palantir S1 (no opinion)

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Keywords: Palantir, Data Science, Analytics, Business Intelligence

Founding story

Started in 2003 and built intelligence and counter-terrorism software. Then worked with commercial enterprises

General pitch

“Our welfare and security depends on effective software. Customers come to Palantir because their technological infrastructure has failed them.“

Palantir has two business lines:

  1. Governments (US and its allies)
  2. Corporations

Pitch for using SaaS instead of custom software

The enterprise software industry’s focus on custom software tools and applications is misplaced. Those approaches often only work briefly, if at all. The problems and needs of an organization often change before the software can even be deployed.

Our partners require something more. They need generalizable platforms for modeling the world and making decisions. And that is what we have built.

Products

Two principal software platforms:

  • Palantir Gotham (“Gotham”) for government
  • Palantir Foundry (“Foundry”) for enterprise

Gotham, our first software platform, was constructed for analysts at defense and intelligence agencies. They were hunting for needles not in one, but in thousands of haystacks. And they did not have the software they needed to do their jobs. In Afghanistan and Iraq, soldiers were mapping networks of insurgents and makers of roadside bombs by hand.

Gotham enables users to identify patterns hidden deep within datasets, ranging from signals intelligence sources to reports from confidential informants, and helps U.S. and allied military personnel find what they are looking for. We later found that the challenges faced by commercial institutions when it came to working with data were fundamentally similar. Companies routinely struggle to manage let alone make sense of the data involved in large projects. Foundry was built for them. The platform transforms the ways in which organizations interact with information by creating a central operating system for their data.

Product Installation

“In particular, the time required to install our software and begin working with a customer has decreased more than five-fold since Q2 2019 to an average of 14 days in Q2 2020. In some cases, our customers can now be up and running in six hours. We have also invested heavily in developing the infrastructure used to deliver software updates to our customers. As a result, the number of upgrades our engineers can manage across installations more than doubled from an average of 20,000 per week in Q2 2019 to more than 41,000 per week in Q2 2020.”

Production Vision

Other software companies have incorrectly assumed that the future will look like the past, forming their strategies based on assumptions about a world that no longer exists. A focus on targeted analytical tools and optimizing specific functions within complex organizations is insufficient. We believe that software must connect the entire enterprise. Our most critical institutions cannot wait a year or longer for a promised application or bespoke solution to be developed. Those options are often obsolete before they are even delivered.

Industry Trends

1. Alpha vs. Beta

Most software makes companies more similar, not different. Packaged software tends to be designed to meet standardized needs. But commoditized solutions are only sufficient when keeping up with the market — that is, beta — is the goal. When it is time to generate differentiated value — that is, alpha — we believe that typical packaged software falls short. A company seeking to capitalize on its unique resources or to uncover a need unmet by its competitors requires more than software that simply conforms to well-defined best practices.

2. Buy or Build

In the search for differentiation, institutions often resort to a default approach: attempting to coordinate the construction of a custom solution themselves. They enlist the help of consultants, IT services companies, packaged and open source software, and sizable internal IT resources.

Some custom IT and software efforts reach completion. But even in these cases, finishing a project on time does not guarantee enduring value. Such projects typically entail stitching together individual solutions with custom code and forcing them to interoperate. Every new piece of the patchwork opens new avenues for failure down the road.

3. Embrace Complexity or Resist It

The largest and most complex undertakings when it comes to data integration and analysis, where the risk of failure is highest, also offer the greatest potential return. Across industries, institutions with decades of experience are competing to overcome decades of fragmented IT investments. We have repeatedly seen that institutions that use data to transform their core operations are the ones that win.

Customer Challenges

Building and deploying enterprise software are among the most significant challenges our customers face.

1. Most Data Integration and Analytical Projects Fall Short

Organizations frequently attempt to build their own data platforms before turning to buy ours. Government agencies and commercial enterprises often experiment with single-purpose tools and custom software solutions for specific workflows such as customer relationship management and financial planning.

Each new tool or application creates a new silo within an already fragmented data landscape. When it comes to making operational decisions, institutions are left to invest significant time and resources in attempting to unify their data. By the time a question is answered, the underlying data may be stale. When a new question arises, the process begins again.

2. Fixing a Legacy System Can Be as Expensive as Building One from Scratch

As organizations grow and change, their legacy technology often fails to keep up. Each pivot requires flexibility, and some systems simply lack it. Institutions often have to start over as each aging system becomes obsolete.

3. Instead of Enabling Collaboration, Many Software Security Models Preclude It Altogether

When an institution’s data systems are fragmented, deploying a security model that functions effectively can be costly, if not prohibitively expensive. In order for an enterprise data platform to effectively power an institution’s operations, security systems must be built in from the start. Security features should always follow a piece of data from its source system to final state. The workarounds — such as manually pulling data from silos and emailing it around — create room for human error and impede oversight.

Approach

We do not sell features, tools, or one-off custom applications. When it comes to working with data, those approaches generally work only briefly, if at all.

Some companies throw people at the problem. Others build dashboards. We build software platforms that become part of the institutions we serve. And we believe that every large institution in the world has a problem that our platforms are designed to address.

1. Our Software Creates a Central Operating System for Data

Our platforms, Gotham and Foundry, allow organizations to recast their siloed systems as contributors to a unified data asset. Our software enables our customers to transform massive amounts of information into knowledge that reflects their world. Data is represented not as cells in a spreadsheet, or exports from a single system, but as entities, events, relationships, consequences, and decisions in context.

2. Our Software Does Not Displace Existing Systems, It Augments Them

Flexibility and openness are core tenets of our software. By integrating their existing solutions into our central operating system, organizations can choose to maintain key historic investments without having to rebuild their entire data infrastructure. As the world changes and technology evolves, institutions can adjust their data model and integrate new systems, instead of rebuilding everything from scratch.

3. Our Approach to Security Enables Collaboration Instead of Inhibiting It

Our early days with the U.S. intelligence community informed our development approach. Security is always our first priority. We designed our software to embrace the complexity of security clearances, institutional boundaries, and varying data sensitivity levels. The same technology that allows our platforms to construct a model of the real world from individual data points allows our customers to secure each of those pieces of information. Security need not come at the expense of collaboration. Our software enables both.

Principles

First Principles

1. Make Data Actionable

Our platforms put data in context, using language that people understand. They transform data into objects that make sense to everyone in an organization. These objects bring data to life on our platforms, to the benefit of both developers and end users.

2. Integrate and Manage Systems

Our software integrates and orchestrates data systems. Data from all integrated systems is available directly to our out-of-the-box applications, APIs, and application development frameworks. Our platforms empower developers to build applications that leverage the relevant data in their source systems, without the added burden of systems integration.

3. Orchestrate, Don’t Replace

Our customers already have many of the tools they need to keep their institutions running. These tools, however, are limited to the essentials required for daily operations instead of helping our customers differentiate themselves from their competitors. Rather than replace these tools, our software serves as a substrate, binding an enterprise’s IT landscape together. By orchestrating and augmenting the operations of packaged and bespoke technologies, our software maximizes their value.

4. Generate Network Effects

Every data source that is integrated into the system, and every action taken by a developer, data scientist, or operational user, is made accessible to all other users at the institution, provided they have the necessary access permissions. Operational users adopt our platforms because they deliver the critical applications that those users need. Application developers adopt our platforms because they provide an operating environment that allows the applications those developers build to deliver immediate results for users. These dynamics produce network effects — each additional operational user, developer, system, and application makes our platforms more valuable to every other user, developer, system, and applicatio

Consequences of First Principles

1. Create a Trustworthy Operational Foundation for Data

Know exactly what the data sources is and how reliable it is. Also make the data sources auditable, as much as possible

2. Make data intelligible

People do their best work when they can reason in concepts familiar to them. Built infrastructure to unite nontechnical users with technical users so that both can wield data effectively

3. Enabling operational change through data-driven decisions

On the software, people can easily collaborate to make decisions. Then, they can model these decisions into the software to analyse then, creating an operational feedback loop

4. Accelerating customer outcomes by rapidly delivering and updating platforms across environments

Make it really easy to update your software — even for airgapped users

Market Opportunity

Estimated TAM for Palantir products is $119B/year

Commercial

Estimated TAM is $56B. Potential customers are around 6,000 companies with more than $500M in revenue

Government

Estimated TAM is $63B

Competitive Strengths

Personal note: None of these are differentiated strengths - except 5 and 6. Real strength is that they are really embedded into the organizations that they already have, and the lock-in is difficult to get out off

  1. Build privacy controls into platforms from the start
  2. Have engineers on the front-line (Forward Deployed Engineers)
  3. Software brings government grade security to industry, and a breadth of private sector experience to government
  4. Platforms deliver multi-tenant cloud economics even for air-gapped customers
  5. Business is built to expand within organizations and across sectors
  6. Strategic relationships last for years

Growth Strategies

  1. Continued expansion into commercial sector
  2. Increasing reach within existing customers
  3. Become default OS for data cross the US government
  4. Expand reach with US allies abroad
  5. Pursue new methods of customers acquisition with partnerships
  6. Become the industry default
  7. Continue to grow the direct sales force

Risk Factors Summary

  • Sales efforts involve considerable time and expense and sales cycle is long and unpredictable
  • Business is dependent on continuing to maintain good relationships with the largest customers, an on continued upselling
  • Key business metrics can fluctuate significantly on a quarterly basis – making it difficult to predict future results and plan
  • Have complex platforms and have lengthy implementation processes
  • Negative press coverage (whether or not rooted in reality) can have negative business ramifications
  • A hack into their systems or any of the suppliers they rely on can cause significant problems for them