Palantir Technologies Research Report

Continues to Ride the Big Data Tailwind

We initiated coverage on Palantir on October 13, 2014. Since then, a number of developments have taken place, both company specific and industry related. This update captures the incremental information since our initiation and provides an updated valuation analysis. We reiterate our positive thesis on Palantir on the back of our belief that the company is well positioned in the sweet spot of spending.


Our research process involves proprietary channel checks with users, competitors, and industry experts, and synthesis of publicly available information from the company and other reliable sources.


  • Well Positioned in the Sweet Spot of Spending. Palantir has maintained its competitive position as one of the leading Big Data/Smart Data software vendors. The company’s data analytics platform analyzes multiple forms of data including structured, unstructured, relational, temporal, and geospatial data. Through its flagship products – Palantir Gotham and Palantir Metropolis – the company helps to integrate, manage, secure and analyze all forms of data in multiple verticals.
  • Data Analytics Market is Large and Growing. Industry projections for the market for Big Data software and analytics is estimated at $98.6B for 2019 in the US and $187B worldwide, growing by over 50% from 2015, according to market data from IDC. Consensus growth estimates for Palantir’s public market peers, which include Hortonworks, Tableau Software and Splunk, are in the 30-50% range, underscoring the robust demand and growth outlook for Big Data analytical tools and services.
  • Shift to Commercial from Government. At the time of the initiation, roughly 40% of the company’s sales were generated from government contracts and the rest from the commercial sector. The company has executed on its stated goal to shift its primary focus to the commercial sector. According to the latest estimates, government’s share of the customer mix has dropped to 20%, with the rest coming from commercial enterprises.
  • Raised Additional $1.6B Since our Initiation. Palantir has raised a total of $2.69 billion in 13 rounds. The last round (Series K) was completed on December 23, 2015. The amount raised was $880 million at $11.38 per share, raising the post money valuation to $20.53 billion.
  • Core Thesis Remains Positive. Our core investment thesis on Palantir is positive and rests on our belief that the Big Data phenomena is still in its early stages given the complexity and explosive growth in data formats from multiple devices.
  • Valuation. We believe, deriving Palantir’s valuation based on the bookings (as opposed to revenues) is justifiable. Based on this methodology, we peg Palantir’s valuation at $15.5 billion on the low end and $20.5 billion on the high end. This translates to $8.62 per share and $11.36 per share, respectively, based on 1.8 billion shares outstanding.


Exhibit 1: Palantir Booking ($B)











Source: Manhattan Venture Research


Exhibit 2: Palantir Revenue ($B)









Source: Manhattan Venture Research


Industry Update

Big Data is Still Big

Data sets have become increasingly complex. Three secular trends are driving the rapid adoption of Big Data/Smart Data: (1) Explosive growth of unstructured data; (2) The availability of innovative and cost-effective software and hardware tools; and (3) The wide applications of Big Data/Smart Data analytics across verticals that are facilitating proactive and data-driven business decision-making. In particular, the availability of open source software frameworks, commodity hardware, and the improvements in the price performance of memory and processing power. Together they have been instrumental in lowering the cost and improving the scale of the architecture. This will increase the appeal of Big Data to a wide range of verticals – beyond the financial services and other IT-intensive verticals.

One favorable aspect of Big Data development has been the prominence of open source software frameworks. In particular, the Hadoop platform has become synonymous with Big Data, and the focal point of the movement and spawned innovation, which is spreading through the broader technology industry. Other frameworks driving Big Data development are in memory architecture stores and specialized databases such as NoSQL.

Today, roughly 95% of data generated is unstructured, driven by humans and machines – a stark contrast to the data generated from the traditional enterprise business applications (e.g., ERP, CRM, SCM). The key drivers of this change have been a combination of machine data, mobile traffic growth, and social media. The traditional relational databases and IT infrastructure, which were designed for structured data, cannot process the enormous volume and complexity of today’s data.

Data, for all practical purposes, has become the lifeblood of almost all verticals, as data-centric decisions become the norm and not the exception. Every enterprise needs and wants to control, learn from and exploit the potential value of the data it collects and proliferates. Few are able to do so. Search for needles in an exponentially growing, unstructured haystack is a great challenge. From manufacturers looking to gain greater insights into streamlining production, to financial services firms seeking to proactively detect fraudulent transactions and government agencies using complex data sets to pinpoint national security threats, the operative term is predictive analytics. Palantir has emerged as a leader in providing solutions to this definitional problem of the 21st Century.

Industry projections for the market for Big Data software and analytics is estimated at $98.6B for 2019 in the US and $187 billion worldwide, growing over 50% by over 50% from 2015 (IDC). Consensus estimates for Palantir’s peers, which include Hortonworks , Tableau Software and Splunk, are 30-50% growth, showing the rising tides in the industry and near –term opportunity.


Figure 1: Global Big Data/BI Market ($B)










Source: IDC

Big Data firms are showing divergence based on their business models. Open-source platforms like Hortonworks have been challenged, while more specialized companies like Ayasdi (initially focused on healthcare) are rapidly scaling revenues. Ayasdi’s CEO has publicly stated he expects the company’s valuation to cross $1 billion next year. Proprietary technology and access to client data are the major moats separating the emerging leaders.

Investment Thesis

Our core investment thesis on Palantir remains positive and rests on the three central pillars outlined below. Additionally, we believe two concerns should be factored in to form a balanced view of the company:

Investment Positives

  • Still Early Days in Big Data. The explosive growth of data from multiple sources and its increasing complexity underpin the Big Data/Smart Data phenomenon. The volume of data produced globally doubles every two years, according to IBM. In the year 2000, for instance, 800,000 petabytes (PB) of data were stored in the world; that number is expected to reach 35 zettabytes (ZB) by 2020. [The growth in data production and storage is, literally, exponential. Data unit measurement terminology dramatically illustrates this trend. We keep needing and using new units of measure. Each new name, megabytes, gigabytes, terabytes represents an increase of 1000x over the old name. 1000 terabytes = 1 petabyte; 1 million petabytes = 1 zettabyte].

Automated and scalable handling of machine and sensor data is the looming challenge of the near future. Palantir has been a leader in melding structured and unstructured databases and gleaning usable patterns from either or both. We see a constant proliferation of raw machine data. There is a foot race to gain dominance in systematizing and harvesting insight, pattern and clarity from these mounting zettabytes. Palantir is well positioned to win this race.

  • Well Positioned in the Sweet Spot of Enterprise Spending. From manufacturers looking to gain greater insights into streamlining production, to financial services firms seeking to proactively detect fraudulent transactions and government agencies using complex data sets to pinpoint national security threats, the operative term is “predictive analytics” that deliver measurable and actionable results. Palantir is on the leading edge of predictive analytics and a big beneficiary of spending in this category.
  • Scalable, Secure and User-Friendly Solutions. Palantir’s software tools enable less technical users to visualize reams of data from several databases in a relatively user-friendly way. Specialists can look at specific bits of information and the links among them, users can find answers to complex questions and find the proverbial needle in the haystack. A common refrain after each terrorist attack or attempt is that various intelligence agencies had the various bits of intelligence to foil the plot, but could not “connect the dots”. This is the problem that Palantir is meant to solve: pulling in information from many sources and making it as easy as possible to connect the dots.


Investment Concerns

  • Enterprise Software Space is Highly Competitive. The market for predictive analytics products and services is extremely competitive and subject to rapid change. Large companies’ R&D departments, universities, start-ups and competitors are furiously spending and are hard at work in this area. While Palantir has established a strong presence with its sophisticated platform tailored to high growth markets – cyber security, terrorism, financial fraud, and defense – the incumbent software vendors (IBM, Oracle, and Hewlett Packard, among others) pose a strong competitive threat.

Ability to Maintain the Value Proposition. A constant risk for Palantir is its ability to stay at the cutting edge of technological innovations to tackle the increasingly sophisticated nature of threats to sensitive national security databases and installations –Palantir’s core competency. The company’s steep price tag of $1.0 million per installation in some cases – higher in a few cases – will be unsustainable if it cannot prove its value proposition, which is scalable, secure and user-friendly solutions. At a time when cost cutting is the mantra at all institutions, and new and innovative companies are always at your heels, no lead is permanent.

Product & Customer Update

Palantir has two main product lines, Gotham and Metropolis.  In the face of ubiquitous unstructured data coming from all types of machines and devices, the two products “tag and organize” the data, perform analytics, and find answers to critical questions.  The key focus of all Big Data analytics platforms is to find the answers to three core questions: what happened in the past (descriptive analytics), what’s happening now (real-time analytics), and what will happen in the future (predictive analytics).   Gotham addresses the first two, Metropolis all three.

Palantir’s technology has helped locate terrorists, prevent bank fraud and track disease outbreaks using data-mining tools that allow for users to comb through and make connections in massive sets of disparate data.

Palantir has updated its target markets to address the following use cases:

  • Case Management
  • Crisis Response
  • Disaster Preparedness
  • Trader Oversight

The growing number of applications indicate both increasing brand penetration and a scalable product with an ever-expanding addressable market. Early adopters in each channel have shown to be successful test cases for Palantir’s bespoke approach. This careful calibration has led to several new product joint ventures with customers, including Signac, a joint venture with Credit Suisse to proactively identify insider trading, and Insightics, a product built with First Data that uses payments information to provide recommendations to small businesses. Other prominent companies known to be using Palantir’s products include Hershey’s analysis for product placement on shelves; Santander, JPMorgan for finding fraud and enhanced compliance controls; Bridgewater Associates for sorting proprietary data on investments.

Palantir enjoys a technologically driven advantage over competitors. Best of breed learning algorithms require vast data sets to process and learn from. As an early player in Big Data/Smart Data, Palantir has been learning and iterating longer than many others. Additionally, the size, scope, and nature of Palantir’s large clients have allowed the company to process and learn from unique and vast caches of data.

To date, the company has assembled a broad roster of clients that range from the NSA and the FBI to JPMorgan Chase and News Corp. Other significant clients include Axa SA, Bridgewater Associates, Credit Suisse Group and First Data Corp. In November 2014, Palantir signed British Petroleum (BP), its biggest client worth $1.2 billion over 10 years, plus bonus payments as determined by executives from both companies, according to media reports.

Most recently (May 26, 2016), Palantir landed a $222 million firm-fixed-price contract from the U.S. Special Operations Command for the delivery of All Source Information Fusion software licenses and associated support services. The Defense Department said that Palantir will provide ASIF licenses to support SOCOM’s Special Operations Forces Acquisition, Technology and Logistics. SOCOM will obligate $5 million from fiscal 2016 operations and maintenance funds along with additional funding as it becomes available. DoD added the company will perform work in several locations within and outside the continental U.S. throughout the base year and three additional option years if exercised. The contract is a sole-source award for analytics software that will work to support intelligence analysis and production.

Commercial Contracts on the Rise

Palantir’s customer mix is shifting more and more toward commercial enterprises. The company now reports roughly 85% of its revenues from enterprise customers, doubling over the prior year, highlighting the company’s drive to diversify its revenue base and, more importantly, reduce its reliance on government contracts.

A key attribute of Palantir’s offering is its stickiness. This is a function of the company’s “total integration” approach where engineers are embedded on-site for six months or more. Typical commercial contracts with customers often last five to 10 years, though CEO Karp says clients are not locked in: “We believe that a good partnership is one where a client can leave.” “Deep” customer relationships have led to some new products/JVs as noted above and often lead to demand for additional user licenses, based on public contract histories available such as the LAPD.

Possible Pent-up Demand?

Palantir also subjects new customers to an approval process, including total information access for its engineers. The company offers two products, Gotham and Metropolis, which can be used for multiple industries and applications as seen below:

Now we can try to understand the Palantir product offering by spending hours trying to regurgitate their marketing material or by interviewing one of their PR people to get the canned media spiel, but instead we’ll give you an example of what they can do.

In a demonstration of how powerful their platform is, the Company put together a Palantir Gotham instance that integrated anonymized data from Medicare and various other data sources to show the potential of a fully integrated, interactive system. The instance integrated the 6 following data sets:

  1. Medicare data representing 100 million claims, 1 billion medical procedures, 30 million individual beneficiaries, and 700,000 physicians
  2. Data from the National Plan Provider Enumeration System, used to standardize identifiers across payers and providers
  3. Data from the Dartmouth Atlas Project—a well-curated collection of hospital-specific performance data
  4. Data from PubMed, representing 22 million biomedical journal articles.
  5. Data from the Department of Health Human Services Office of the Inspector General composed of entities excluded from participation as Medicare providers due to past fraudulent behavior
  6. Data from the US Census showing demographic trends across the country

Can you even imagine how tough it would be to link all these datasets? If you were building a data warehouse, you could just import all the databases into a master database and begin to query it. The problem here is that these aren’t all databases. Dataset #3 listed above consists of 22 million biomedical journal articles. How do you even begin to analyze those? That’s where Palantir’s tools can add value. And this example is just 6 datasets. The U.S government has made public almost 200,000 datasets from 170 different sources which they have posted for public access. There is an incredible amount of “big data” available for free and the first company that can start to link all these data sets and learn from them will have the one ring that rules them all. Palantir Gotham allows you to do this.

After you’ve created your ginormous Big Dataset, you can then use Palantir Metropolis to analyze the data and learn from it. Examples given by Palantir include tracking and analyzing insurance claims data, network traffic flow, and financial trading patterns:

Metropolis ingests terabytes of claims data from Fortune 500 payers to identify plan members in need of critical services like immunization and chronic disease screenings. End-to-end workflows in Palantir lead to direct action to connect these members intelligently with providers based on distance, barriers to service, and provider quality.


Palantir has made five acquisitions since February 2013, including two since our initiation in October 2014. The two most recent include Kimono Labs on February 15, 2016 and FT Technologies on February 6, 2015. The other three acquisitions were Propeller on July 31, 2014, Poptip on July 29, 2014, Voicegem on February 16, 2013. None of these 5 acquisitions seem to qualify as large or costly transactions.

Following is a brief description of the acquisitions:

  • Kimono Labs: Founded in 2014, Kimono had taken in $5 million to develop a way to turn websites into structured APIs from your browser in seconds. You don’t need to write any code or install any software to extract data with Kimono. You just add the Kimono bookmarklet to your browser’s bookmark bar. Then go to the website you want to get data from and click the bookmarklet. Select the data you want and Kimono does the rest. After just 2 years, over 125 thousand developers, data scientists and businesses used the tool that is until now.
  • FancyThat (FT) Technologies: Founded in 2013, “Fancy That” is the name of a technology developed by 4 Stanford students under the umbrella of a company they called “FT Technologies”. According to media reports, Palantir simply acquired the company for their talent (acqui-hire) as opposed to any finished product they had developed.
  • Propeller: Launched in 2012, Propeller had taken in $1.5 million to develop a web platform to quickly create and update apps with an intuitive drag-and-drop interface. While the process to create an app has traditionally been intimidating and expensive, Propeller was developing a platform that would allow anyone to create an app with no coding experience required. Right before they were acquired, Propeller was offering two solutions. One solution called “Shop” would let you quickly create a shopping app for your retail store. The other solution called “Pages” would let you turn your Facebook page into an app. It doesn’t look like they had a very big development team so their technology must have had some serious potential for such high profile investors to climb on board for such a small investment round.
  • Poptip: Founded in 2012, Poptip raised $2.4 million to develop their conversation analysis platform which helps brands and media fully understand public opinion in real-time and make decisions quickly based on that opinion. Their real-time natural language processing tool called Zipline could be used to quickly analyze large volumes of Tweets. They had also developed a tool called “Poptip Questions” which was a platform agnostic platform-agnostic solution to asking questions and getting answers which allowed publishers to engage their audiences in two-way conversations using social voting. Before Poptip was acquired, their customer list included L’Oreal, NBA, ESPN, NFL, Spotify, Budweiser and Yoplait.
  • Voicegem: Founded in 2012, Voicegem took in an undisclosed amount of funding to develop their platform, VoiceGem, which allowed you to send voice messages to anyone. Enter any number of email addresses, record a message as long as you want and your voice message or “gem” would be delivered to your recipients’ inboxes.

Funding Update

Palantir has raised a total of $2.69 billion in 13 rounds. The last round (Series K) was completed on December 23, 2015. The amount raised was $880 million at $11.38 per share, raising the post money valuation to $20.53 billion.

The following figure highlights all the rounds, along with the lead investor(s) in each round, amount raised, price per share and the implied post-money valuation.


Figure 2: Palantir Funding Rounds













Source: Pitchbook, Manhattan Venture Research



We have reconstructed Palantir’s financial model following newly available information from multiple sources.

Palantir’s revenue model is a combination of sales (via software solutions sold to clients) and maintenance & support services, either bundled with the contract or as after-sales services. According to our estimates, the company generates approximately 40% of its revenue through the sale of software and solutions and the balance from providing services to clients. With regard to clients, Palantir generates about 15-20% of the revenue from government clients and the balance from commercial private enterprises. The commercial contribution has been rising and we expect this trend to continue.

Based on our assumptions and checks, we believe the company exited 2015 with $1.7 billion in bookings, $420 million in revenue, and an EBITDA loss of $421 million. Looking ahead, we expect bookings to maintain its robust pace of growth on the back of new contract wins. We also expect the pace of revenue conversion to pick up in 2016 and forward, resulting in positive EBITDA in 2017.

[For a more detailed discussion with the MVR analysts on the financial model and the assumptions, please contact your sales representative]


Figure 3: Palantir Model ($M)












Source: Company reports, Manhattan Venture Research



Palantir has a unique revenue model. The company typically enters into long term contracts and recognizes revenue over time, with additional bonuses and other incentives tied to the contract. Accordingly, we believe, deriving Palantir’s valuation based on the bookings (as opposed to revenues) is justifiable. Based on this methodology, we peg Palantir’s valuation at $15.5 billion on the low end and $20.5 billion on the high end. This translates to $8.62 per share and $11.36 per share, respectively, based on 1.8 billion shares outstanding.


Figure 4: Implied Comparative Valuation – Based on EV/Bookings Multiple ($M)












Source: Manhattan Venture Research


About Manhattan Venture Partners

Our Research Methodology

Manhattan Venture Partners provides clients with accurate, timely and innovative research into the companies and sectors we cover. To that end we have established an experienced team of analysts, researchers, economists and industry veterans that focus exclusively on private companies with a proven track record of success. Producing quality research on a private company is uniquely challenging. Our analysts communicate with employees, ex-employees, early investors, VCs, competitors, suppliers and others to gather valuable information about the company under coverage. This information enables us to create unique financial models that value the underlying company and provide insight to our clients and industry experts, leveraging years of experience working for bulge bracket firms.

Manhattan Venture Partners reports include business and financial aspects of late-stage companies. These reports include but are not limited to industry overviews, competitor analyses, SWOT analysis, products (existing and in development), management and key directors, risks and concerns, other propriety channels, historical financials, revenue projections, valuations (using various matrices and valuation recommendation), waterfall analysis, and a capitalization table.

About the Analysts

Santosh Rao

Santosh Rao has over 18 years of experience in equity research, primarily within the technology and telecommunications space. He started his equity research career as an Associate at Prudential Securities and later moved to Broadpoint Capital (Formerly First Albany Capital), where he was the Senior Equity Analyst, and later to Evercore Partners, where he worked with the Telecom and Data Services Group. Prior to joining Manhattan Venture Partners, he was the Managing Director and Head of Research at Greencrest Capital, focusing on private market TMT research. Mr. Rao started his career as a Financial Analyst in the Operations Groups at PaineWebber (UBS) and Prudential Securities. Santosh has an undergraduate degree in Accounting and Economics, and an MBA in Finance from Rutgers Graduate Business School.

Max Wolff

Max Wolff is an economist specializing in international finance and macroeconomics. Before joining Manhattan Venture Partners, he was Chief Economist at Greencrest Capital, and prior to that spent four years as the senior hedge fund analyst at the Beryl Consulting Group LLC. Mr. Wolff teaches finance and statistical research methods in the New School University’s Graduate Program in International Affairs. Max’s financial markets and Macro-Economics work appears regularly in Seeking Alpha, The WSJ, Reuters, Bloomberg, The BBC, Russia Today TV, and Al Jazeera English.


I, Santosh Rao, certify that the views expressed in this report accurately reflect my personal views about the subject, securities, instruments, or issuers, and that no part of my compensation was, is, or will be directly or indirectly related to the specific views or recommendations contained herein.

I, Max Wolff, certify that the views expressed in this report accurately reflect my personal views about the subject, securities, instruments, or issuers, and that no part of my compensation was, is, or will be directly or indirectly related to the specific views or recommendations contained herein.

Manhattan Venture Partners LLC (Hereafter “Manhattan Venture Partners”), the parent company of Manhattan Venture Research, does and seeks to do business with companies covered in this research report. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision. This document does not contain all the information needed to make an investment decision, including but not limited to, the risks and costs.

Additional information is available upon request. Information has been obtained from sources believed to be reliable but Manhattan Venture Partners or its affiliates and/or subsidiaries do not warrant its completeness or accuracy. All pricing information for the securities discussed is derived from public information unless otherwise stated. Opinions and estimates constitute our judgment as of the date of this material and are subject to change without notice. Past performance is not indicative of future results. Manhattan Venture Partners does not engage in any proprietary trading.  The user is responsible for verifying the accuracy of the data received.  This material is not intended as an offer or solicitation for the purchase or sale of any financial instrument. Manhattan Venture Partners does not have ownership of the subject company’s securities. Manhattan Venture Partners does not have any market making activities in the subject company’s securities. The opinions and recommendations herein do not take into account individual client circumstances, objectives, or needs and are not intended as recommendations of particular securities, financial instruments or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. Periodic updates may be provided on companies/industries based on company specific developments or announcements, market conditions or any other publicly available information.

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