Palantir is in many ways the epitome of the power of big data and today’s enterprise needs to better leverage their own data. At its core, Palantir is a big data consultancy: a company that deploys teams of engineers and business people to client companies to help them set up more powerful data visualization and analytical infrastructure. However, through another lens, Palantir can be seen as an outsourced solution for other companies to capture more value out of their own data.
Palantir was spearheaded in the wake of 9/11 by a team of Silicon Valley heavyweights including Peter Thiel, largely in response to the government’s need for greater insights into data for counter-terrorism goals. Soon, Palantir began to serve myriad other government institutions for their various big data needs, a notable example being data-driven anti-fraud projects.
Using their government projects to build infrastructure, expertise, and credibility, Palantir soon began to expand to commercial projects. Today, its clients come from all industries, notable ones including finance (anti-fraud being a great concern for insurance and bank companies), law (where legal research projects are becoming more complex and data-intensive), and pharma (where R&D is similarly becoming more complex). Despite growing quickly over the last decade and being valued at over $15B, Palantir reportedly is not going to seek an IPO.
Palantir’s value creation stems from its ability to create big data solutions for other companies. In the world of increasing sources and complexity of data, there is value to be unlocked in the insights that data can provide (which exist, but are hard to get to), and value to be captured by a company that can help other companies unlock those insights. Palantir’s consulting services involve assessing the types of data the company already generates, combining it with publicly available sources of information, and creating more robust and flexible database structures and visualization tools to allow other company functions to better perform their jobs.
For example, one Palantir project has helped pharma companies detect fraudulent prescription claims. Palantir can combine proprietary data on drug claims from individual pharmacies with all manners of publicly available data, such as business information and pharmacy addresses. It then creates a portal where an analyst can see, for example, a map of all pharmacies within an area, any overlapping names of people who have submitted suspicious prescription claims, and then a timeline of when all these claims happened. Using Palantir’s tools, the client company can ostensibly better detect fraud – value creation in the form of a cost reduction. Palantir also captures some of this value in the form of its fees.
A couple things are noteworthy about this process and operating model. First, Palantir is not necessarily in the business of providing the ultimate solution for its clients – for example, it creates the systems to help the company better detect fraud, but it does not always draw conclusions itself about which accounts are fraudulent. This allows Palantir to stay within its own realm of expertise (i.e. creating big data systems) without having to develop numerous, deep domain-specific areas of knowledge (e.g. detecting pharmacy-related fraud).
Second, Palantir is not really in the business of helping the company develop completely new sources of data. There is already much value to be created by simply leveraging data the client already collects with publicly available data.
Third, by slightly abstracting away from the specifics of the industry or client, Palantir develops solutions that are replicable across clients. Principles of visualization and working with private and public data apply beyond the boundaries of a given firm, and Palantir is well-positioned to develop this area of cross-firm and cross-industry expertise.
Fourth is a critical aspect of the process that is not as well advertised in public materials: Palantir generally only accepts projects where there is strong support from the highest executive level, often the CEO. Its projects could be seen as a threat to the company’s internal IT or business intelligence units, so it can avoid some politicking by demanding a mandate from the very top.
Finally, a critical key to the Palantir operating model is its talent – it has a high bar for recruiting engineers (who today would probably rather join a “sexy” Silicon Valley company than the IT department of most of Palantir’s clients) and also those with business backgrounds (who can better deal with client relations). Much like management consulting firms, Palantir uses human capital to create value for a range of client businesses, human capital that its clients largely cannot attract. In some senses then, Palantir combines elements of work traditionally done by IT or infrastructure consultancies (e.g. SAP, Salesforce, Oracle) with the high-level mandate and broad human capital of management consultancies (e.g. McKinsey, Bain, BCG).
Palantir is an impressive manifestation of the power of data and new kinds of businesses that can be built on top of data expertise.