LinkedIn’s Economic Graph – The Digital Home of the Global Workforce

How LinkedIn is embracing digitization beyond its core business model

“Twenty years ago, the resume was a piece of paper. Now, it’s a collection of all [candidate] data that can be found online, like participation in online communities, conferences and meet-ups. Recruiters can assess whether a person will fit, and learn if he or she has the right skills for a job.” – Jon Bischke [1]

The recruitment industry has undergone a huge transition from offline to online in the past two decades. From newspapers job listings and rolodexes to job sites and professional networking platforms, digitization of recruitment has powered the business models of recruitment technology companies such as Indeed, Monster, and of course, LinkedIn.

To most people, LinkedIn may seem like a place to brand yourself professionally, look for jobs, or network with other professionals. And from a B2B perspective, LinkedIn’s business model relies on enabling recruiters, salespeople and marketers to target the sets of professionals they are looking for. LinkedIn has ridden the wave of digitization to build a network that enables these core customer propositions, but LinkedIn’s vision expands much further than that, which is to create economic opportunity for every member of the global workforce by creating digital representations of members, companies, jobs, skills, schools and knowledge.

LinkedIn’s business model is built on creating value for a network of professionals which has enabled it to accumulate a wealth of data. Every time members add new connections, update their profiles, share their professional insights, or every time employers post jobs or make new hires, LinkedIn is learning something about the global workforce. In aggregate and over time, this activity makes it possible for LinkedIn to create a digital map of the global economy. And LinkedIn’s operating model must interpret and surface this data, with thoughtful privacy controls, to the right consumers of this data. As you can see, LinkedIn’s business model and operating model are closely intertwined and both fueled by its ultimate vision of creating the world’s first Economic Graph.

The Economic Graph is a digital representation of:

  1. The world’s 3 billion workers
  2. All 60-70 million companies that employ these workers
  3. All jobs at those companies
  4. Every skill required to obtain those jobs
  5. Profiles of higher education institutions and the classes offered
  6. Published knowledge [2]

How will LinkedIn interpret and operationalize insights from building these nodes within the Economic Graph? Here are two examples of how LinkedIn is converting data into actionable recommendations to solve big problems.

In 2015, LinkedIn piloted a program in in Colorado and Phoenix for workers to acquire new skills to advance their careers. This is important because while half of the workers in these two states have at least a high school diploma and some college education, half of the recruiters in these states said it was hard to find people with the right skills. LinkedIn developed Training Finder which is a marketplace matching workers who have a desire to upskill themselves and training programs located in their area. Training Finder provides information on the programs’ employment rate, cost, duration, skills taught, and estimated salary of graduates. [3] These examples are more relevant than ever, since according to the World Economic Forum, technology will displace five million of today’s jobs by 2020. [4] Workers must continually upskill themselves to remain relevant in a world where automation is inevitable.

training-finder

LinkedIn’s direct access to 450 million+ professionals enables it to facilitate knowledge sharing and to pool valuable data in a way no other company can. In November 2016, LinkedIn launched its Salary tool to enable professionals to make more informed career decisions. Any LinkedIn member can access aggregate salary insights by contributing their own salary information anonymously. [5] With initiatives like this, LinkedIn can eliminate information asymmetry and reduce job market inefficiencies that would otherwise persist.

linkedin-salary

LinkedIn’s data puts it in a unique position to create value for many constituents beyond the two examples above. Here are some other high potential ideas for the future:

  • Advise governments on which countries their talent is migrating to and immigrating from to inform work visa policies.
  • Advise educational institutions on what courses to offer based on forecasted employer demand.
  • Advise companies on locations to open new overseas offices where they may be able to more easily hire the right talent.

In LinkedIn’s own words, the Economic Graph will allow all forms of capital – intellectual, working, and human – to flow to the areas where it can best be leveraged, giving all LinkedIn members the means to begin, grow, and transform their careers. [6] At the same time, LinkedIn must continue to shift its business model from a provider of a professional network to a data-driven advisor for members, institutions, governments or any other party that could benefit from the collective wisdom of the Economic Graph.

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[1] Nicole Fallon Taylor, “Hiring in the Digital Age, What’s Next for Recruiting?”, Business News Daily, January 11, 2016, www.businessnewsdaily.com/6975-future-of-recruiting.html, accessed November 2016.

[2] Virginia Backaitis, “What LinkedIn’s Economic Graph Brings to Microsoft, You and the World,” CMS Wire, September 26, 2016,

http://www.cmswire.com/digital-workplace/what-linkedins-economic-graph-brings-to-microsoft-you-and-the-world/, accessed November 2016.

[3] Allen Blue, “How LinkedIn is Helping Create Economic Opportunity in Colorado and Phoenix,” LinkedIn Official Blog, March 17, 2016,

https://blog.linkedin.com/2016/03/17/how-linkedin-is-helping-create-economic-opportunity-in-colorado-and-phoenix, accessed November 2016.

[4] Oliver Cann, “Five Million Jobs by 2020: the Real Challenge of the Fourth Industrial Revolution,” World Economic Forum, January 18, 2016,

https://www.weforum.org/press/2016/01/five-million-jobs-by-2020-the-real-challenge-of-the-fourth-industrial-revolution/, accessed November 2016.

[5] Ryan Sandler, “Introducing “LinkedIn Salary”: Unlock Your Earning Potential,” LinkedIn Official Blog, November 2, 2016, https://blog.linkedin.com/2016/11/02/introducing-linkedin-salary-unlock-your-earning-potential, accessed November 2016.

[6] “Economic Graph Challenge Frequently Asked Questions,” on LinkedIn Economic Graph Challenge website, economicgraphchallenge.linkedin.com/faq/, accessed November 2016.

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5 thoughts on “LinkedIn’s Economic Graph – The Digital Home of the Global Workforce

  1. Great post, Georgia. Given our discussion today in class I am intrigued by one of the suggestions you gave for Linkedin: “Advise educational institutions on what courses to offer based on forecasted employer demand.” How can Linkedin effectively predict employer demand? In addition, can it monetize this data not just in the context of educational institutions but also workers that are likely to have their jobs globalized or disintermediated by technology?

  2. Thank for this post, Georgia! It became clear to me that LinkedIn’s vision is much broader than just being a professional network to match employers and workers at any point in time. All the efforts you’ve described can indeed be transformational for all stakeholders in a future that looks extremely challenging for professionals, companies and governments. I just wonder if the company is prepared to effectively switch its business model to one that heavily relies on data analytics, either because of the substantial investments that would be required to become such a complete global player, or because of lack of high-quality information (is all data collected reliable?). I also question whether some of the practices you mentioned such as advising companies on where to expand their operations would lead to significant conflicts of interest (i.e.: LinkedIn’s current clients may not welcome more competition for talent where they operate).

  3. This is really exciting, Georgia! This data is a competitive advantage for LinkedIn so I’m curious how it utilizes it moving forward. If it was to make it more openly accessible to third-party developers and governments it could create a significant amount of value overall as these players could find creative applications and uses for it. However, if LinkedIn prices access to the data too high or decides to keep it all to itself it may capture more value for itself but likely also generate less value for others. I also wonder if there are notable gaps in the dataset, specifically for more blue-collar workers or industries (e.g., construction) that are less likely to utilize the platform, and if LinkedIn has any intentions on filling in these gaps. Would love to hear your thoughts on these topics!

  4. It is indeed a very informative post. I earlier believed that Linkedin is only a professional network. But going through this post I realised that Linkedin’s mission is way beyond.In the age of internet when things change so fast, it is really important to continuously upgrade skills. The question which needs to be answered is which are the skills required and going through your post I realised Linkedin’s vision is to provide answers to these questions.

  5. This is fascinating, thanks Georgia. I can really see the value LinkedIn could provide here. However, to take a truly global view the company needs global coverage, which is something it currently does not have. For example, Xing is the leading player in Germany and Dajie is in China. Maybe this requires a more aggressive global M&A strategy? Or a greater organic growth push in key markets?

    In countries where LinkedIn is the market leader I think there is also an opportunity to publish perhaps less noble, but highly valuable, reports on specific industries. As a consultant I used LinkedIn as a research tool for competitor analysis e.g. to determine what sectors specific companies are seeking to grow in by analysing job titles and joining dates, but at the time it was not set up for this use and this required manual work. We definitely would have paid for some custom analytics!

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