LinkedIn’s Shiny Data Pools

How much should you trust LinkedIn’s big and small data.

LinkedIn gathers a lot of data. With 258 million subscribers, 3 million business pages, over 1 billion skills endorsements and 2 new people joining every second, there is a lot of information to gather, process, analyze and apply.

According to LinkedIn Chief Executive Jeff Weiner “Our ultimate dream is to develop the world’s first economic graph”. He describes this as a digital map of skills, people and job opportunities around the world. Certainly LinkedIn is a great source of data. However, this data is self-reported, self-selected and highly filtered and, as such, there can be challenges in relying on it to make decisions.

How LinkedIn Makes Money: The Three Main Money Makers

LinkedIn makes money from selling ads, selling premium accounts, and providing talent solutions for employers and recruiters. The later is the big money maker. ‘Big data’ mining and selling is one of the biggest trends of the year. For LinkedIn to continue to benefit from this trend, they must continue to gather updated and accurate information from their members.

Do You Know What LinkedIn is Doing With Your Data?

When you create even a basic free account, you provide significant data. In exchange for your data, you can gather data from other people. It is a fairly straightforward exchange. Additionally, based on the entire array of data you provide, LinkedIn also pushes information at you including suggestions of people, groups, training, news, services, jobs, and more info provided as relevant by their algorithm.

Consider the data LinkedIn mines. When you complete your history you are telling a story about your past and present and, it turns out, maybe your future. By identifying patterns of careers, skills, education, human migration, trends in education, training and certification, gaps in skills in certain markets and more LinkedIn makes recommendations. LinkedIn can ‘’see’ which types of candidates are a better match for specific companies in specific geographic locations based on past employment and retention rates.

With the ‘economic graph’, Weirner claims,  ‘we could look at where the jobs are in any given locality, identify the fastest growing jobs in that area, the skills required to obtain those jobs, the skills of the existing aggregate workforce there, and then quantify the size of the gap”. He indicated that they could feed that information to those planning and delivering training thereby helping local communities identify the skills they need to prioritize. The implications of LinkedIn data mining are fascinating but not without many challenges. For it to work LinkedIn needs to presume that profiles are generally accurate and that their algorithms are working as they planned.

Algorithms, Accuracy, Fatigue and Privacy

Algorithms: LinkedIn employs researchers, behavioural scientists, engineers and more to build tools and use the information compiled. Just how sustainable and accurate is the information sourced and shared?

Accuracy:  People misrepresent and falsify information all of the time. Data reported last year by StatisticBrain.com indicates up to 53% of resumes contain false information. Almost 27% of respondents in the survey (conducted by AOL jobs) indicate they have or would consider lying on their resume. The data provided to LinkedIn can often be inaccurate and incomplete.

Fatigue: LinkedIn began as a digital Rolodex for professional networking. As the database grew, employers began using LinkedIn for recruiting. LinkedIn used to have ‘rules’ that suggested you only connect with people you know. But that ‘rule’ has gone out the window. For this, and many other reasons, it is possible that high quality candidates choose to opt out of LinkedIn or at least opt in less frequently. LinkedIn fatigue could impact the quality of data LinkedIn uses to feed their algorithms.

Privacy: LinkedIn suggests activity to users frequently; a connection, group, job, training and more.  In order for LinkedIn to make appropriate recommendations, they spy on their members. LinkedIn watches your past and present, and makes predictions about your future. Are people so used to a lack of personal privacy that having their every move on LinkedIn analyzed for aggregate and individual use no longer raises flags? If LinkedIn ‘filter bubbles’ what you see and who sees you on LinkedIn that has  impact on your very future. To learn more about Filter Bubbles, watch 2011 TED presentation entitled, Eli Pariser: Beward online “filter bubbles’.

LinkedIn provides some seemingly uncanny information with myriad implications. You can certainly use this information for efficiency. The information can be of benefit. However, it is important to consider the costs as well as the benefits. If you use LinkedIn data,. you may find what you need but you should remind yourself of what you might be missing.

Sources

The Future of LinkedIn and the Economic Graph

The Relevance of Data: Going Behind the Scenes at LinkedIn