linkedin – DAN REICH

The Future of Hiring New Employees

Image representing LinkedIn as depicted in Cru... — Image via CrunchBase

This post originally appeared on Forbes.com.

Technology and data are changing the ways companies do business but perhaps more interestingly is the way they are influencing how companies are hiring, and could be hiring, new employees. In many organizations the human resource department is considered the most important part of the organization. And rightfully so. A company is nothing more than the people within it so it should be no surprise when you hear about how rigorous some hiring processes are. For example, its been said that Google has had candidates come in to interview “as many as 16 times before ultimately releasing them back to the wild.”

So let’s take a look at three new and innovate concepts that may help drive the future of how companies are staffed.

The Social Graph. It’s not what you know but who you know. We’ve all heard this phrase before but we finally have social graphs that are accessible through technology. These are social graphs and social connections that will at some point be used to help us as individuals in our careers. So imagine how powerful it would be if companies could leverage one’s social network to get personal references at scale. A company called Jibe is doing this and their employers have said that Jibe candidates are 4x more likely to be hired than those from traditional job boards. And it makes complete sense. Personal references are invaluable and its why 92% of hiring managers in 2010 used social networks as a recruiting tool, according to CareerEnlightenment.com.

Statistical Data Models. If data exhaust was actually smoke we would all be suffocating by now. The abundance of raw data is staggering and making sense of it all can be a daunting task. It is the reason new cloud computing based companies are starting to emerge. But capturing and understanding data is very different than taking actionable steps from the findings. There is a new school of thought among firms that look to statistical models as the basis by which candidates are hired. For example, every company has an associated cost with hiring and training a new employee. The cost of hiring this new employee is recouped if that person stays for a certain period of time. If however that candidate leaves before that time, the firm realizes a loss in opportunity costs. So what if you could predict with a high level of probability that a candidate will stay beyond a certain period of time? What if you could essentially predict which candidates are retention risks? Well this is what one, stealth-mode Chicago firm is working on and their results could end up saving firms millions of dollars annually in their hiring process.

Niche Data Sets. Hiring a math teacher is very different than hiring a quantum mechanics physicist. As the world continues to slice itself up into niche verticals, it will also be important to have niche data sets especially for the purpose of hiring highly specialized candidates for very specific roles. This is probably the reason why LinkedIn (NASDAQ: $LNKD) has seen its biggest growth in revenue come from its hiring solutions line of business. This is also probably part of the reason why LinkedIn has subtly added new fields like “skills” into profile pages. These fields make it easier for recruiters and hiring managers to look for people with very specific skill sets. And just as the ladders.com tailored to folks looking to make over $100k, I believe there is also an opportunity to specialize on other niche verticals like B2B sales, pharmaceuticals, nuclear engineering, and many, many more.

New solutions will continue to emerge but I think we are beginning to see the future of how new firms will hire employees.

Connect with Dan Reich on Twitter – @danreich.

Generativity of Social Networking Sites and Their Accountability

Jonathan Zittrain defines Generativity in the following manner:

“Generativity is a system’s capacity to produce unanticipated change through unfiltered contributions from broad and varied audiences”

Jonathan Zittrain, The Future of the Internet and How to Stop It

In reality, this description somewhat defines the nature of a social networking sites. If you look at social networking sites today, users are able to participate in 3 ways, all of which contribute to the generative nature of a social network: Users are able to:

Generate self identifying content (their profile, blog, homepage)
Generate and consume bi-directional content (messaging, statuses)
Generate and consume multi-directional content (groups, discussion boards, forums)

These three methods of participation allow the internet and social networking sites to grow at the staggering rate they are today. However, as these sites grow, keeping the content organized so that it remains relevant and meaningful to the user, becomes increasingly difficult. This issue is more prominent in the third method, as users are able to impact the entire network in a single instance.

Take for example the Groups feature. A single user can create a group made available to the entire network. That’s fine. But what happens when multiple users create the same group? An overlap occurs, and what should have been a single meaningful group, now becomes one group of many just like it.

Today, I joined my University of Wisconsin – Madison group as I am a recent alumni. There were about 3-4 identical groups? Do I join them all? The same scenario applied to many of the groups I wanted to join.

The generative nature of social networks allow for more noise, and enables users to disrupt the very social graph they create, making the networks more complex and less meaningful. Other people recognize the growing occurrence of this noise, and ironically enough, have used the same generative nature of social networks to maintain strong connections, content, and a healthy social graph (see Triiibes).

About six weeks ago, I joined Seth Godin’s social network called Triiibes (which he created using a white-box social network: Ning). The network was only made available to those that made an early purchase for his new book. As a result, the content and communication in the network is much stronger and meaningful then I’ve seen on any other network.

As social networks grow, they must look to sites like Wikipedia for guidance. They must learn how to keep the network connected using only meaningful and unique data points.

(Jonathan Zittrain’s book is a good read for anyone interested in technology and communications, and their inevitable effects on society)