Category Archives: Dark Matter

How to Effectively Source Talent via Social Media & Networks

Sourcing talent via social media requires an entirely different mindset than sourcing with other forms of human capital data, such as resumes/CV’s, employee directories, conference attendee lists, etc.

Back in early 2009, one of only 2 guest posts ever co-written on my site was published on the topic of non-standard descriptors and the role they play in social media. Valerie Scarsellato was a Sr. Sourcer at Intel Corporation at the time when she put together the framework for the original article on sourcing via social media, and she has now moved into a Segment Marketing Specialist role at Intel and is loving it. For those of you who feel that employer marketing/branding/communications is a logical extension of sourcing, Valerie would wholeheartedly agree with you – check out this video in which she discussed her award winning _codehearted; work for Intel.

Now that nearly 2 years has passed since the Searching Social Media Requires Outside-the-box Thinking article was published, social media usage has continued to explode – monthly visitors to LinkedIn and Facebook have doubled, they’ve nearly quadrupled for Twitter , and we now have Google+, Pinterest and others springing on the scene, making the topic even more relevant today. As such, I wanted to rework the original piece and update it with a few more examples.

The primary challenge when leveraging social media for sourcing talent is that nonstandard terminology is prevalent – it’s generally acceptable to use slang and other verbiage that would otherwise never be found on a resume, even when it comes to describing one’s profession.

If you use the same query terms when sourcing LinkedIn, Facebook, Twitter, etc. as you would when searching for resumes, you will certainly find people. However, you will also exclude a decent portion of the available results, unknowingly relegating them to Dark Matter and otherwise undiscovered talent. This is because you can only retrieve what you explicitly search for. Continue reading

Talent Sourcing: Man vs. AI/Black Box Semantic Search

Back in March 2010, I had the distinct honor of delivering the keynote presentation at SourceCon on the topic of resume search and match solutions claiming to use artificial intelligence in comparison with people using their natural intelligence for talent discovery and identification.

Now that nearly 2 years has passed, and given that in that time I’ve had even more hands-on experience with a number of the top AI/semantic search applications available (I won’t be naming names, sorry), I decided it was time to revisit the topic which I am very passionate about.

If you’ve ever been curious about semantic search applications that “do the work for you” when it comes to finding potential candidates, you’re in the right place, because I’ve updated the slide deck and published it to Slideshare. Here’s what you’ll find in the 86 slide presentation:

  • A deep dive into the deceptively simple challenge of sourcing talent via human capital data (resumes, social network profiles, etc.)
  • How resume and LinkedIn profile sourcing and matching solutions claiming to use artificial intelligence, semantic search, and NLP actually work and achieve their claims
  • The pros, cons, and limitations of automated/black box matching solutions
  • An insightful (and funny!) video of Dr. Michio Kaku and his thoughts on the limitations of artificial intelligence
  • Examples of what sourcers and recruiters can do that even the most advanced automated search and match algorithms can’t do
  • The concept of Human Capital Data Information Retrieval and Analysis (HCDIR & A)
  • Boolean and extended Boolean
  • Semantic search
  • Dynamic inference
  • Dark Matter resumes and social network profiles
  • What I believe to be the ideal resume search and matching solution
Enjoy, and let me know your thoughts.

LinkedIn’s Dark Matter – Undiscovered Profiles

Sourcing has a fundamental problem: All searches return results.

Yes, that is actually a problem.

Why? Because everyone’s a winner.

Type in a few keywords and BAM! – you get some good looking results. Hey, this sourcing stuff isn’t so hard!

If I’ve said it once, I’ve said it a thousand times – sourcing is easy. In fact, it’s ridiculously easy to find some people.

So if you and your company are happy with finding some people and not necessarily the best people available to be found, then you can stop reading now and go back to finding some people.

For everyone who’s still reading this, try answering these questions:

  1. Can you ever be sure you’re finding everyone there is to be found?
  2. How do you know you’ve found the best people available?
  3. How do you know you’ve found all of the best people?
  4. Are there people on LinkedIn, in your ATS, in job board resume databases that are never found?
  5. How can you be aware of social media profiles and resumes that your searches can’t return in results – but are there?

Sourcing is easy, but it’s not easy to get to the point where you are sure you have found all of the best available results, nor is it easy to specifically target and find people others cannot and do not.

Most people use relatively basic, straight forward/direct keyword and title searches. There’s nothing wrong with that – they clearly “work” – anyone running those kinds of searches will find results.

However, they will also find exactly what everyone else finds when searching for the same types of people, which yields zero competitive advantage.

The fact that all searches produce results is a problem because it lulls people into thinking that sourcing is easy, and at least on the subconscious level – it leads people to believe that the results that are returned from searches represent all available matching and relevant results.

However, it is a fact that no single search can find all of the people you’re looking for, and there are many social media profiles and resumes that are never found.

Let me introduce you to the concept of Dark Matter. Continue reading