Trillium Insights

Thoughts and Insights from Trillium's Practice Leaders

What’s the big deal anyway?

What’s the big deal anyway?

Some people believe resume, LinkedIn and Internet sourcing is so easy that sourcing is either dying or dead or can be performed for $6/hour. After all, resume and LinkedIn sourcing appears simple and easy on the surface, however – it is deceptively difficult and complex. Anyone can find candidates because all searches "work" as long as they are syntactically correct. That doesn’t mean the searches are finding all of the best candidates.


People make assumptions when creating searches. Every time an assumption is made, there is room for error and you unknowingly miss and/or eliminate results! After all, no single search can return all potentially qualified people. Every search both includes some qualified people and excludes some qualified people. Some of the best people have resumes or social profiles that may not appear to be obvious or strong matches to your needs. People cannot effectively be reduced to and represented by a text-based document. Job seekers are NOT professional resume or LinkedIn profile writers which means people don’t create their resumes and LinkedIn profiles thinking about how you will search for them.

Most people still believe shorter and more concise resumes and social profiles are still better which means they are removing data/info from their resumes which can no longer be searched for! No one mentions every skill or responsibility they’ve had, nor describes every environment in which they’ve ever worked. There are many ways of expressing the same skills and experience, and even employers often don’t use the same job titles for the same job functions.  Not to mention that sometimes people don’t even use correct terminology.

Finally, anyone easy for you to find is easy for other recruiters to find which means there is no competitive advantage!  The work I did gathering available data has really paid off in allowing me to truely understand how the data relates.  It is really powerful.

 

Finding some people is easy… finding the best people IS NOT!

Finding some people is easy… finding the best people IS NOT!

In addition to the individuals that recruiters find while searching a system for job candidates, there are people that could be a good fit, but aren't found because of the lack of a specific keyword in a text box or missing value from a dropdown. Clues to a great candidate are hidden in the freeform cover letters and text resumes that might get ignored.  I’m guessing that a rough estimate of this class of data would be at least 50% of each data source that is searched. Using the Natural Language Processing power of Watson streamlines the search process and eliminates the need to create difficult and complex Boolean searches that rely on heavily formated data.  By also including the "intelligent search and match capabilities" of Watson ensures that a recruiter will consider other qualified candidates that might otherwise have been missed.

What Do I Know?

What Do I Know?

Trillium staff have hands-on practical experience, (read that as trying to find people to fill real jobs), with many of the industry leading search applications specifically designed for human capital data, so when I talk on the matter of semantic search, I’m not throwing around empty opinions. I’ve seen what these solutions can do, and what they simply can’t do. We have to find and recruit talented, qualified candidates with highly specialized skills and experience within 24-48 hours of receiving a client request on a daily basis.  If semantic search solutions can speed up that process and help us find more and better candidates faster – trust me, we would use them! Ultimately, it’s not about humans vs. technology – it’s about results.

Watson is Relatable

Watson is Relatable

If you search YouTube, you can find the historic Jeopardy episode and watch as IBM’s Super Computer “Watson” blows away Brad Rutter, Jeopardy’s all-time biggest money winner, along with Ken Jennings, the show’s record holder for longest championship streak. The formidable players stood no chance against Watson, which ended the game with $35,734 compared to Rutter’s $10,400 and Jennings’ $4,800. Watson parsed the keywords in the Jeopardy’s clues while looking for “related” terms as responses.


The operative word is “related”. Identifying related terms and concepts is something that pure Boolean searches cannot do – after all, Boolean searching is about looking for specific keywords (i.e. those included within the string itself).  The ability to identify related terms and concepts is akin to Semantic Search, which seeks to improve search accuracy by understanding searcher intent and the contextual meaning of terms as they appear to generate more relevant results.


The ability to quickly research and answer trivia questions (or provide questions for the answers, in the case of Jeopardy) is a far cry from having to boil a hiring need (skills, capabilities, and specific responsibilities in specific industries and environments) down to a series of queries to mine flawed and incomplete human capital data (i.e., resumes and social media profiles) in order to return people who have a high probability of not only being qualified for the position, but also interested in the job (i.e. “recruitable”). My short term goal is to use Watson to create the process to get and relate the data into those requests and find the right recruitable people.