Tag Archives: Sourcing

What is Sourcing? I Propose a New Universal Definition.

 

Definition of Sourcing on TwitterWhat better time than at the beginning of a new year to take a critical look back at where we’ve come from, to reflect on our current state and to look forward to a next step in the evolution of sourcing?

It believe it would certainly be helpful and beneficial to have a universally agreed upon definition of exactly what sourcing is. If you’ve attended any sourcing and/or recruiting conferences, it doesn’t take long to notice people using “sourcing” to describe different types of activities. When anyone talks about the sourcing function at their company, it immediately begs the question of exactly what the sourcers are tasked with. Do they find people and pass them on to recruiters to contact, or do they also engage the people they find? The same goes for hiring sourcers – one of the first questions is always whether or not they will be responsible for engaging potential candidates. 

Am I the only person who thinks this is a bit absurd, if not just unhelpful and annoying?

The fact that there is no universally agreed upon definition of what sourcing is when it comes to talent acquisition has always bothered me. Don’t you think it’s well past time to move the ball forward and make the attempt to develop a single definition of “sourcing?”

Historically, sourcing was typically used to refer to talent identification only – name generation, org charting, finding resumes and social profiles, etc. However, I have noticed over the past few years that more people and companies are starting to use sourcing to describe both the identification and the engagement of talent, which aligns with what I’ve always believed sourcing to be.

Let’s take a look at other people’s opinions on what sourcing is and leverage what sourcing is considered to involve when it comes to procurement to see if we can achieve some parity before I share with you my proposed definition of sourcing. Continue reading

Is Boolean Search Boring and Less Effective than Semantic Search?

 

Boolean Search is Boring

Do you think Boolean search is boring, tiresome and ineffective, and that semantic search delivers faster results that count?

I was struck by the image Marc Drees used in his #SOSUEU: the day after post, which you can see above.

I would have loved to sit on that panel discussion and contribute my experience and thoughts on the subject – I was actually supposed to attend and speak but I left the sponsoring company just prior to the event.

Such is life. :)

Regardless, I am happy to weigh in here, and I believe that the majority of people simply aren’t looking at search properly in the first place.

I’ll address the statements from #SOSUEU in order.

Boolean Search is Boring

Let’s hit the reset button first and get a couple of things straight:

If Boolean search is boring, then searching the Internet, Amazon, etc.,  for anything is boring. Any time you use more than 1 term in your search on Google, Bing, Amazon, eBay, etc., you’re using Boolean search. The same is true with LinkedIn and many other sites you can search to find people. Am I alone in this simple understanding?

This may confuse some people, but “Boolean Search” isn’t about Boolean – it’s about search. Searching is about finding things you need and want, and there are many ways that you can search for and find those things. Do you find it more “exciting” to select from a list or check a box on a LinkedIn facet?

Github list and LinkedIn Facet

Whether you type in keywords, select from a list, check a box, apply a filter, etc., all you’re doing is configuring a query to get results to review.

I don’t think there is anything intrinsically “boring” about Boolean operators. I think the real issue is that some recruiters just don’t enjoy searching for people, and if you don’t enjoy something it’s common to find it boring. The same people who bash Boolean search don’t find typing terms into separate search fields, picking from lists and checking boxes exciting or particularly enjoyable.

Some people really like searching databases, social networks, the Internet, etc., for people to engage and recruit. Others would be happy to post jobs and wait for people to come to them and would rather not ever have to search for potential candidates to engage.

To say that Boolean search is boring is to say that carefully looking for and trying to find people (paraphrasing Merriam-Webster’s definition of search) is boring. Semantic search solutions alleviate the boredom of searching for those who find it tedious, because similar to posting a job and getting responses, semantic search solutions often allow you to enter minimal information and get results.

I’d be willing to bet that those same recruiters who don’t enjoy searching to find people to engage also don’t enjoy reviewing responses to job postings as many are unqualified – they would much rather be given a list of well matched people, which semantic search solutions claim to be able to do.

What do you think?

Boolean Search is Tiresome

If you think Boolean search is tiresome, I say you’re lazy.

Why?

Well, for basic Boolean search, we’re talking 2 operators and 2 of modifiers – in many search engines you don’t even need to type AND, as any old space will do.

Is typing in OR, -, ” ” and ( ) really tiresome?

Is filling out multiple search fields (e.g. Twitter below) any less tiresome than typing a couple of Boolean operators? By the way, the common elements of most “advanced search” interfaces are essentially AND’s (All of these words), OR’s (Any of these words), NOT’s (None of these words), and quotation marks (This exact phrase).  Oh wait – that’s basic Boolean, right? Snap!

Twitter Advanced Search Interface

While I love the concept of natural language queries, I actually find writing them tiresome and limiting (e.g. Facebook Graph Search).

Facebook Graph Search Tiresome

Is it less tiresome to use Facebook’s search fields? They’re all essentially linked by AND’s, btw.

Facebook Graph Search Interface

If there is anything that people really find tiresome about any non-semantic search, Boolean or otherwise, is that it requires you to think and expend mental energy.

I know – thinking is tough!

If you’re read a lot of my content over the years, you know I like to bring up the study of information retrieval techniques that bring human intelligence into the search process, otherwise known as Human–computer information retrieval (HCIR).

The term human–computer information retrieval was coined by Gary Marchionini who explained that “HCIR aims to empower people to explore large-scale information bases but demands that people also take responsibility for this control by expending cognitive and physical energy.”

I know the dream is to have computers read our minds so we don’t have to type a single search term – but until that day comes, you should be aware that experts in the field of HCIR do not believe that people should mentally “check out” of the information retrieval process and let semantic search/NLP algorithms/AI be solely responsible for the results.

Having said that, I can see why some people would see the process of building a massive OR string for all of the ways in which a person could possibly reference a certain skill or experience to ensure maximum inclusion as tiresome. It’s exhaustive work – especially if you don’t want to exclude great people who simply don’t reference their experience with the most commonly used search terms.

This is one of the main value propositions of semantic search solutions for people sourcing – through taxonomies and/or NLP-powered AI/algorithms, a user can enter in a single search term and effectively search for other related terms without requiring the user to know and search for all of the other related terms.

Sound great, right? Letting a taxonomies and/or algorithms doing the conceptual search work for you is certainly a lot less tiresome than having to perform research and pay attention to search results, looking for patterns of related and relevant terms to continually refine and improve Boolean searches.

To be sure, there are some solid semantic search offerings for talent sourcing on the market today that make sourcing talent faster and easier for people who find Boolean search boring, tiring, difficult and ineffective. However, to think that semantic search solutions don’t come with their own host of challenges and limitations would be ridiculous.

In case you haven’t see it before, you may want to quickly drive through my Slideshare on Artificial Intelligence and Black Box Semantic Search vs. Human Cognition and Sourcing derived from my 2010 SourceCon keynote to get a high-level overview of some of the challenges faced by semantic search solutions specific to talent sourcing.

If you don’t want to flip through the presentation, here’s a very brief summary:
  • Human capital data/text is often incomplete and widely varied – many people with the same job have different titles, explain their experience using different terms, and in many cases simply do not explicitly mention critical skills and experience
  • Semantic search solutions can only search for what is explicitly stated in resumes and social profiles
  • Taxonomies are difficult, if not impossible to make “complete” and thus they can exclude qualified talent
  • AI/NLP can be useful in determining related terms, but not necessarily relevant terms
  • Many semantic search solutions suffer from “once and done” query execution – there is no way to refine and improve searches or to exclude false positives/irrelevant results

Boolean Search is Ineffective

The effectiveness of Boolean search strings has more to do with the person writing the queries and the sources being searched and less to nothing to do with Boolean logic.

When used in a search, Boolean operators are essentially being used as a very basic query language, and according to Wikipedia, “an information retrieval query language attempts to find…information that is relevant to an area of inquiry.”

Any search a user conducts, whether they know it or not, is essentially a formal statement of an information need.

How effectively a user can translate their information need into a query/search string largely determines the relevance of the results – Boolean logic itself often has little to nothing to do with search relevance!

Assuming a sourcer/recruiter has a solid understanding of  what they’re looking for (a dangerous assumption, by the way – try giving 5 people the same job description and then ask them separately what they’re looking for), the effectiveness of any search they use, whether Boolean, faceted, semantic, etc., is more dependent upon the user’s ability to “explain” their needs to the system/site being searched via an effective query.

For example, let’s say you’re sourcing for a sales leader and you have a military veteran hiring initiative. Regardless of whether you decided to search your ATS (e.g. Taleo), LinkedIn, CareerBuilder, Indeed, etc., you’re essentially asking the same question, “Do you have anyone with experience leading sales teams who is also a veteran?” (among other things – just trying to keep it simple here).

How would you construct a Boolean search for a sales leader who is also a veteran?

How would (and/or could!) a semantic search engine search for a sales leader who is also a veteran?

Ultimately, it comes down to how many ways can someone who has sales leader experience could possibly express that experience on their resume or profile, and how many ways someone who is a veteran could possibly reference their veteran status.

Do you know them all?

Does any semantic search engine know them all? Some don’t know any because they simply aren’t included in their taxonomies. Others could use NLP to find some, but definitely not all. However, a person with decent sourcing skills could produce a veteran query like this one in about 5 minutes (not too tiresome) and continuously improve it:

(Army OR USAR OR “U.S.A.R.” OR “Army Reserve” OR “Army Reserves” OR Navy OR USN OR USNR OR “U.S.N.” OR “U.S.N.R.” OR “Naval Reserves” OR “Naval Reserve” OR “Air Force” OR USAF OR “U.S.A.F.” OR USFAR OR “U.S.A.F.R.” OR “Force Reserve” OR “Force Reserves” OR “Forces Reserve” OR “Forces Reserves” OR Marines OR “Marine Corp” OR “Marine Corps” OR USMC OR “U.S.M.C.” OR USMCR OR “U.S.M.C.R.” OR MARFORRES OR “Marine Expeditionary Force” OR MEF OR “Coast Guard” OR USCG OR “U.S.C.G.” OR USCGR OR “National Guard” OR Veteran OR “honorable discharge” OR “honorably discharged”)

The effectiveness of any search, Boolean or semantic, can be measured by the relevance of the results (e.g., a high percentage of the results are exactly what the searcher is looking for) and the inclusiveness of the results (how many relevant results are retrieved as a percentage of the relevant results available to be retrieved – those available but not retrieved are excluded into the abyss of Dark Matter).

Only the person conducting the search can judge the relevance of the results returned by any search, Boolean or semantic, as relevance is defined as the ability (as of an information retrieval system) to retrieve material that satisfies the needs of the user, and only the user truly knows what their needs are.

When it comes to inclusion, I am aware of some folks who are proponents of “good enough” searches and search solutions (e.g., finding some good people quickly is good enough and there is no need to find all of the best people).

Try telling your company’s executives that you really don’t care about finding the best people available to be found and that you believe that the quickest and easiest to find should be good enough for your company’s hiring needs.

Let me know how that works for you.

Okay, but what about Context and Weighting?

Some folks argue that Boolean search is ineffective due to the fact that Boolean searches are not contextual (e.g. you search for a term and it shows up not in the person’s recent experience, or in their experience at all) and that all terms in a query are given equal weight (e.g., if you search for 10 terms, some terms are likely to be more important than others, but basic Boolean logic doesn’t allow you to differentiate the value/relevance of specific terms).

Admittedly, some Boolean searches are.

However, if you have well parsed/structured data and a search interface that allows you to exploit that structured data, you can use simple Boolean logic to search contextually. For example, most recent/past employer and title, most recent/past experience, etc.

Some search engines do in fact allow you to assign different weights to terms within a single Boolean query (e.g. Lucene, dtSearch, etc.) – this functionality is sometimes referred to extended Boolean search. These same search engines allow you to search for terms in or exclude them from specific areas (e.g. top of the resume, bottom of the resume) via proximity search – functionality that also allows you to perform powerful user-specified semantic search at the verb/noun level to target people with specific responsibilities (have goosebumps yet?).

Okay, that was easy to address.

So, Is Boolean Search Boring and Less Effective than Semantic Search?

For some people, yes – Boolean search is boring, tiresome and less effective than semantic search.

For others, Boolean search is exciting, easy, and more effective than semantic search.

What do I know about any of this?

I’ve evaluated, implemented and used extended Boolean search solutions as well as semantic search solutions. In addition to using them myself on a regular basis, I help 100’s of recruiters use them effectively to find the right people for 1,000’s of real positions. From my practical hands-on experience, I can tell you that sometimes semantic search produces very good results – sometimes it doesn’t. Sounds similar to Boolean, yes?

To all of the “Boolean Bashers” out there – you’re missing the point.

The effectiveness of any Boolean search has more to do with more to do with the person writing the queries and the sources being searched and less to nothing to do with Boolean logic and search syntax

Let’s remember what the goal of sourcing is – to easily find and successfully engage people who are highly likely to be the right match for the roles being sourced/recruited for.

The ultimate sourcing solution would parse resumes and profiles into highly structured data that could be searched via semantic search (autopilot) and extended Boolean (manual control) to ensure that any user could quickly find the right people under any circumstance.

I’m honestly not sure why anyone believes sourcing solutions have to leverage semantic search and exclude Boolean/extended Boolean search capability.

How to Find the Best Software Engineers on Stack Overflow

 

Looking to source and recruit software engineers?

One of the best places to find software engineers is Stack Overflow, where nearly 2,000,000 programmers from all over the world ask and answer programming-related questions.

How would you like to know which software engineers might be the most talented and skilled?

Stack Overflow Main

A year ago, Peter Kazanjy of TalentBin published an extensive piece on how to source talent on Stack Overflow on the SourceCon website. If you haven’t already read his post, I highly recommend you do so before proceeding further.

I am going to go one step beyond Peter’s article and show you how to find software engineering talent by Stack Overflow reputation and badges, which are earned from peers and activity, offering a degree of independent verification of a software engineer’s knowledge, experience and ability. Continue reading

My Future of Sourcing Keynote at Talent42

Talent42 audience viewI recently attended and thoroughly enjoyed the Talent42 conference in SeattleJohn Vlastelica and Carmen Hudson have done a fantastic job, and I was also honored to be asked to present the closing keynote on the current and future state of sourcing.

Aside from the stacked speaker lineup, valuable content, sourcing roundtables led by a good portion of who’s who in the sourcing community, and power + wireless for all (other conference organizers please take note!), what I especially enjoyed about Talent42 is the fact that it is the only technical recruiting-only conference.  My entire career has been focused primarily on the technical recruiting, so it was nice to spend a couple of days in the company of people who share a similar recruiting background and appreciate the unique challenges associated with sourcing and recruiting IT professionals.

As my keynote presentation had a lot of animations, off-slide commentary and embedded videos, I took the time to modify the slide deck so that it could be largely understood that without the benefit of hearing me speak to the content (I wish more presenters would do the same!!!), and I have uploaded it to Slideshare, complete with informative, funny, and controversial YouTube videos.

In this presentation I address what I feel is the current state of talent sourcing as well as what I believe the future of talent sourcing will be, sooner than later.  Additionally, I demonstrate Facebook’s Graph Search and offer insight into functionality from several “Big Data” talent sourcing tools, including Dice Open Web, TalentBin, Entelo, and Gild.

LinkedIn Sourcing Ninja Webinar Recording now on YouTube

 

In case you missed my record-setting LinkedIn sourcing webinar on 6/4 (3,000+ attendees!), the fine folks at LinkedIn recorded the whole session and have graciously uploaded the presentation to YouTube, where you can find the Become a Sourcing Ninja: Earn your Boolean Black Belt with Glen Cathey video.

 

 

Be sure to change the quality to 720 for the best viewing experience.

Content covered includes:

  • Boolean search operators and query modifiers supported by LinkedIn
  • Beyond Boolean – asking better questions
  • Human-Computer Information Retrieval (HCIR)
  • Hidden Talent Pools
  • Diversity sourcing (gender demonstrated)
  • Agile Sourcing Methodology
  • Probabilisitic and Exhaustive Sourcing
  • Sourcing Capability Maturity Model
  • LinkedIn Signal
  • How to automatically find people who have just joined LinkedIn

 

Happy hunting!

 

Analytics, Big Data & Moneyball HR/Recruiting for Dummies

 

Analytics Big Data Moneyball Recruiting for DummiesThe interest in leveraging big data, analytics and Moneyball in HR and recruiting is gaining significant steam.

Ever since my first article on the subject back in 2011, I’ve set up Google Alerts and Hootsuite streams set up to catch any mention of big data, analytics and/or Moneyball in conjunction with HR, sourcing or recruiting,  and the volume of activity is bordering on surprisingly massive and overwhelming, and I’m not the only person to notice this.

Yes, it does seem like everyone is talking about big data in HR.

 

Twitter Big Data Post

 

In 2012, “big data” was mentioned in 2.2M tweets by 980,000+ authors, at a peak rate of 3,070 times per hour!

However, as is often the case with relatively new and nebulous concepts, there is quite a bit of confusion surrounding big data and Moneyball and how they can be applied to HR and recruiting, as evidenced by the obviously incorrect usage of the terms in many cases. It’s also nearly impossible to stay on top of all of the content being generated on the subject (although I am trying my best!).

This is precisely why I’m going to take the opportunity to clear up any confusion by concisely explaining the concepts of big data, analytics, and Moneyball as it relates to HR and recruiting, as well as illustrate some obviously incorrect references to these concepts in recent articles, including those from the Wall Street Journal, Forbes, The Economist, The New York Times, and more.

I’ll tackle analytics first, big data second, and then Moneyball in HR/recruiting, leveraging Slideshare presentations and YouTube videos from experts for support. Continue reading

LinkedIn Catfish: Fake Profiles, Real People or Fake Photos?

 

There have been numerous articles written about fake LinkedIn profiles, and some are really easy to spot because their names aren’t even names.

 

 

Then there are LinkedIn profiles with names that appear real but the profiles are obviously fake.

 

 

This person profile actually has some endorsements. I’m pretty sure this is a picture is of Sophie Turner, who plays Sansa Stark in Game of Thrones (I’m really looking forward to season 3!)

 

 

Next we have LinkedIn profiles that look like real people, at least when it comes to the profile details, but the profiles are likely created by recruiters and perhaps even hiring managers (yes – this happens…stay tuned for a future post on this subject), and the photo is obviously not the photo of the person who created the profile.

And finally, there are LinkedIn profiles that are likely to be real people – where the details of the profile accurately reflect the person behind the profile – but the profile picture isn’t real.

I refer to these profiles as LinkedIn Catfish.

Catfish on LinkedIn

Have you seen the film Catfish or the MTV series based on the film?

The movie is a documentary about the evolution of Nev Schulman’s online relationship with a girl on Facebook who ultimately ends up not being who she was pretending to be online. The television show follows the same format, finding people who are in online relationships with people they’ve never met, performing research on the people, and arranging an in-person meeting to determine if the people are really who they are portraying themselves to be on Facebook.

One of the techniques that Nev Schulman consistently uses on the television show to determine whether or not the people are lying about who they are is Google Image Search in conjunction with Facebook photos.

I’ve posted a few “real or fake” challenges on Twitter from time to time, and while some LinkedIn profiles are obviously fake, others can be quite difficult to determine. I believe some LinkedIn profiles are really examples of “Catfish,” where the people are real but they are using other people’s photos.

How do I know?

From time to time I use Google Images to check LinkedIn profile photos of the people that are sending me invitations to connect as well as some of the profiles that LinkedIn claims are “people I may know.”

I thought I would share some of my findings with you, starting with some obviously fake LinkedIn profiles and progressing to some that I believe are in fact real people who just happen to be using someone else’s image for their LinkedIn profile image.

Let’s start with something I found the other day when I glanced down to the “People you may know” section on LinkedIn.

 

 

When I clicked on Lola’s profile, I found it devoid of any content, which of course immediately makes it suspect.

 

 

Where it gets interesting is when you perform a Google Image Search for that photo – multiple Facebook hits:

 

 

Now let’s take a look at a few LinkedIn profiles of “developers” that I think are really fake profiles created by recruiters.

First is “Alison Cork.”

 

 

If you try searching for Alison Cork using the first name and last name fields in LinkedIn, this profile doesn’t appear to exist anymore.

Taking a look at the “People also viewed” list on the right side of “Alison Cork’s” no-longer-existing profile, I spotted Elizabeth Rose, a “developer at Chevron,” and Danielle Baker, a “web developer at Pfizer.”

 

 

If you click the link to “Elizabeth’s” profile, you’ll see that at least the details all seem to align (date of graduation, data of first work experience, location of school and current location, etc.) – someone took at least a little effort to make this profile seem like a real developer. However, I believe this profile is really the creation of a recruiter looking to use the profile to connect with other developers.

Checking Google Images for the profile photo shows the possible origin:

 

 

“Danielle” below is a similar example.

 

 

If you click the link for this profile, it’s similar to “Elizabeth’s” in terms of being relatively well filled out/detailed.

Performing a Google Images search for “Danielle,” this is what you’ll find:

 

 

Now I’d like to move on to the category of people who *could” be the people with the experience listed on the profile, but they are using someone else’s picture for their LinkedIn profile photo.

For example – this person came up on LinkedIn as someone I might know.

 

 

I blurred the details because this *could* in fact be a real person, and on top of that – they seem to work in sourcing/recruiting. The profile mentions they have worked in recruiting leadership roles at some very prestigious companies, and they have given one (definitely real) person at one of those companies a recommendation (but haven’t received any).  If you’re extremely curious and a tad bit technically savvy, you can probably find this profile – it is public.

When I performed a Google Image search for the profile picture, here’s what is returned:

 

 

So what do you think – is this profile of a real person?

Why the term “Catfish?”

Apparently (at least according to Internet and other lore), the use of the term “catfish” comes from the story about the early days of shipping live cod, where the fish’s inactivity in their tanks during shipment resulted in fish with a mushy texture and bland taste. Someone had the idea to ship the cod with some catfish in the tank, because catfish often conflict with cod in the wild, so during shipment, the catfish would harass the cod and keep them active, resulting in cod with the proper texture and taste, as if they were caught fresh. In the movie, one of the characters theorizes that the person Nev thought he was having a relationship with was like a “catfish” – serving to keep him active, always on his toes, and always thinking.

When you’re on the Internet – even on professional networking sites such as LinkedIn, you always have to be on your toes. Some of the people you’re finding and connecting with may not be who they appear to be, and they might not be real people.

Even so, you may want to connect with some of these folks anyway (as I do in some cases).

Why?

If you fully appreciate and understand the X-degrees of separation concept, there is value in connecting with the “wrong” people because they can actually be conduits to the “right” people. In fact, it could be argued that in many cases, the *only* way to add some of the “right” people you’d like to have in your network  is to connect with the people who are connected with them – even the ones that don’t seem to make sense on the surface.

If you connect directly with a “catfish” profile has been created by a recruiter or hiring manager specifically to connect with software engineers, and they have been successful in connecting to many of them at the 1st degree, then those software engineers would be in your 2nd degree network on LinkedIn. With a free account, you’d be able to see their full names in any people search.

Also, as a 1st degree connection, you have the option to search their connections if they haven’t shut that down (the 2 “developers” above haven’t), and you also have access to their contact details – so if you’re really curious, you could ask them directly about the reality of their profile.

:)

 

Diversity Sourcing is a Breeze with Facebook Graph Search

 

In some respects, Facebook’s Graph Search has literally changed the game when it comes to diversity and inclusion with regard to sourcing.

If you don’t already have access to Graph Search, you may not be aware just how easy it is to leverage diversity criteria such as gender, race and ethnicity.

How easy is it?

I’ll show you how.

Gender Sourcing with Facebook Graph Search

Imagine being asked to find and identify as many female _______________ (accountants, project managers, software engineers, etc.) who currently work at a particular company or any company.

Think about it.

Without Graph Search, how would you go about accomplishing this goal?

It’s no easy task. I know several people who’ve worked at some of the top software companies in the world who have had to do some crazy search gymnastics in the past  in order to even somewhat successfully identify a small portion of female software engineers at target companies in order to diversify their software development talent.

With Graph Search, it’s now simply a matter of asking Facebook for all of the female software engineers or any role you’re targeting at any company.

For example:

 

 

As you can see, sourcing for gender diversity is so easy that Facebook’s Graph Search has essentially rendered it a non-issue, at least when it comes to searching for people by title and/or company.

I have to imagine that this has already been done by sourcers and recruiters at Facebook, Microsoft, LinkedIn, etc.

If not – hello!

Not that it wouldn’t, but this approach also works just as well if you were looking for female engineers in Germany, or any position in any country. Continue reading

Why Facebook Graph Search is No Threat to LinkedIn…For Now

 

Facebook's Graph Search options of special interest to sourcers and recruiters: Employer, Position, Employer Location, Time Period, School, Class Year, ConcentrationAs with all new and bright shiny objects, people are quick and eager to make blind and wild predictions, and Facebook’s Graph Search is an excellent example.

Facebook announced Graph Search on January 15th, and there are already 100’s of articles published on the possibilities, including how Graph Search will challenge Google in advertising, Match.com & eHarmony in online dating, Yelp and others in services, travel and entertainment, and yes, even LinkedIn and Monster in recruiting.

When Mark Zuckerberg himself says “One of my favorite [Graph Search] queries is recruiting. Let’s say we’re trying to find engineers at Google who are friends of engineers at Facebook,” it’s hard to not get excited about the possibilities of tapping into the data Facebook has on over 1,000,000,000 users globally, and over 167,000,000 users in the U.S. alone.

Don’t worry – this isn’t another Facebook-Graph-Search-is-an-awesome-disruptor article.

Rather than throwing fuel on the Graph Search fire, I am happy to throw a wet blanket instead.

Don’t get me wrong – I’m excited to use Graph Search, and I know sourcers and recruiters will be able to make use of it. However, there are some major limitations to Facebook and Graph Search specifically that I want to recognize and bring to light that will clearly explain why it isn’t a threat to LinkedIn. Continue reading

Do you suffer from Obsessive Exotic Sourcing Syndrome?

 

 

Okay, that might have been a bit dramatic, but I do expect a strong negative reaction from some folks because I am going to address an issue that might be a tad sensitive to the sourcing community.

The issue I would like to address is the apparent obsession of many with exotic sourcing.

What is Exotic Sourcing?

If you check out the definition of “exotic,” you will find “strikingly, excitingly, or mysteriously different or unusual.”

Exotic sourcing consists of sourcing methods and technologies that are, yes – you guessed it – “strikingly, excitingly, or mysteriously different or unusual.”

If you’re looking for some examples, here are a few:

What’s the Problem?

I like experimenting with new search engines, deep web searches, and seeing if I can extract sourcing and recruiting value from new, non-recruiting websites sites just like many people do in the global sourcing community. Yes, I’ll admit I’ve poked around Pinterest and Instagram.

So what’s the problem? Continue reading

The Moneyball Recruiting Opportunity: Analytics & Big Data

 

Earlier this year, I traveled to Australia to present a keynote at the Australasian Talent Conference on the topic of the Moneyball opportunity that exists for companies when they are sourcing, identifying, assessing, recruiting, and developing talent, and how big data and predictive analytics will be the next major area of competitive advantage in the war for talent.

Below you will find my keynote presentation, including a couple of YouTube videos.

Big Data and predictive analytics are just beginning to be leveraged in talent acquisition by a few forward thinking companies, and I am convinced they will both play major roles in the near future.

Unfortunately, at this time there is still some confusion around exactly what “Big Data” is and is not. For example, this Wall Street Journal article incorrectly references the use of personality assessments and other online tests to facilitate hiring as an application of Big Data, when in fact it is really just an example of analytics.

Data from personality assessments and online tests coupled with other human capital data doesn’t represent a combination of high-volume, high-velocity, and/or high-variety information assets, which most experts agree is required for something to be classified as “Big Data.”

In this presentation, I think you will find the examples of how companies are currently leveraging analytics in their recruitment as well as in the analysis of their current workforce to be quite interesting, as well as some of the tools that already exist that do in fact harness high volume, high velocity, and high variety information assets.

You may be shocked to find that data supports the finding that taller and more attractive men and women make more money than their shorter and less attractive peers (especially shocked to find out exactly how much more!) – which gives us a glimpse into how people make hiring and promotion decisions on a daily basis based on unconscious prejudice, similar to how unconscious prejudice, wisdom, and “gut” instincts are and have been used in athletic recruiting – which Billy Beane and Paul Depodesta of the Oakland A’s specifically set out to counter.

As demonstrated in Moneyball, very strong teams can be built with data-based decision making, throwing conventional wisdom to the wind.

Enjoy the presentation, and please do let me know your thoughts. Thanks!

 

 

If you like what you’ve seen in the Slideshare, you may want to read this post I wrote on Big Data, Data Science, and Moneyball recruiting last year.

 

Why Boolean Search is Such a Big Deal in Recruiting

In the past, I’ve explained the Boolean Black Belt concept and exposed what I feel is the real “secret” behind learning how to master the art and science of leveraging information systems for talent identification and acquisition.

Now I would like to show you precisely WHY Boolean search is such a big deal in recruiting.

There are 2 main factors:

  1. Candidate variable control
  2. Speed of qualified candidate identification.

The goal of this article is to shed significant light on the science behind talent mining, how it can lead to higher productivity levels (more and better results with less effort), why I am so passionate sourcing, and why everyone in the HR, recruiting, and staffing industry should be as well.

Control is Power

Talent identification is arguably the most critical step in recruiting life cycle – you can’t engage, recruit, acquire, hire and develop someone you haven’t found and identified in the first place.

My experience has shown me that properly leveraging deep sources of talent/candidate data (ATS/CRM’s, resume databases, LinkedIn, etc.) can enable recruiters to more quickly identify a high volume of well matched and qualified candidates than any other method of candidate identification and acquisition (e.g., cold calling, referral recruiting, job posting).

The true power of Boolean search lies in the intrinsically high degree of control over critical candidate variables that using Boolean strings to search deep data sources such as resume databases, the Internet, and social media affords sourcers and recruiters.

Applying that that high degree of control to large populations of candidates – tens of thousands (small internal ATS, niche resume database) to tens of millions (large ATS/CRM, Monster resume database, LinkedIn, etc.) enables adept sourcers to perform feats of talent identification and acquisition most would think impossible.

Continue reading

The Current and Future State of Talent Sourcing

I had the distinct honor and privilege of serving as the conference chair of the biggest-ever SourceCon, held at the Georgia Aquarium in February. Part of my responsibility in that role involved kicking off the event, and I took the opportunity to touch upon my observations and opinions on the current state of sourcing, as well as what I believe will be the future of sourcing.

Even as I was standing on stage I knew I would be writing a post on this topic, because it was apparent that there is much misunderstanding and debate surrounding sourcing, and certainly no shortage of opinion, qualified or otherwise.

If you’re ready, I’ll walk you though my definition of sourcing, my observations on the current state of sourcing, and what I (and others!) see as the future state of sourcing.

WARNING: If you don’t like/have time to read long posts, I suggest you turn back now. While I could have split this content up into 9 weeks worth of 500 word posts, I’d prefer to give you the goods rather than string you along.

What is Sourcing?

First and foremost, I believe it is critical to have a common understanding of what sourcing is.

I define sourcing to include any and all activities whose primary purpose is talent discovery and identification.

My definition is purposefully broad, because I find too many people seem to associate sourcing solely with searching the Internet with Boolean search strings.

While some companies may limit their sourcers to exactly that – searching only the Internet and generating names for someone else to engage – sourcing is and should be much more than that.

Sourcing encompasses the use of any source of human capital data – an ATS, Monster, LinkedIn, Twitter, Facebook, mobile apps, etc., and it can also include the phone, email, and messaging work of engaging potential candidates and networking with them to yield referrals and the opportunity to identify more potential candidates.

Yes, networking with people – whether they be new hires, existing staff and management, or complete strangers – to find and identify potential candidates is also sourcing, regardless of method (electronically, over the phone, or in person).

Of course, sourcing also includes traditional phone sourcing as effectively addressed and dramatically demonstrated at the SourceCon event by Conni LaDouceur.

And finally, although passive and offering little-to-no control over the qualifications and experience of the talent discovered, job posting is even a form of sourcing – the primary purpose of posting a job is to discover talent.

The Critical Importance of Sourcing

When it comes to the entire talent management lifecycle, nothing is more important than sourcing.

That’s because, quite simply, the entire talent management lifecycle is completely dependent upon discovering and identifying potential talent in the first place.

You cannot engage, build a relationship with, recruit, hire, retain and develop someone you haven’t found.

Period.

Try cutting and polishing a poor quality diamond, or better yet – try cutting a diamond you don’t actually have. You could have the best diamond cutters in the world on your staff, but without a steady supply of high quality rough diamonds, you simply won’t be in business.

When it comes to hiring and retaining, all future outcomes are dependent upon that magical moment when a sourcer/recruiter first finds and makes contact with a potential candidate.

The Current State of Sourcing

I believe that sourcing is largely misunderstood, undervalued, and under-invested in today.

I offer as evidence:

  • An alarming number of people seem to believe that sourcing is all about Boolean logic
  • There are people who believe that sourcing is a function that can be easily replaced by software
  • There are well respected companies who don’t give their sourcers or recruiters any premium or purpose-built tools or resources
  • The recently conducted sourcing compensation survey illustrated that 23% of the respondents make less than $40,000 annually Continue reading

Top 15 Common Talent Sourcing Mistakes

Practically everything I have learned about sourcing and recruiting didn’t come from a mentor or any formal training.

Instead, I learned how to become a top performing recruiter “the hard way.”

What that really means is that when it came to finding top talent, I tried a lot of things that didn’t work, and because I refuse to make excuses, give up, or accept anything less than the best results, I kept experimenting until I discovered things that enabled me to find people that others can’t and don’t.

With over fifteen years of experience in sourcing and recruiting, I’ve made my fair share of mistakes along the way. I’ve also had the opportunity to assess, train and coach corporate and agency sourcers and recruiters, which has exposed me to many myths, misconceptions and mistakes when it comes to leveraging information systems for sourcing and recruiting.

Here are what I believe to be some of the most common productivity-robbing and results-reducing mistakes sourcers and recruiters make when looking for the right match.

In no particular order… Continue reading

The Talent Community Conundrum

First it was social recruiting, then it was mobile recruiting.

Now talent communities are apparently the latest cure for all of your talent troubles.

One the surface, the talent community concept seems like a brilliant “no brainer.”

However, like Socrates, I believe there is value in questioning everything. So when I start seeing  a strong buzz about just about anything, I immediately hit it with a dose of healthy skepticism and start asking some tough questions.

I’m well aware that there are talent acquisition leaders out there right now that are saying, “What we really need is a talent community,” primarily because of the buzz the concept has been building over the past year or so. I worry that these same people are placing blind faith into the talent community concept out of the hope that it will help them in some significant manner with their talent acquisition challenges.

When I attended a webinar on building sustainable talent communities the other day, I felt it raised more questions than it provided answers. Because I know I can’t be the only person wondering about the validity of the talent community concept, I thought it would be a good idea to share with you my thoughts and questions. Continue reading

What is Your Talent Sourcing ROI?

Anything worth doing is worth measuring, and sourcing isn’t exempt from this.

If you want to know which method of sourcing has the highest ROI in terms of enabling a person to find more of the right people more quickly, then you’re in luck – because that’s what this post is about.

Human capital data comes in many forms – resumes, social network profiles, blogs, bios, press resleases, etc. – and I have found that a key and critical aspect of sources of human capital data that many people fail to formally recognize is the depth and completeness of the data that can yield information through review and analysis.

When it comes to leveraging information systems such as the Internet, applicant tracking systems, social networking sites, job board databases, etc. for sourcing and recruiting – the operative word is “information.”

Data is the lowest level of abstraction from which information can be derived. For data to become information, it must be interpreted and take on a meaning.

Generally, the quality and amount of information that can be gleaned from any particular source is directly linked and limited to the quality and amount of data present to be reviewed and analyzed. How useful is an information system supported by only a small amount of limited data?

In this post, I will:

  • Review the major sources of human capital data
  • Examine sourcing return on time invested
  • Explore the potential candidate’s point of view
  • Ask you to take a quick sourcing test

Ready? Continue reading

The Guide to Semantic Search for Sourcing and Recruiting

If you have nearly any tenure in HR, sourcing or recruiting, you’ve probably heard something about “semantic search” and perhaps you would like to learn more.

Well – you’ve found the right article.

As a follow-up to my recent Slideshare on AI sourcing and matching, I am going to provide an overview of semantic search, the claims that semantic search vendors often make, explain how semantic search applications actually work, and expose some practical limitations of semantic search  recruiting solutions.

Additionally, I will classify the 5 basic levels of semantic search and give you examples of how you can conduct Level 3 Semantic Search (Grammatical/Natural) with Monster, Bing, and any search engine that allows for fixed or configurable proximity.

But first – let’s define “semantic search.” 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.

Big Data, Data Science and Moneyball Recruiting

With each passing day, an increasing amount of data is being generated and transmitted by and about more people than ever before.

At Google’s 2010 Atmosphere convention, Google CEO Eric Schmidt stated that “There were 5 Exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days.”

In case you were wondering, an Exabyte is 1,000,000,000 gigabytes, or 10,000,000 terabytes. That’s a lot of information.

Interestingly, Google’s CEO may have actually underestimated the amount of data being generated at the time. From their research, RJMetrics believes that a more accurate figure would be approximately 6.6 exabytes every 2 days. One thing is for sure – the number is even bigger today.

What does any of this have to do with recruting? Why should HR, recruiting and sourcing professionals, as well as corporate executives care about big data?

Well, because a chunk of big data is human capital data, and as I have been ranting about for the better part of 3 years, human capital data can be leveraged to identify and hire more great people more quickly.

If you’re a dinosaur recruiter or sourcer, I don’t recommend you read the rest of this post, because:

  1. I will challenge they way you think and work, and that might make you uncomfortable
  2. You’ll probably think it’s a load of garbage
  3. It might make you aware of your pending extinction (the precise timing of which is debatable)

I have to warn you that this is not a short, quick-hit post – this may be the longest single post I have ever written, which explains why you didn’t see a post from me last week. I wrote this piece to introduce a human capital paradigm shift, to challenge the long-standing conventional wisdom in HR and recruiting, and to (hopefully!) provoke progressive thought from my peers. If that’s not your thing, turn back now.

If you want a glimpse into the future of talent identification and acquisition, you’re always interested in figuring out how your company can gain a competitive advantage, and you’re wondering what the heck my “Moneyball recruiting” reference could possibly be about, then read on.  Continue reading

Talent Sourcing: Beyond Tips, Tricks, Hacks and the Internet

It’s bothered me for quite some time now that many people essentially equate sourcing with Internet search – using search engines such as Google and Bing to find resumes, lists, press releases, etc.

It bothers me because sourcing is so much more than that.

It also bothers me because I am aware that many companies (some quite large and well respected) limit their sourcers and recruiters primarily to the Internet as the only source of information.

I believe a major contributing factor as to why sourcing isn’t highly valued by some organizations and why sourcing doesn’t get as much widespread respect and recognition as it should is because too many people associate sourcing primarily with Internet search.

The future of talent sourcing will involve a shift from manual Internet search and ATS/CRM systems with only rudimentary search and analysis capability to highly specialized tools specifically designed for mining vast and proprietary human capital data sets dynamically compiled from multiple sources that enables predictive analytics.

It’s coming – will you be ready? Will you be ahead of the curve or behind it? Continue reading