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?

ATS, Job Board Resume Databases

Resumes typically represent the deepest source of human capital data.

While the accuracy of them can be argued (albeit no differently than social media profiles) – most resumes contain significant and specifically professional information about the people who wrote them.

Even when poorly written, most resumes contain:

• A summary of experience
• Objectives that can give you insight into the types of opportunities they are interested in
• A work history that can give you an idea of their capabilities based on their past responsibilities and experience at specific companies, as well as an educated guess as to their desired compensation
• A full address, which can be critical in making an educated guess at whether or not they might be open to a particular commute

LinkedIn Profiles (CV’s?)

LinkedIn is the one stand-out social networking application that has a decent number of profiles with deep human capital data.

Although not a resume database, you can typically find (and thus search for and target) more employment qualification-related information than anywhere else outside of an actual resume database.

While LinkedIn calls them “profiles,” and some contain very little information other than 1 title and 1 employer, some LinkedIn users fill their profiles out just as they would their resume.

In fact, with the employment market in relatively bad shape, there are a number of articles advising job seekers to do exactly that – fill out their profile as they would a resume.

And now, LinkedIn even offers the ability to convert your LinkedIn profile into a resume(fantastic move, by the way!).

It also doesn’t hurt that LinkedIn has a robust search interface, supporting full Boolean logic as well as a number of LinkedIn-specific advanced search operators. Great search interface + deep human capital data = highly leveragable information system for talent identification.

Of course, it can’t be overlooked that there are more incomplete and shallow LinkedIn profiles than there are complete and fully fleshed out profiles, so all is not perfect in LinkedIn land.

Additionally, while LinkedIn has started to add some more specific location options for people to select (for example, my zip code gives me the option to select Alpharetta or Atlanta), many people still use their major metro area as the location on their profile (I do).

This can make it difficult to find people who are likely to be close to the location of the job you are sourcing for and thus “recruitable.”

What About Facebook, Twitter, and Google+?

While many people in the recruiting and staffing industry get REALLY excited about Facebook, Google+ and Twitter - I don’t.

Before you recoil in absolute horror that I haven’t jumped on the bandwagon with everyone else, let me say that I’m a big fan of leveraging  any/all social networking sites (provided your target talent uses them to a good degree, of course).

Yes, I they’re cool, and yes, I use them.

However, I refuse to get so blinded by their perceived potential and the hype in the sourcing/recruiting community that I fail to see their limitations.

You can certainly use Twitter, Google+ and Facebook to identify and contact potential candidates – there’s no arguing that. While Twitter is highly searchable, supporting Boolean queries and their own set of advanced search operators, Facebook isn’t (although it does offer you access to the largest single repository of people on the planet), and Google+ isn’t nearly as searchable as it should be given that it’s a Google creation.

However, regardless of “searchability,” none of those sites offers much professional data about the people who use them, or at least not the right types of information that can help a sourcer or recruiter gain any significant insight into specific skills, experience (including precise responsibilities and capability as well as overall years and career progression), and specific location.

You might get lucky to see a title on Facebook, Twitter, or Google+, and you might find people talking about their line of work, but the people who do mention titles and in some cases even employers is the vast minority.

Lastly – when it comes to social networking sites like Google+, Facebook, and Twitter, even when people do mention something work related online that can enable you to try to guess what it is they do, in many cases they do so using non-standard terminology, which poses an additional challenge to talent identification.

Shallow Human Capital Data

Facebook, Twitter and Google+ can be effectively leveraged for employer and recruiter branding, marketing, online community development, and socializing job opportunies (that’s social media speak for “job posting”) – which are largely passive methods of talent attraction.

However, as shallow sources of human capital data, Facebook, Twitter and Google+ are not particularly effective for active candidate identification.

When I say “active candidate identification,” I’m not referral to job seeking status (people actively seeking employment) – I’m referring to the process of actively searching for and identifying candidates with specific experience and qualifications that are highly likely to match specific hiring needs.

Posting jobs is a passive method of identifying potential candidates, because you post the job and then sit and wait for people to do the work of identifying themselves.

There is no doubt you can find and contact LOADS of people using Facebook, Twitter and Google+. However, in most cases, you have no real idea how much and exactly what kind of experience these people have prior to contacting them, and in many cases, you don’t know precisely where they live.

Just because they list that they have their CPA, or that they belong to a nursing association, or they are a “fan” of a PHP developer page - it certainly does not guarantee you of anything beyond that.

Non-Resume Internet Research

Using Internet search engines such as Google, Bing, Blekko, et al, to search for and sift through human capital data can definitely produce results.

I won’t argue that. However, once you go beyond resumes (the deepest sources of human capital data), you quickly enter the shallow end of the human capital data pool - press releases, blog posts and comments, articles, etc.

I would never suggest that these shallow data sources can’t be leveraged for sourcing and recruiting – but my point is that the intrinsic probability that any particular non-resume search result is qualified for your hiring needs is LOW.

This is because less data means less information available to be gleaned about the potential candidate – leaving us with little to no idea as to their professional experience and qualifications, and even specific location in many cases.

Expect a Return on your Time Invested

Maybe some sourcers and recruiters like to find and contact lots of people because they get paid to just be social and make lots of friends online.

Maybe some companies think it’s productive and cost effective to sift through and contact large quantities of people who aren’t qualified for, would not be interested in, and/or would not commute (or relo!) to the opportunity they are being sourced/identified for.

I certainly don’t! Who does anyway?

Wait – please don’t raise your hand (not you – that other person).

As shallow sources of information, Facebook, Twitter, Google+ and practically all sources of non-resume human capital data on the Internet simply don’t have much professional-experience/qualification-relevant information.

Less and incomplete data doesn’t really make for a heavily leverageable information system.

At least not when it comes to talent identification where it’s more than helpful to know a little bit about someone’s experience before you contact them.

Value to the Candidate?

Candidates generally appreciate being contacted for opportunities that are in their “ballpark” when it comes to location and responsibilities.

Most candidates don’t appreciate being contacted for opportunities that aren’t.

Think about this for a second – what VALUE are you providing to people that you find and contact using shallow sources of human capital data when they are in fact not even remotely qualified or interested in your opportunity?

Most people don’t appreciate being contacted by recruiters only to end up being used as a tool in your networking/referral recruiting efforts because you didn’t have enough information about them to possibly provide anything of value to them.

Yes, I remember the days of just picking up the phone and calling people with little to no information – but take a second to answer this question: Is this kind of practice and process the best and highest ROI method of sourcing and recruiting?I think not.

Critical Candidate Matching Variables

Deeper and more detailed human capital data enables more precise and controlled searches, allowing sourcers and recruiters to be able to make an educated decision to contact people based on capability and experience rather than blind faith or a guess based on perhaps a title alone.

With resumes or fully fleshed out LinkedIn profiles, a talented sourcer or recruiter can effectively control critical candidate variables such as location, potential opportunity match, and experience/capability – including years of experience, which can tie into compensation.

Sourcing Test: Which Person is More Likely to be Interested and Qualified?

Here is a dramatic and certainly more practical example of deep vs. shallow human capital data: If you were responsible for filling a position for a Business Analyst with energy industry experience and specific experience working on SAP projects and using UML, which of the following people has the higher probability of being both qualified and interested in your opportunity?

Person #1: LinkedIn Profile

Person #2: Twitter Bio

The contrast is dramatic.

The LinkedIn profile is essentially filled out as completely as a resume would be, and as such, we can feel confident when contacting this person because their experience appears to closely align with our opportunity, and even if they aren’t recruitable, they’ll have to admit the opportunity was relevant.

The Twitter profile mentions the title of “Business Analyst,” but little else – we have no idea as to this person’s industry or project experience. While we can cross reference the Twitter Bio with LinkedIn, when doing so, we can see by looking at her profile that she does not appear to have any energy industry experience, and we cannot tell if she has any SAP project or UML experience.

If you had a choice between using either an information system that had shallow data on the people contained within, or an information system that had deep data on the people contained within - and you could only choose one – which would you choose and why?

I know which one I would choose – all things being equal, I would choose the information system with the deep and more complete human capital data.

That way, I can run creative and effective queries to search for, find, and contact people based on specific experience and qualifications. Why would anyone choose any different?

Final Thoughts

You can find and hire people by searching any source of human capital data – resume or otherwise.

However, searching Facebook, Twitter, Google+, blogs, the Internet and other similarly shallow sources of human capital data requires a higher amount of effort for a smaller return – what I call low yield sourcing and recruiting.

While there is undoubtedly more shallow human capital data than deep human capital data, does it sound like a good idea to go out of your way to focus on low yield sourcing and recruiting?

When it comes to proactive candidate sourcing (e.g., searching for people and not posting jobs and waiting for responses), I’d argue that the deep sources of human capital data such as resume databases, applicant tracking systems, LinkedIn, and Internet resumes are responsible for producing 80% of the search based sourcing and recruiting results (hires).

Conversely, the shallow sources of human capital data such as Facebook, Twitter, Google+ and and non-resume Internet research produce 20% of the active-search based sourcing and recruiting results. You essentially have two paths:

1. Find and contact more uninterested and unqualified people
2. Find and contact more interested and qualified people

Which one will you take?

Does your employer give you a choice?

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

The trouble with search today is that people put too much trust in search engines – online, resume, social, or otherwise.

I can certainly understand and appreciate why people and companies would want to try and create search engines and solutions that “do the work for you,” but unfortunately the “work” being referenced here is thinking.

I read an article by Clive Thompson in Wired magazine the other day titled, “Why Johnny Can’t Search,” and the author opens up with the common assumption that young people tend to be tech-savvy.

Interestingly, although Generation Z is also known as the “Internet Generation” and is comprised of “digital natives,” they apparently aren’t very good at online search.

The article cites a few studies, including one in which a group of college students were asked to use Google to look up the answers to a handful of questions. The researchers found that the students tended to rely on the top results.

Then the researchers changed the order of the results for some of the students in the experiment.  More often than not, they still went with the (falsely) top-ranked pages.

The professor who ran the experiment concluded that “students aren’t assessing information sources on their own merit—they’re putting too much trust in the machine.”

I believe that the vast majority of people put too much trust in the machine – whether it be Google, LinkedIn, Monster, or their ATS.

Trusting top search results certainly isn’t limited to Gen Z – I believe it is a much more widespread issue, which is only exacerbated by “intelligent” search engines and applications using semantic search and NLP that lull searchers into the false sense of security that the search engine “knows” what they’re looking for.

This is Your Search Without a Brain

It’s easy to see why people and companies create search products and services using semantic search and NLP that claim to be able to make searching “easier” – they are looking to sell a product  based on the value of making your life easier, at least when it comes to finding stuff.

If you take a look at some of the marketing materials for intelligent search and match search products, you’ll find value propositions such as “Stop wasting time trying to create difficult and complex Boolean search strings,” ”Let intelligent search and match applications do the work for you,” and “A single query will give you the results you need – no more re-querying, no more waste of time!”

I love saving time and getting to what I want faster, but my significant issue with “intelligent search and match” applications is that they try to determine what’s relevant to me.

And that’s a rather large issue, because only I know what I am looking for.

It’s critical to be reminded that the definition of ‘relevance,’ specifically with regard to information science and information retrieval, is “how well a retrieved document or set of documents meets the information need of the user.”

The only person that can make the judgment of how well a search result meets their information need is the person conducting the search, because it’s their specific information need.

Any reference to “relevance” by a search engine, whether it be Google, Bing, LinkedIn, Monster, etc., is based purely on the keywords, operators, and/or facets used.

Search engines don’t know what you want – they only know what you typed into or selected from the search interface.

Poor use of keywords, operators or facets will don’t stop you from getting results. All searches “work,” as I am fond of saying – but the quality or relevance will likely be low.

Of course, that assumes that the person conducting the search is actually proficient at judging the quality or the relevance of the results – comparing results to their specific information need and experimenting with different combinations of keywords, operators and facets to look for changes in relevance.

Related Does Not Equal Relevant

I personally never implicitly trust first page or top ranked search results online, nor top ranked results on LinkedIn, Monster, or anywhere I search. Some of the best search results I have ever found were buried deep in result sets – far past where most people would typically review, and essentially in the territory of results the search engine deemed least “relevant.” Pshaw!

One reason for this is because I understand that any search engine I use, no matter how “dumb” (straight keyword matching), or “intelligent” (semantic/NLP), they can only work with the terms I give it. What do you think the  most “intelligent” search engine can do with poor user input?

When it comes to searching, unfortunately everyone’s a winner, because every search “works” and returns results.  The problem is that few searchers know how to critically examine search results for relevance.

Regardless how how “intelligent” a search engine might be, it can only try to find terms and concepts related to my user input.

This is an often overlooked but critical issue – just because terms might be related, it does not mean they are relevant to my information need.

It certainly helps to understand that some of the most relevant search results can’t actually be retrieved by the obvious keywords, titles or phrases, or even those that a semantic search algorithm deems related to them. In fact, some of the best results simply cannot be directly retrieved – see my post on Dark Matter for more information on the concept.

However, to appreciate the concept that no single search, no matter how enhanced by technology, can find all of the relevant (by human standards and judgment) results available to be retrieved, you have to know a thing or two about information retrieval in the first place.

And if you already lack the ability to critically judge search results and evaluate them for relevance, how can you be expected to be able to evaluate and critically examine the search results returned by intelligent search and match applications?

The “black box” matching algorithms of intelligent search and match applications pose significant issues to users in that searchers have absolutely no insight as to why the search engine returns the results it does. Without this, what option does a user have other than to implicitly trust the search engine’s matching algorithm?

Searching Ain’t Easy

Who says search has to be easy anyway?

Just because you might want it to be, should it be? Does it have to be?

Let’s face it – a lot of people look for the easy way out. The sheer volume of advertisements pushing diet supplements that claim you can lose a ton of weight without having to watch what you eat and exercise is evidence that people want to get the results they want without working for them.

You know the best way to lose weight? A healthy diet combined with regular exercise. The problem is that eating healthy and exercising regularly is that it requires discipline and hard work.

I’m not saying there isn’t a better way to search – I am a fan of Thomas Edison’s belief that “There is always a better way.”

However, I believe that the better way, specifically when it comes to information retrieval, involves discipline and the hard work of people using critical thought in the search process – not short-cutting or completely removing it from the equation.

And I am not alone.

There is already considerable work being done to create new kinds of search systems that depend on continuous human control of the search process. It’s called Human-Computer Information Retrieval (HCIR) - which is the study of information retrieval techniques that bring human intelligence into the search process.

Truly intelligent search systems should not involve limiting or removing human thought, analysis, and influence from the search process – in fact, they should and can involve and encourage user influence.

When you break it down, the information retrieval process has 2 basic parts:

1. The user enters a query, which is a formal statement of their information need
2. The search engine returns results

The key, in my opinion, is that the search engine should return results in a ”Is this what you were looking for?” manner and allow you to intelligently refine your results, as opposed to a “This is what you were looking for” manner.

There’s a BIG difference.

The former begs for user influence and input, the latter does not – it makes the assumption that it found what you wanted

The bottom line is that no matter what you are using to search for information, only you know what you’re looking for and therefore judge the relevance of the search results returned.

Intelligent search isn’t easy, because you actually have to think before and after hitting the search button.

The Intelligent Search Process

As I have written before, searching should not be a once-and-done affair – there is no mythical “once search to find them all.”

Searching is ideally an iterative process that requires intelligent user input.

Here is an example of an intelligent, iterative search process applied to sourcing talent:

1. Analyzing, understanding, and interpreting job opening/position requirements
2. Taking that understanding and intelligently selecting titles, skills, technologies, companies, responsibilities, terms, etc. to include (or purposefully exclude!) in a query employing appropriate Boolean operators and/or facets and query modifiers
3. Critically reviewing the results of the initial search to assess relevance as well as scanning the results for additional and alternate relevant search terms, phrases, and companies
4. Based upon the observed relevance of and intel gained from the search results, modifying the search string appropriately and running it again
5. Repeat steps 3 and 4 until an acceptably large volume of highly relevant results is achieved

Anyone can enter search terms and hit the “search” button, but not everyone can effectively and intelligently search.

Until you’ve witnessed intelligent and iterative search in action, you likely wouldn’t know the difference between “great” search results, “good” search results and “bad” search results.

It’s as dramatic as the difference between and experienced professional offshore fisher, a recreational fisher, and someone going offshore fishing for the first time.

The ocean holds the same fish for everyone fishing it. While a first-time or recreational fisher can get lucky every once in a while, only a person who really knows what they’re doing can get “lucky” on a consistent basis and catch the fish  the recreational fisher only dreams of catching.

Final Thoughts

The ability to enter in some search terms and click the “search” button doesn’t convey any supernatural search ability, but it does certainly make people feel like they are good at searching, because unless you mistype something, everyone’s a winner.

Ultimately, search engines of all types retrieve information, but information requires analysis, and only humans can analyze and interpret for relevance.

Eiji Toyoda, the former President of Toyota Motor Corp., has observed that “Society has reached the point where one can push a button and immediately be deluged with…information. This is all very convenient, of course, but if one is not careful there is a danger of losing the ability to think.”

Critical thinking is perhaps the most important skill a knowledge worker can possess.

The reason why so many people stink at search is because most people simply don’t think before or after they search, and they place too much trust in the machine.

Additionally, the quality of the search terms/info entered directly affects the quality of the results. “Garbage in = garbage out” certainly applies here. And effective searching is rarely a “once and done” affair – the ability to critically evaluate search results for relevance and successively refine the search criteria to increase relevance is the key to true “intelligent search.”

“Black box” matching algorithms can be wonders of technology and engineering, but they pose significant problems in that searchers have absolutely no insight as to why they return the results they do, and in many cases, the engineers creating these semantic/NLP matching algorithms assume they know what their users are looking for better than the users themselves. I’m sorry if I am the only person offended by such an assumption.

Okay, I’m not sorry.

I love technology, and I use and have used some of the best matching technology available, but also I know it’s not a good idea to try to limit or remove intelligent critical thinking from the search process and completely replace it with matching algorithms.

The term human–computer information retrieval was coined by Gary Marchionini whose main thesis is 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.” (emphasis mine)

For those who simply want information systems to magically provide them with the most relevant results at the click of a button, you should take special note of the fact that experts in the field of HCIR do not believe that people should step out of the information retrieval process and let semantic search/NLP algorithms/AI be solely responsible for the search process.

If you want to get better search results, use the latest technologies, but don’t put too much trust in the machine.

Instead, put some skin in the game, take responsibility for the search process, and expend some cognitive energy critically thinking through not only your search input, but also the results for relevance.

“In the age of information sciences, the most valuable asset is knowledge, which is a creation of human imagination and creativity. We were among the last to comprehend this truth and we will be paying for this oversight for many years to come.” — Mikhail Gorbachev, 1990

Strictly For the Search Geeks

Check out this HCIR Challenge, and at least read the  introduction which compares and contrasts precision vs. recall, and references iterative query refinement.

I read an article on ERE about the other day titled “Love Writing Boolean Instead of Recruiting? Then Don’t Read This Post.”

While I happen to be pretty good at and thoroughly enjoy writing Boolean queries for talent mining, I actually love the entire recruiting life cycle. Sourcing is a means to an end, not a means in and of itself for me. Even so – with such a provocative post title (nice work John!), I had to read the article.

The article is a pretty strong pitch for Scavado, which “does the search work for you, saving hours of time otherwise spent developing Boolean search strings and applying them manually to each site searched.”

Things really got interesting when I got down to the comments on the article, as I stumbled into an interesting exchange between Amybeth Hale and Keith Halperin which covered direct sourcing, referral recruiting, and outsourcing sourcing at $6.25/hour.

Read on to learn my thoughts on all of the above.

Sourcing vs. Recruiting?

I’m not exactly sure where the whole concept of sourcing vs. recruiting comes from, but I do find it interesting that some people think that people who source spend more time writing Booelan search strings than they do recruiting.

I think it comes mostly from people who either don’t know how to source candidates via ATS/CRM systems, resume databases, social media and the Internet, or just aren’t that good at it.

For anyone who is remotely adept at sourcing, the actual process of creating and refining Boolean (and Faceted search on LinkedIn) takes less than 10% of their time. For me, that number is less than 5%. On an average recruiting day, I might spend 15 – 20 minutes or so per day working specifically on building and refining Boolean search strings.

That’s about 3.1% to 4.2% of an 8 hour day.

Care to guess what I’m doing the other 95.8% – 96.9% of the time? If you guessed recruiting and NOT writing Boolean search strings, you’d be right. The author of the ”Love Writing Boolean Instead of Recruiting? Then Don’t Read This Post” article mentioned that the creator of Scavado “got tired of spending more time writing search strings than calling prospects.”

If someone is spending more time writing Boolean search strings than calling potential candidates, something is seriously wrong.

Of course, if you’re dealing with low quality/shallow data (the Internet) and poor search interfaces/capability (many ATS’s and some search aggregators), that time might expand somewhat – but it should never be a significant chunk of any given day. No one should be asked to be fast, efficient and highly productive if they are stuck with using only free sources and a practically unsearchable ATS – but that’s a topic for a future post.

Oh, and you did know that there are more recruiters and HR professionals who source candidates than sourcers who source candidates, didn’t you? Just one more reason I am confused by the sourcing vs. recruiting mentality.

Ultimately, the whole point of becoming more facile with information retrieval (Boolean search, faceted search, semantic search, talent mining, etc.) is to be able to more quickly identify and engage people to assess their potential as candidates and/or get referrals.

More on that later.

Outsourcing Sourcing

There is nothing intrinsically wrong with outsourcing sourcing, but I love to hear of people using resume and lead sourcing services at rates as low as $6.25/hour.

When you pay $6.25 – $15/hour for sourcing, you’re essentially getting a resume scraping service, which literally scrapes the surface of the talent pool in the sources being searched. This level of sourcing is what I refer to as Level 1 Talent Mining (with perhaps a sprinkle of Level 2), which essentially finds what everyone else finds with basic and imprecise searches. The proverbial tip of the iceberg, offering no competitive advantage.

If anyone can hire all of the people they need to, at the level of candidate quality and at the speed needed using this level of sourcing, then more power to them.

One thing to think about, however, is that you may be paying $6.25 per hour and be billed for an hour that was really 5-10 minutes of someone’s time. Some of these folks may be really making $37.50 –  $62.50 an hour for their services.

Think about it.

Boolean Search Strings vs. Referrals

I’m going to take this post as an opportunity to clear the air with regard to sourcing (Boolean et al) vs. referrals.

They are not mutually exclusive.

When searching internal ATS/CRM systems, job board resume databases (e.g., Monster, Dice, Careerbuilder, Indeed), LinkedIn, the Internet, etc. – the results returned by the searches are merely first-order results, and only represent a fraction of the talent that can ultimately be reached and actualized.

Any sourcer/recruiter worth their salt is not only looking to potentially recruit the people directly returned by their searches (first-order results),  but also tap into the networks of those people (second-order results, third-order results, etc.).

In that sense, any source that can be searched can be viewed similarly to LinkedIn, as each person directly retrieved via any search method or source knows people who know other people, and so on.

Here’s the bottom line: Sourcing via Boolean search strings or any other method of information retrieval affords referral recruiting opportunities.

In fact, the more effective and efficient you are at sourcing, the faster you can crowdsource your hiring need. Yes, strong sourcing actually accelerates and multiplies any sourcer’s/recruiter’s referral recruiting opportunities.

If you’re searching ANY site/source and your’re not tapping into the second- and third-order+ results available (each person’s direct and extended network), leveraging everyone you contact for networking and referrals, you’re not doing your job as effectively as you could.

Are Referrals Really the #1 Source of Hire?

Gerry Crispin’s CareerXroads fantastic Source of Hire data was mentioned and linked to in the comment exchange between Amybeth and Keith, so I decided to take a peek (again).

Notice that 27.5% of hires came from referrals. No surprise there, right?

Stick with me.

If you keep moving through the report, you’ll find that 45% of the respondents attributed all of their referral hiring from employee referrals.

That means a good chunk of referrals come from non-employees.

Where am I going with this?

You’ll notice that the #2 source of hire was job boards, at 24.9%, nipping on the heels of referrals (27.5%) as a source of hire.

Yeah, I know – I thought job boards had been killed years ago by social media and referrals. Who knew?

Humor aside, if you keep moving forward to Figure 15, you can see that 52.8% of firms said that their job board hires were predominantly from postings and not resume searches.

That still leaves a good percentage of job board hires coming from resume searches.

So let’s say you search Monster (via Boolean queries) for potential candidates, contact someone who turns out to not be available to consider making a move at this time, you sell your opportunity to them and ask who they could recommend for the role, and they refer you someone.

Let’s say that the person referred to you is a great fit and eventually gets the job.

What’s the source of the hired candidate?

Monster?

Referral?

What’s the probability that it’s coded properly in the ATS?

Who knows – if every referral hired that came from people sourced through job board, ATS, Internet or social media searches was actually coded properly and specifically, referrals may not actually be the #1 source of hire.

What is Direct Sourcing Anyway?

In the exchange between Keith and Amybeth, it was asserted that direct sourcing represents only 5% of hires, and it seemed to me that Boolean search was somehow being tied to the concept of “direct sourcing.”

I don’t consider searching my ATS or a job board resume database such as Monster or Dice for potential candidates to engage and hire to be direct sourcing, and I am not alone – take a look at the CareerXroads data regarding direct sourcing:

So a solid chunk of the searching (via Boolean queries or otherwise) of internal ATS/CRM systems and job board resume databases to identify and engage potential candidates isn’t really a part of the 5% of direct sourcing, and could in fact be a significant contributor to the “Job Boards” source of hire. Which, I might remind you, is 24.9%.

Also, it is interesting that “ATS” isn’t its own source of hire in the survey – could it be lumped into the “Career Site” source?

With regard to ATS search (Boolean queries or otherwise), we could easily run into source of hire coding accuracy issues.

For example, let’s say you search your ATS for potential candidates and find an old resume from someone who responded to a Monster ad over a year ago. Let’s say you make contact with this person, recruit and hire them.

What’s the source of the hired candidate?

Monster?

Your ATS?

See the source of hire coding challenge?

Either way, the hire came from a search. Likely Boolean.

Final Thoughts

I completely understand and appreciate the sell and positioning of products and services that “perform your searches for you” – not everyone wants or needs to know how to leverage information systems for talent identification, nor is everyone capable of doing so effectively.

However, as I have written and spoken about many times before, any attempt to automate information retrieval without human influence in the querying process has significant limitations and issues. Of course, it certainly doesn’t help that the people who are drawn most to automated solutions are the least equipped to be able to test the claims made by those who are selling automated search solutions.

I hope I’ve made a dent in the sourcing vs. recruiting issue – it’s not an either/or relationship. Sourcing is a critical part of recruiting – you can’t engage, recruit and hire someone you haven’t identified in the first place.

Posting jobs only attracts active candidates, and referrals only account for 27.5% of external hires – so if you’re going to try and recruit people who haven’t found your job and can’t be reached through employee referrals, you can find and target passive candidates (and even those who aren’t looking but can be recruited!) by searching ATS/CRM systems, job board resume databases, LinkedIn, Facebook, Twitter, the Internet, etc.

Additionally, searching information systems for potential candidates affords you the opportunity to tap into second- and third-order results – the networks and connections of the people you find directly from your searches, increasing and accelerating your referral recruiting opportunities.

Not everyone has to be interested in or capable of searching databases, social media and the Internet to source potential candidates, but there is no denying that the volume of and speed at which human capital data is being generated poses a huge opportunity and need.

For example, some really smart people have been talking for quite some time about the latent power of data, and more specifically human capital data.

Here’s the thing about data – data requires analysis for insights, intelligence, and decision making, but data can’t be analyzed until it’s retrieved.

And the simplest form of information retrieval involves Boolean logic, whether it’s in your face or behind the interface.

Oh – and one last thing…if you’re spending more time creating and refining Boolean search strings than engaging candidate prospects, I’d advise you to get a mentor and perform some deliberate practice to get better and faster at information retrieval, perhaps invest in some training, or if you have no desire to get better at sourcing – outsource your sourcing to specialists if that’s a viable option.

While there is much written on the subject of how to search the various talent sources available to recruiters and sourcers today, such as the Internet, LinkedIn, Facebook, Twitter, Google+, ATS/CRM systems, etc., there does not seem to be much written about their ROI as sources of talent/human capital information.

I believe that the value of any source of information is 50% based upon the actual information contained within (data depth), and 50% in the ability to extract out precisely and completely what the user needs (searchability). Information has no value if you are unable to easily access, effectively search for and find what you need and take action on it.

When it comes to leveraging information systems for talent identification and acquisition, it is critical to assess the depth of the talent/human capital data offered by the source as well as how “searchable” the source is.

Why is Data Depth and Searchability Important?

Quite simply, the deeper the data offered by and the more searchable the the source is, the higher the ROI for your sourcing efforts.

All electronic sources of talent are NOT created equal, and some offer sourcers and recruiters instrinsic advantages with regard to the ability to more quickly and precisely find more of the right people, yielding higher productivity.

I’ve created a graphic representation of a comparison of the data depth and searchability of the most common information systems used by sourcers and recruiters to find candidates.

Shallow Data Depth

The whole point of using information systems to search for candidates is to find people who have specific skills and experience, and typically people who live in a specific location.

It’s not terribly difficult to find PEOPLE, but it can be very challenging to find the RIGHT people.

As you can see from the chart above, I’ve classified Facebook, Google+, Twitter, and the Internet (non-resume results, such as press releases, company directories, etc.) as shallow sources of talent data.

This is because these sources either don’t offer much in the way of professional/occupational information (often a title and little else), and/or they have very little information as to the exact location of the potential candidates. In most cases, they contain very little information regarding critical candidate variables such as skills and responsibilities, quantity and quality of experience, career history and accomplishments, education (Facebook being the exception), precise location, etc.

Many shallow sources of candidate information simply do not provide ANY information regarding some of these details. This is because the majority of people who use sites like Twitter and Facebook often don’t include professional/employment information on their profile.

With little or no information to go on, it is extremely difficult to search for and identify candidates who have a high probability of at least meeting the minimum requirements for your opening, let alone exceeding them.

Low Searchability

While you can certainly search Facebook to find people – Facebook has significant limitations with regard to its “searchability.” Facebook simply was not designed to be highly searchable, at least not to find people you don’t know, and certainly not based on professional info beyond title and employer.

You may think I am crazy for saying that the Internet isn’t very “searchable,” but most search engines, including Google, don’t even support full Boolean logic.

Also – the Internet itself is unstructured, which makes it intrinsically difficult to find exactly what you’re looking for without drowning in a sea of false positive results. Sniffing out and following candidate leads based on shallow information and using sources that aren’t by design highly searchable comes with the territory of being a sourcer or recruiter, and the thrill of the hunt can be quite satisfying.

However, the angle of this article is ROI, or even more specifically ROTI (Return on Time Invested) – which is a good measure of productivity (Productivity + Work / Time).

Trying to search shallow data sources with limited search capability can be an incredibly slow and time consuming process, as well as result in a significantly low return on time invested. I refer to this as “low yield” sourcing, and its cause is the shallow depth of information available and poor searchability of the sources – which cripples your ability to control or even identify critical candidate variables.

Deep Data But Low Searchability

In this quadrant we have many ATS/CRM solutions, as well as Internet resumes.

In both cases, we’re dealing with resumes. Resumes are definitely deep sources of talent data – and while they are not always complete or 100% accurate – most resumes do contain significant information about the people who wrote them. Even when poorly written, most resumes contain summaries of experience, objectives that can give you insight into the types of opportunities they are interested in, a work history giving you an idea of their capabilities based on their past responsibilities and experience, and of course an addresses – which can be critical in making an educated guess at whether or not they might be open to a particular commute.

While this deep level of talent data is wonderful – it’s of little use if your ability to search for and retrieve the data is limited. Unfortunately, many ATS/CRM solutions aren’t very searchable.

In fact, some are laughably unsearchable, considering a major reason for storing human capital data is (you would think) to be able to retreive it to take action on it.

If you look closely at the chart above, you will notice it says “Most ATS’s.” That’s because there are some highly searchable ATS/CRM solutions on the market, and I am also aware of some “home-grown” systems that are also highly searchable. So while there are some highly searchable ATS/CRM solutions available, too many are unacceptably low on the “searchability” scale.

When it comes to the Internet - while you can search the Internet and find resumes, only Bing supports queries employing full Boolean logic. The irony there is that Bing limits you to 10 search terms or 150 characters (the documentation vs. realized results is sketchy).

It’s nice that Google gives you 32 search terms, but in some cases, this limits your ability to configure queries that are precise enough and/or remove all false positives.

And while finding some local resumes can be done using Internet search engines, it is difficult to be sure if you are actually finding ALL of, or even the best available, resumes.

Because the Internet is unstructured, when you search for area codes, state abbreviations, and zip ranges (as you can with Google), you often get a number of false positive results. And if a person puts their resume online but does not list an address or a phone number – good luck trying to find them as a local candidate.

To be highly “searchable” – it should not be hard to find exactly what you’re looking for, and you should not have to suffer many irrelevant results.

Highly Searchable but Shallow Data

Here we have Twitter and LinkedIn.

I’ve written about this many times before – Twitter is an extremely shallow source of talent data. The operative word in the term microblog is “micro” – 140 characters for Tweets and 160 characters for a bio.  That’s not a whole lot to go on. While some tweeps do tweet about their professional life, many do not. Also, many people don’t give away much information in their micro-bio either.

Unlike Twitter, which by design is a shallow source of talent data, LinkedIn is a deeper source of human capital data. However, while some LinkedIn profiles are as detailed as a traditional resume, there are still plenty of profiles with very little, if any, information on them. I have no doubt that over time more people will flesh out their profiles with more information and LinkedIn will move to the upper right quadrant of the chart.

Here’s where both Twitter and LinkedIn shine brightly – searchability. Twitter employs tag searching (hashtags #), supports full Boolean logic, enables location searching via geocoding ( SQL near:DC within:25mi), and some third party applications (e.g., Twellow) allow you to search specific fields such as bios (@bio developer). Click here to learn more about searching Twitter for sourcing candidates.

LinkedIn supports full Boolean logic and can accept and run insanely long and complex queries, allows for Boolean searching of structured data (current/company, current/past title, school…), has configurable location searching, supports industry and group search, as well as “hand-coding” of searches with LinkedIn’s own advanced operators (ccompany:, ptitle:, etc).

Deep Data and Highly Searchable

Here’s the sweet spot for sourcing and recruiting – sources of talent data that offer significant depth of information AND are highly searchable. In this quadrant we have the job board resume databases and something I like to refer to as Talent Warehouse solutions.

The job major job board resume databases (Monster, Careerbuilder, Dice) all have…that’s right – resumes, which as we have already seen are deep sources of talent data. Resumes offer a work history including career progression, skills and experience (at least to some extent), environment/group/division/project information, education, and precise location. In many cases, resumes will detail specific responsibilities and responsibility level, as well as accomplishments and achievements.

In addition, all of the job board resume databases are also highly searchable, supporting full Boolean logic, useful query modifiers such as the asterisk for root-word searching, structured field searching (recent experience/titles, etc.), and configurable location searching.

Sourcers and recruiters can run Boolean strings and structured queries when searching job board resume databases to precisely target specific experience, years of experience, education, certifications, environmental/project, and industry experience.

Those who are particularly adept can even achieve semantic search by crafting Boolean strings that go well beyond buzzword matching and target specific responsibilities, or in other words, what the candidates have actually done as well as what they have done it with.

The combination of deep data and high searchability affords you the ability to search for and essentially control critical candidate qualification variables enabling “high yield” e-sourcing – a high volume of more accurately and appropriately matched results in less time.

Talent Warehouse

When you saw that large yellow bubble labeled “Talent Warehouse” in the upper right hand corner of the chart, I’m sure most of your were wondering, “What the heck is a Talent Warehouse?”

Just as Business intelligence applications are typically supported by a data warehouse - which is the main repository of an organization’s historical data, also known as corporate memory (the total body of data, information and knowledge required to deliver the strategic aims and objectives of an organization) - a ”Talent Warehouse” serves as the main repository of an organization’s Human Capital data, and it would serve as the raw material for a Talent Support System (TSS) – a computerized system for helping to make Talent-related decisions, such as talent identification and acquisition.

The core of a Talent Warehouse is a relational resume database. We’ve already established that resumes are deep sources of human capital data – but you may have noticed that on the chart above, “Talent Warehouse” is actually higher on the scale of data depth than other sources of resumes. “What’s a deeper source of human capital data than a resume you ask?” A Talent Warehouse adds more depth to resumes through the use of comments/notes and tags – sourcers and recruiters can add additional information to candidate records and resumes based on phone screens, in-person interviews, references, tests and evaluations, etc.

Imagine being able to search for candidates based on information contained in their resumes AS WELL AS information gleaned from the candidates through interviews. Imagine that!

Although many Applicant Tracking Systems, HRMS/HRIS solutions and Recruiting CRM applications make lofty claims as to their capabilities and functionality, I don’t consider (m)any vendor solutions currently on the market to be a true Talent Intelligence/Talent Warehouse solution. Most are simply systems that track and organize applicants (ATS), and/or enable the management of “relationships” (CRM), and they often they lack a critical piece of the puzzle – searchability.

A true Talent Warehouse has a search interface that supports the searching of resumes as well as tags and notes using both standard and extended Boolean queries (including configurable proximity and variable term weighting) to enable effective semantic search as well include an Artificial Intelligence resume/job matching engine to cover all angles.

This kind of search interface and engine can enable sourcers and recruiters to quickly and precisely find quantities of well qualified candidates. In the hands of an adept Talent Miner, a Talent Warehouse can yield a high volume of results with a high percentage of candidates who have specific skills and experience, specific responsibilities, specific years of experience, specific environmental/project experience, and who live in a specific area.

What this essentially affords a recruiter is the ability to leverage technology to find, contact, and establish and build relationships with more of the right people more quickly – increasing effectiveness and productivity!

Final Thoughts

You can find and hire people by searching any electronic source of talent data – resume or otherwise.

However, searching shallow and less searchable sources such as Twitter, Facebook, Google+, blogs, the Internet and other similarly shallow sources of candidate data takes a higher amount of effort for a smaller return – a low ROI. This results in low yield sourcing and recruiting and ultimately lower productivity.

If you have deeper and more searchable sources of talent data, why would you go out of your way to (or allow your sourcing/recruiting team to) focus on low yield sourcing and recruiting?

Highly searchable and deeper sources of human capital data enable you find more appropriately qualified candidates more quickly, through your ability to search for, control, and quickly analyze and assess critical candidate variables such as specific roles/responsibilities, years of experience, skills/technologies, environment, education, and location.

This results in a higher return on time invested and higher productivity. While it may sound perfectly logical to start with the deepest and most searchable sources of talent data available to you, I assure you – not everyone actually does this. I continue to see and hear about sourcers and recruiters who are blinded by buzz of sources like Twitter and Facebook and who spend more time using them than their own ATS/CRM, or other deeper and/or more searchable sources available to them.

And if your private candidate database/ATS/CRM isn’t as searchable as it could be - consider doing something about it- because it should be. Make the conscious decision to focus the majority of your e-sourcing efforts on the highest ROI sources – those with deep data and those that are highly searchable.

If you listen to certain people in the recruiting industry, you’d think that being able to leverage information systems for talent discovery and identification will be an obsolete skill for recruiters and that sourcers will have to find another profession in the near future.

According to these folks, people with sourcing skills won’t be necessary because the future of sourcing will lie in total automation – they believe that applications that employ semantic search, AI and NLP (Natural Language Processing) will be able to perform the entire candidate matching process for you.

However, neither Watson, Artificial Intelligence, Natural Language Processing nor semantic search will be putting any sourcer or recruiter out of a job anytime soon unless all they’re doing is basic keyword and title searching.

Be Wary of Total Automation

As I have said countless times before, you should not seek to automate that which you do not fully understand (e.g., human capital information retrieval) and that you cannot accomplish manually.

If an organization hasn’t already mastered manual human capital information retrieval via Boolean queries and LinkedIn’s faceted search, then they should most certainly not try to implement a solution that automates candidate matching.

Admittedly, some roles and hiring profiles are incredibly easy to match based on most recent title (e.g., accountant, customer service, account manager/executive, etc.) coupled with a few supporting keywords. I’ve used a number of solutions that employ semantic clustering for concept matching and I have to say they do remarkably well for these kinds of roles.

Unfortunately for the people who make these solutions and fortunately for some (who make a living off of being able to do what matching apps cannot), semantic and AI matching applications don’t do very well with anything approaching even moderately complex hiring profiles where the best indicator of relevance isn’t easily determined by basic title and keyword conceptual matching.

Thankfully, I’ve never had the luxury of being able to find a volume of well qualified and matched candidates by typing in a title or two and a few keywords from job descriptions.

I say thankfully because I would not have developed the skill I have now had I started my career in recruiting focusing on roles where basic keyword and title searches allowed me to find a suitable number of relevant results, I would never have developed an interest in human capital information retrieval, and this blog would not exist!

My entire career has been focused on higher-level information technology and finance and accounting positions, including those requiring up to and over Top Secret clearances. For these kinds of roles, basic title and keyword searching yields essentially what anyone else can easily find and match with little thought (offering me no competitive advantage), as well as a flood of false positives to wade through. I’m often called in to figure out how to find candidates when all other attempts and solutions – both human and AI – have failed.

If an organization’s talent needs can be met solely by basic keyword and title searching, you certainly don’t need people to manually perform the searches, and we don’t need to wait for Watson-level performance because even todays semantic search solutions can take care of this level of retrieval.

However, always realize that not all results are created equal, and every search – automated via AI or otherwise – returns some relevant results and excludes some relevant results. Do not be “wowed” by semantic matching applications that can return what appear to be good results – you can be guaranteed that you’re also missing some fantastic candidates, and there is no automated solution for exploring dark matter.

I am positive that over time, semantic search/clustering, machine learning, and NLP solutions specifically designed for talent discovery and identification using human capital data will make advancements and their effectiveness will improve.

What I am not so sure of is how many jobs will actually be displaced by these solutions. In fact, some new jobs are certain to be created as a result of these applications.

Specialized Technology Requires People with Specialized Skills

Take a look at the emergence of ERP and Business Intelligence solutions as an example.

Have applications like SAP or Oracle’s HRMS, Financial and Supply Chain solutions eliminated jobs?

Perhaps some lower-level positions, but it is quite clear that they have created a great many jobs, and some ridiculously high level/paying! Some people with highly specialized experience with specific SAP modules can earn over $200/hour – and that’s not just for technical people – it’s for functional experts as well!

You would think that multi-million dollar software applications would do all of the HR and accounting work for you. The reality is that these applications don’t do any real “work” themselves.

Here’s a one-word search for SAP on Indeed:

Notice how many “Analyst” jobs come up even though I didn’t search for the term. That’s because SAP applications don’t perform any real work on their own – they store and move data (via reports and such), but people are required to make sense of it.

Business Intelligence solutions such as SSAS, Cognos, Essbase, or Business Objects don’t “work” on their own – they require people to configure, use, and analyze the information provided by them.

Highly specialized applications require people with the specialized skills and experience to use them, to make sense of, interpret and to make decisions based on the data and information provided by them.

Artificial Intelligence Requires Real Intelligence

The operative word in the phrase “Artificial Intelligence” is “artificial.”

Just as with ERP and Business Intelligence applications, when it comes to sourcing and recruiting solutions that use semantic clustering, AI, and NLP – people will be needed to implement, maintain, upgrade, customize and of course actually use the semantic search and matching solutions in production to find top talent.

Thinking that a semantic search solution for recruiting will run automatically without some guidance from a power user is like believing that companies can rely solely on the “canned” reports that come with HRMS and Financial ERP systems and Business Intelligence solutions. They don’t – practically no one does!

A non-customized semantic search solution that isn’t tailored specifically for the organization and multiple business units using it is essentially equivalent to a “canned” report that comes with PeopleSoft, SAP or Microsoft out of the box. I do quite a bit of work in the Information technology space, and I’ve seen a steady need for well over a decade for people with expertise in creating custom cubes, views and reports using ERP and BI applications.

If you think about it, a report is really just a query for the retrieval of information to analyze. Not much different than searching for, retrieving and analyzing human capital data for potential candidacy!

Additionally, non-customized semantic search solutions can nullify competitive advantage. If 5 companies who are constantly battling it out for top talent all use the same semantic search application, all 5 companies will find all of the same people. That means no competitive advantage. Perhaps even more importantly, they’ll also be totally unaware of the people their semantic search solution could not find – a common hidden talent pool of Dark Matter profiles that cannot be tapped by any of the companies.

Ultimately, Jeffrey Liker nailed it when he said “Computers move information, people do the work.” Computers and applications will always be able to move and sort data faster than a person – but in the end, people are needed to analyze the resulting information and make decisions.

The Future of Sourcing

The future of sourcing will see an increased usage of ever-improving semantic search and artificial intelligence candidate matching solutions.

Lower-level sourcing roles, such as name generation and searching with basic titles and keywords, will be all but eliminated. Software will be able to do this level of sourcing thousands of times faster than people can, and even more cost effectively than human outsourcing solutions that can currently cost $8 – $15/hour. This is because this level of sourcing is little more than moving data from once place to another (e.g., from the web, Monster, or LinkedIn to an ATS) – there is little to no analysis involved.

However, on the other end of the sourcing spectrum, it’s a totally different story.

Just as there are currently hundreds of thousands of people who are responsible for having functional and analytical expertise with today’s widely used Financial, HRMS, SCM ERP and BI applications to query and analyze data and information, people within HR, Recruiting and Talent organizations will eventually be required to have highly specialized skills and abilities with regard to leveraging  human capital data through the use of semantic search solutions for talent discovery, identification and matching.

In the future, the process of creating and automating effective queries of human capital data for talent discovery and identification will be incredibly similar to what a BI analyst currently does when they design data cubes and produce reports from them. Instead of Applicant Tracking Systems, companies will build Talent Warehouses which will become their source of significant competitive advantage with regard to identifying and acquiring top talent.

The talent discovery and identification solutions of the future will require people with specialized skills and experience to use them effectively, to customize them for specific business units and ever-evolving hiring needs, and to “teach” and steer matching applications to continually improve them and make them better.

Will you be one of those people?

I will be!

Hopefully it won’t take too long for companies to value and invest as heavily in sourcing and recruiting just as they currently do with their multi-million dollar Data Warehousing, Business Intelligence and Decision Support Systems.

Investing millions in ERP and BI solutions is great – but what about investing millions into enabling your organization to find, hire and retain the talented, game-changing people who will be using the ERP and BI solutions to analyze your financial, product and sales data and make decisions that can save or make your company millions?

What’s more critical and strategic to the sustained and long term success of a company than talent acquisition and retention?

As sourcing and matching technology advances, they will require people with the specialized skills and experience to use them, to make sense of the results returned (and not returned!) by them, and to interpret and make decisions based on the data and information provided by them.

As will likely always be the case, the value that humans bring to any endeavor is in the ability to do what machines and applications cannot.

I thoroughly enjoyed reading Amybeth Hale’s recent “Sourcing: The Next Sexy Thing” post on the SourceCon website. In her article, she quoted Chris Brogan, who opined that unstructured data gathering, “analytics tools and the people who know how to drive them will be the next sexy thing.“

I could not agree more!

In fact, I published an article over 2 years ago in which I  proclaimed that leveraging human capital data for talent discovery, identification and acquisition was the sexiest function in recruiting.

Unfortunately that was back in 2009, when pretty much no one read my blog, so I am updating and reposting it here for the global sourcing and recruiting community to absorb and respond.

What’s The Sexiest Function in Recruiting?

In February of 2009, I read this excellent post on the Google blog written by Jonathan Rosenberg, SVP, Product Management at Google, and I was especially excited when I got to this part:

“Hal Varian likes to say that the sexy job in the next ten years will be statisticians. After all, who would have guessed that computer engineers would be the cool job of the 90s? When every business has free and ubiquitous data, the ability to understand it and extract value from it becomes the complimentary scarce factor. It leads to intelligence, and the intelligent business is the successful business, regardless of its size. Data is the sword of the 21st century, those who wield it well, the Samurai.” (emphasis mine)

Hal Varian, Chief Economist at Google gets it.

Reid Hoffman of LinkedIn gets it.

Unfortunately, too few people in the HR and recruiting function get it, and even fewer executives who proclaim that their people are their competitive advantage do.

“Get what?” you ask?

I’m talking about the fact that the ability to understand and extract value from data is a scarce factor and a significant source of competitive advantage.

When it comes to recruiting top talent, the ability to understand and extract value from the ever-increasing amount of human capital data available to everyone is the scarce factor in talent identification and acquisition, and it leads to intelligence and success in business through the ability to hire more of the best people more quickly!

As such, the sexist function in talent acquisition is sourcing!

The Big Deal about Human Capital Data

Every day, more information about more people is made available electronically in the form of data, and I would argue that not only is the sheer volume of human capital data increasing, but it’s accelerating as well.

This comes from a variety of activities that people perform: responding to job postings with resumes and entering them into corporate databases, creating social media profiles online, posting resumes on the Internet and in online job boards (e.g. Monster, Dice, CareerBuilder, etc.), being mentioned in press releases, blogging, Tweets, foursquare check-ins, LinkedIn network updates, etc.

With the amount of human capital data available increasing at an accelerating rate, it is becoming more important for organizations to be able to leverage these sources of human capital data for talent identification and acquisition.

There is no denying that building relationships with potential candidates is at the core of effective recruiting, and I don’t think that will ever change. However, what has changed is that if you can take advantage of and tap into the power of the unprecedented amount of human capital data available to sourcers and recruiters today (which increases and accelerates every day!), you can quickly find, engage and develop relationships with and recruit more of the right people.

I’ll say it again because I don’t want anyone to scan past this critical point –  effectively leveraging human capital data enables recruiters and companies to develop relationships with and recruit more of the right people! Especially the coveted “passive” candidates.

If your definition of success (or acceptable paycheck) is based on achieving more than 1 to 2 hires per month, you are at a significant competitive disadvantage if you cannot leverage human capital data.

If you CAN effectively leverage human capital data for talent identification and acquisition, it is a productivity MULTIPLIER.

And for those who believe that the best people can’t be found online or in a database, I would argue that with each passing day, there are fewer and fewer people that cannot be digitally discovered either directly or indirectly through networking or referral recruiting with people who can be found online or in your ATS.

Sourcer = Data Analyst?

I think the term “sourcing” does not do the role/function of leveraging human capital data for talent identification proper justice. I think that what most people refer to as a sourcing function is really more accurately labeled as human capital data analysis.

Data analysis is defined as a process of gathering, analyzing, and transforming data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making.

If you take a look at a typical data analyst job description, you’ll see responsibilities such as:

• Collaborate with business process owners to identify opportunities; define business requirements, and design and implement solutions designed to maximize efficiency and productivity
• Perform complex data mining and aggregation; critically examine the results
• Accumulate, analyze, and interpret data in understandable terms for the customer from multiple systems
• Analyze complex data including: structured, unstructured, and plain text
• Develop analytic approach in collaboration with project staff
• Respond to ad-hoc and standing customer requirements
• Utilize in-house database applications
• Perform data capture, cleansing and migration
• Research new data sources and analytical tools
• Support analysis results at customer meetings
• Utilize query languages  (SQL)
• Conduct competitor and benchmarking analyses

Here is the same job description, adapted to sourcing/recruiting:

• Collaborate with managers and business unit owners to identify opportunities, define position requirements, and design and implement talent identification solutions and processes designed to maximize efficiency, productivity, and results
• Perform complex talent mining and aggregation; critically examine the results for relevance, qualifications, and probability of match to hiring requirements
• Accumulate, analyze, and interpret human capital data in understandable terms for the customer from multiple systems, including, but not limited to the Internet, resume databases, and social media
• Analyze complex human capital data including: Resumes, social media profiles, blog posts, press releases, and unstructured plain text
• Develop analytic approach in collaboration with project staff
• Respond to ad-hoc and standing customer requirements
• Utilize in-house Applicant Tracking Systems and database applications
• Perform data migration, permanently capturing human capital data from multiple sources and entering them into corporate ATS/Talent Warehouse
• Research new human capital data sources and analytical/search tools
• Support analysis of search results at customer meetings
• Utilize query languages (Boolean)
• Conduct competitor and benchmarking analyses

If you are a sourcer or are responsible for sourcing potential candidates, what do you think of the above job description?

Sourcing is Much More than Boolean Search

As demonstrated by the above exercise, a sourcer or a recruiter performing sourcing is essentially functioning as a data analyst that is analyzing human capital data for talent identification and acquisition.

This process involves analyzing and interpreting hiring needs and requirements, often with poor or incomplete information, leaving them to piece the puzzle together. Once the needs have been understood, they intelligently assess and strategically select available information resources to leverage, translating hiring qualifications into precise information retrieval strategies to search targeted sources of talent-related data and quickly retrieve relevant results – human capital data representing people who have a high probability of meeting or ideally exceeding the basic qualifications of the hiring needs.

A talented human capital data analyst is capable of leveraging information sources and systems with such speed and precision to enable organizations to achieve Just-in-Time sourcing and recruiting – identifying and acquiring the right talent, in the right amount, at the right time, without the need for having to recruit people ahead of need and building talent pipelines that may not be recruitable when the actual need arises.

Human capital data analysts are capable of searching databases and systems containing data representing millions of people to quickly and precisely uncover, identify, and tap into the talent pools that exist in every source of human capital data to target people with specific educational requirements, years of experience, current and prior roles and responsibilities in specific environments and in some cases in specific companies. That’s no small feat!

Whereas data analysts responsible for working with financial, product, customer or other types of data are responsible for producing reports and analyzing information for meaning and identifying relationships to assist with strategic decision making, human capital data analysts are responsible for identifying and assisting in the acquisition of a company’s most precious assets – its talent, and potentially (and ideally!) the next “game changing” employees who can take the company to the next level.

What’s Your Competitive Advantage?

Most well run companies know that the true scarce resource is talent – and that identifying, acquiring, and retaining top talent is a company’s only true and sustainable competitive.

The more ubiquitous human capital data becomes, the more critical it becomes that companies employ people with strong information retrieval and human capital data analysis skills who are capable of leveraging information systems more quickly, precisely, and accurately than the competition to identify top talent and target them for acquisition.

As Jonathan Rosenberg of Google stated, there is significant intelligence and value in data, but it does not come from simply having access to the data – it comes from the ability to understand and extract value from the data.

I would argue that the most critical asset of any company is its human capital – and the most critical step in the human capital supply chain is the process is identifying human capital.

Don’t agree?

Consider this – you simply cannot engage, develop relationships with, acquire, or retain people you haven’t identified in the first place.

Final Thoughts

There is a reason why companies pay financial analysts, data analysts and business intelligence analysts very good money – because they are knowledgeable specialists and they perform highly critical functions that cannot be fully automated. Typically, the more critical the corporate function, the more specialized and capable the resources are, the more they are compensated, and quite often, the more advanced the technology solutions they employ.

I think that the majority of HR/recruiting organizations as well as many corporate executives haven’t yet figured out that analyzing human capital data for talent identification and acquisition is a highly specialized, valuable, and critical function.

While there are opportunities to automate some aspects of human capital information retrieval, it would be foolish to rely on a software solution alone for your talent identification and acquisition needs. No company relies solely on software to make critical business decisions – computers and applications are used to move and present information (reports, etc.) and then people perform the real work in the form of analyzing the information and making decisions.

It’s also a mistake to rely on entry level, junior, and/or low cost resources for talent discovery and identification. In case you haven’t noticed, there’s a continual war for talent being waged. Do you really think it’s a wise corporate strategy to try to cut costs and corners and battle it out with your competitors with less experienced and non-specialized resources?

As I’ve previously asserted, the most critical step in the human capital supply chain is the process is identifying human capital, because you simply cannot engage, develop relationships with, acquire, or retain people you haven’t identified in the first place. As such, don’t you think it would be wise to employ highly specialized, experienced, and capable resources in talent discovery and identification roles?

Not only is human capital data sexy, it is the sword of the 21st century – those who wield it well will win the battles they fight in war for talent.

When I recently spoke at SourceCon in New York, I showed an example Boolean search string that could be used as a challenge or an evaluation of a person’s knowledge and ability.

The search string looked something like this:

(Director or “Project Manage*” or “Program Manage*” or PM*) w/250 xfirstword and (truck* or ship* or rail* or transport* or logistic* or “supply chain*”) w/10 (manag* or project)* and (Deloitte or Ernst or “E&Y” or KPMG or PwC or PricewaterhouseCoopers or “Price Waterhouse*”)

During the presentation, an audience member asked me why there wasn’t any use of site:, inurl:, intitle:, etc. I responded by acknowledging that for many, sourcing and Boolean search seems to be synonymous with Internet search – however, this is definitely not the case.

Boolean Logic is Simply the Simplest Way to Search

Some (but I hope not too many!) sourcing and recruiting professionals may be surprised to learn that Boolean logic significantly predates the Internet as well as computers – by over a century!

I still run into sourcers and recruiters that are not aware that the word “Boolean” comes from the man who invented Boolean Logic in the 19th century – George Boole. Boolean Logic is the basis of modern computer logic, and George Boole is regarded in hindsight as one of the founders of the field of computer science.

With Boolean logic being created in the 1800′s – it’s pretty obvious that Boolean logic is not just for searching for people and information on the Internet.

Practically any information system from which you need to search and retrieve information from “speaks” Boolean.

This is understandable, because using Boolean logic is the simplest way to construct a search. When you want a combination of terms/phrases you use AND, when you want at least one of a group of terms/phrases you use OR, and when you don’t want something you use NOT. It really doesn’t get any easier than that.

When anyone types more than a single word or phrase into Google, Bing, LinkedIn, Amazon, eBay, etc., they’re performing Boolean search, because spaces are automatically converted to ANDs. Billions of people across the globe are running basic Boolean strings whether they are aware of this or not, which is a testament to how easy Boolean search is.

Sourcing isn’t about Boolean Search Strings

Sourcing candidates is much more than Boolean search strings – they are but one aspect of sourcing.

Sourcing talent is more accurately and completely defined and described as human capital information retrieval.

Information retrieval (IR) is “the science of searching for documents, for information within documents, and for metadata about documents, as well as that of searching relational databases and the World Wide Web.”

Leveraging information systems for talent discovery and identification is about searching documents, for information within documents, and for metadata about documents, as well as that of searching relational databases and the Internet for human capital information, including titles, companies, responsibilities, skills, technologies, social network updates, blog posts, resume information, event and association lists, etc.

With IR, an information retrieval process begins when a user enters a query into an interface.

Queries are simply formal statements of information needs. For a sourcer or recruiter, their information need is typically to find information that will lead them to discover and identify people with specific skills, experience, capabilities, education, etc.

While using Boolean operators is arguably the easiest way to construct a query, IR queries do not have to be limited solely to Boolean logic, as can be seen in the various non-Boolean query modifiers of Internet search engines (here are some of Google’s and Bing’s), LinkedIn’s advanced search operators, faceted search (e.g., LinkedIn’s filters), etc.

The “hard” part of creating queries for human capital information retrieval isn’t deciding which Boolean operators to use. AND/OR/NOT is the easy part. In fact, my daughter learned about Boolean logic last year, including constructing Venn diagrams – in her 1st grade public school class!

The hard part of creating queries is intelligently selecting a combination of words and phrases, and in some cases strategically excluding some words and phrases, that will return highly relevant results – people who are not only likely to be qualified for the position being sourced for, but also highly likely to be interested in the opportunity (i.e., “recruitable”).

Yes – you actually have to think in order to create effective queries that return highly relevant results.

Human-Computer Information Retrieval

Human–computer information retrieval (HCIR) is “the study of information retrieval techniques that bring human intelligence into the search process.”

According to Wikipedia, which IBM’s Watson used heavily to compete in Jeopardy, “The fields of human–computer interaction (HCI) and information retrieval (IR) have both developed innovative techniques to address the challenge of navigating complex information spaces…[and] Human–computer information retrieval has emerged in academic research and industry practice to bring together research in the fields of IR and HCI, in order to create new kinds of search systems that depend on continuous human control of the search process.” (emphasis mine)

The term human–computer information retrieval was coined by Gary Marchionini whose main thesis is 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.” (emphasis mine again)

For those who simply want information systems to magically provide them with the most relevant results at the click of a button, you should take special note of the fact that experts in the field of HCIR do not believe that people should step out of the information retrieval process and let semantic search/NLP algorithms/AI be solely responsible for the search process.

If you’re interested in learning more about HCIR, I suggest you read this blog – you may be surprised and interested to see who the author is, where he’s been, what he’s done, where he is now, and what’s on his mind.

Talent Mining

In my opinion and experience, Boolean search neither adequately describes nor gives proper credit to what sourcers and recruiters are really doing when they leverage the Internet, resume databases, ATS/CRM applications and social networking sites such as LinkedIn to find candidates, and to what some very talented and highly skilled professionals are able to accomplish with human capital information.

At SourceCon 2010, I spoke about a specialized form of HCIR which I call talent mining, which is essentially human capital information retrieval – a specialized form of IR involving querying and analyzing human capital data (resumes, social network profiles and updates, blogs, etc.) for talent discovery, identification, and ultimately acquisition.

I believe there are at least five distinct levels of Talent Mining:

1. Skill/Title Search
2. Concept Search
3. Implicit Search
4. Semantic/Natural Language Search
5. Indirect Search

Talent Mining is not defined by nor limited to Boolean search – any and all information retrieval methods that can be leveraged to discover and return human capital data are applicable and should be used.

At the strategic level, talent mining is the process of transforming human capital data into an informational and competitive advantage, which is much more than simply writing Boolean search strings.

Only the simplest and most basic level 1 talent mining can be performed without much thought – slapping titles and keywords taken directly from a job description into a Boolean search string and hitting “search.”

Beyond that, more advanced level 1 and most certainly levels 2 through 5 talent mining require significant “cognitive energy,” as well as involve continual improvement.

In fact, effective sourcing can and should be an iterative process.

Beyond Boolean & Internet Search

I believe that those who equate sourcing with basic Boolean Internet search don’t fully understand or appreciate the power of human capital data, its many forms and sources, and the many ways that it can be leveraged.

While the Internet has a lot of information, it is also full of garbage (others would call it “noise”) and it does not hold as many “findable” resumes as you may have been led to believe.

There is no denying that non-resume human capital data is valuable, but searching the Internet for non-resume information can easily spiral into an exercise in low ROI, time consuming garbage-sifting. Many don’t realize (or want to recognize) that non-resume data offers shallow information at best and thus has less qualitative and predictive value.

Additionally, the Internet isn’t a database – it’s a network of networks and the information stored on those networks is largely unstructured.

Structured data is an order of magnitude (it could easily be argued many orders of magnitude) more valuable and searchable than unstructured data, if for no other reason than it’s intrinsically high predictive value.

LinkedIn offers a good example of the power of structured human capital data, although a large percentage of LinkedIn profiles are information-anemic. Even so, all profiles are required to have employer and title information, and both are structured, fully searchable fields.

Additionally, corporate ATS’s and major job board resume databases have hundreds of thousands to tens of millions of candidate records – with deep and sometimes well-structured data. I’m perpetually confused as to why there is so much written on Internet sourcing and why I don’t see more people writing and speaking about mining all of the rich human capital data hiding in resume databases and applicant tracking systems.

Perhaps one of the reasons why the sourcing function and role isn’t highly regarded or respected by some is because those people equate sourcing with basic Boolean search. If all they think sourcers and recruiters are doing is directly searching for keywords and titles from job descriptions, then I can actually understand why some people would think of sourcing as an entry level role or function.

However, sourcing isn’t just about Boolean search, it’s about human capital information retrieval.

While Boolean logic is the simplest way to construct an IR query and practically all information systems accept basic Boolean operators, the real “magic” and work of sourcing talent is the iterative, intelligent, and cognitively challenging process of selecting a combination of words and phrases, and in some cases strategically excluding others, analyzing the results returned, making changes to the query based on observed relevance, and repeating the process until an acceptable quantity of highly qualified and matched candidates are identified.

I would personally like to see more sourcing, recruiting and HR conferences and blogs to address human capital information retrieval, specifically with regard to focusing on the sourcing process, as well as deep and structured human capital data. If this happens, I don’t think it will be long before companies start to realize that sourcing can offer a serious strategic competitive advantage, and perhaps invest more in technologies and talented people to achieve a competitive advantage based on human capital data for talent discovery, identification, acquisition, and retention.

When I spoke at the LinkedIn Talent Connect event last year, I dropped a big question on the 500+ audience:

“What’s your informational and competitive advantage when you all have access to the same people?”

Think about it.

If you have a LinkedIn Recruiter account (over 55% of the Fortune 100 do!), you have access to view any and all LinkedIn profiles.

So do your competitors that are hunting to identify and recruit the same talent.

Regardless of your LinkedIn account type (Free, Business, Business Plus, Executive, Pro, Talent Basic, Talent Finder, Talent Pro, or Recruiter), you still have access to viewing any and all public profiles, although you just might have to jump through some flaming hoops with a small network and a free account.

So now I will ask you – if the majority of sourcers, recruiters and human resources professionals in the world use LinkedIn for sourcing and talent acquisition (there’s nearly a million!), what’s your competitive advantage over your rivals?

What is Competitive Advantage Anyway?

I use this term frequently because it’s a critical concept.

Competitive advantage is defined as the strategic advantage one business entity has over its rival entities within its competitive industry. It can also be described as simply as something you can do that your rivals cannot, or something you can do significantly better than your rivals.

Traditional explanations of competitive advantage often refer to access to resources (e.g., natural, human, or information). However, as I’ve already addressed, access isn’t the advantage in the case of LinkedIn (or the Internet, for that matter).

So what can you and your company do with LinkedIn that your competitors cannot? How can you leverage LinkedIn better than your rivals?

LinkedIn is Seriously Passionate about Data

We all know LinkedIn has a lot of data, with over 85 million profiles worldwide and information on millions of companies. Depending on your source of estimates, LinkedIn may have as many profiles of U.S.-based professionals as Monster has resumes.

However, LinkedIn doesn’t just hoard data – they are constantly looking for ways to extract value and insights from the information they have collected.

As a human capital data nut, I could not have been more thrilled to hear Reid Hoffman (Co-Founder and Chairman of LinkedIn, if you didn’t know) talk at Talent Connect 2010 about “data as a platform” and data as web 3.0 .

You may be interested to learn that Reid recently invested in Groupon, specifically because of “the power of data.”

Speaking of investments, LinkedIn also recently snagged a top scientist from Google whose specialty is data and information retrieval.

Coincidence? More like a calculated strategic move.

Daniel Tunkelang is now the Principal Data Scientist at LinkedIn, and he wrote on his blog that he will be working on “products and discover insights from a data collection,” tackling his favorite challenges in the areas of computer science, which happen to be “information extraction, matching, recommendation, social network analysis, and network visualization.” Sounds like my kind of guy!

Oh, and did I forget to mention Daniel was the Chief Scientist and Co-Founder of Endeca?

When I saw that Daniel’s Twitter bio mentioned “HCIR Guy” – I was especially excited because I thought I was no longer the only person passionate about Human Capital Information Retrieval. However, after a quick search, I found out that I’ll have to settle for human-computer information retrieval.

If you need more proof that LinkedIn is passionate about data, specifically with regard to recruiting, watch this video of Jeff Weiner being interviewed at the Web 2.0 Summit 2010, read this post on how LinkedIn recently hired a neurosurgeon as a data scientist, and this article on the secrets of LinkedIn data scientists.

What is the Value of Data?

Having a ton of data is fantastic, especially human capital and company data if you’re in recruiting.

However, I argue that the value of data lies not in the data itself, nor access to it – but in the ability to retrieve the data and extract value from it.

Quite simply, data has no value if you don’t recognize it, don’t review it, or cannot retrieve it.

Of course, LinkedIn doesn’t prevent you from retrieving any data. It might be easier for some people to access certain profiles and certain information (out of network results and full names, depending on your network size and account), but the information is there to be retrieved if you know how and you’re particularly adept.

However, just because the information is there and you have access to it, it doesn’t mean you can retrieve or, or even recognize its worth if you do. Many people unknowingly only retrieve a fraction of the available results when searching LinkedIn – the proverbial tip of the iceberg.

To be sure, anyone who runs a search on LinkedIn will get results – but that most certainly does not mean anyone is actually finding all of the best candidates that LinkedIn has to offer.

If 20 recruiters from 20 different companies are looking for candidates with the same experience and hiring profile, they would likely get 20 different searches, with some recruiters finding some of the same candidates. However, some will find profiles that the others do not.

You may not believe it, but the reality is that some of the best candidates are never found by the people who are searching for them on LinkedIn. That’s not LinkedIn’s fault or problem – you just can’t be aware of something your searches do not, or cannot return.

The Competitive Advantage of LinkedIn

Having access to and using LinkedIn doesn’t afford you any competitive advantage over your rivals in the war for talent.

If you have a premium or ultra-premium account on LinkedIn, you do have access to use more Talent Filters, which certainly make it easy to slice and dice the data by years of experience, years in most recent position and current company, groups (any), company size, Fortune rank, etc. You also have more saved searches and InMails for contacting prospects. However, your competitors who also use LinkedIn Recruiter have access to exactly the same features.

If 5 companies that compete for the same kinds of talent are all using LinkedIn Recruiter, they all have access to view all profiles, and they all have access to the same premium filters and features – so where’s the competitive advantage?

If you don’t have access to some or any of LinkedIn’s Talent Filters, don’t fret too much. While the premium Talent Filters can make very short work of narrowing down results, they do have limitations that few understand and appreciate, and someone with strong information retrieval skills can find precisely what they want and need without the use of filters/facets.

I think LinkedIn has done a great job with their faceted search, and they continue to offer new ways of slicing and dicing the LinkedIn network. However, the only true and significant competitive advantage to be gained through the use of LinkedIn is how effectively you use (and search) LinkedIn.

Does LinkedIn Offer Recruiters any Competitive Advantage?

Yes, but your competitive advantage is dependent upon and directly proportional to your information retrieval skills.

The war for talent will be won and lost over human capital data and information, and more precisely over human capital information retrieval and analytics.

Simply having access to the information does not afford a sourcer, recruiter or organization any competitive advantage.

However, a human capital informational and competitive advantage can be achieved through more effective retrieval – in other words, more effective queries (i.e., Boolean search strings and facet utilization).

A query is simply a formal statement of an information need.

When it comes to sourcing and recruiting, your queries are formal statements of your talent/human capital needs. When searching to identify talent, the more effective you are at translating your talent needs (skills, experience, qualifications, etc.) into queries, the more likely you are to find all of the best candidates any particular source of talent has to offer.

While LinkedIn might be the Ferrari of social recruiting solutions, having a set of keys doesn’t mean you can drive it like a pro.