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

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

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

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

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

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

Corporate Leadership – Is Your Money Where Your Mouth Is?

Most people agree that any given company’s only sustainable competitive advantage is to identify, attract, recruit and retain great people.

If the executive leadership of any company really believed that their people are their greatest asset, and that hiring great people is critical to their long term success as a business, why would they limit their sourcers and recruiters to using common Internet search engines to search free and unstructured data?

Do they really believe that they are enabling their talent acquisition team with the highest probability of success in consistently being able to find, attract and recruit top talent?

Think about it – if you’re using Internet search engines to source potential candidates, you’re using Google, Bing and other sites in a manner for which they were certainly not specifically designed, and you’re searching unstructured data that everyone has free access to.

Does that sound like you’re being enabled with a competitive advantage in any way?

There’s no denying that valuable information can be mined from the Internet, and there is no doubt that the more knowledgeable you are with regard to leveraging Internet search engines, the more quickly and easily you can retrieve information others cannot and do not find.

However, I believe that technology-enabled talent sourcing is many years (at least a decade) behind what it should and could be at this point, and a huge contributing factor has been the last 10+ years spent focusing primarily on Internet search.

Sourcers and recruiters responsible for sourcing should not be asked by their employers to become sourcing MacGuyvers – having to inventively use common and unspecialized tools that everyone has access to to solve their company’s talent acquisition challenges.

You could technically play a game of baseball with a broomstick, a tennis ball and just your hands for catching, but you’d lose to a similarly skilled team that used the same bats, balls and gloves that Major League Baseball pros use.

Sourcing: Beyond the Internet

Boolean search does not = Internet search, although you would be hard-pressed to believe that based on the majority of information published on the topic, at least within sourcing and recruiting circles.

A good bit of the “Boolean” search advice you can find online isn’t really focused on Boolean logic at all – you’ll find that a large chunk of the sourcing advice and information actually consists of Internet search engine tips and tricks and specific website “hacks.”

Being able to effectively leverage Internet search engines is important and helpful for any sourcer or recruiter, but searching unstructured and public data certainly has its limitations, and there are much more powerful search engines available that were designed from inception for powerful text retrieval.

There is no denying that the Internet is a big pile of unstructured data, a good deal of which is old and outdated – and that there is plenty of “stuff” out there that can’t even be retrieved, simply because it’s never been indexed.

Let’s also recognize that just because you can search the Internet doesn’t make it highly searchable. I define highly searchable as the ability to quickly and easily retrieve highly specific information with a very low rate of false positives. A good bit of the work any sourcer or recruiter has to perform when sourcing with Internet search engines is attempting to remove false positives.

Of course, there is no denying that searching the Internet with standard or Custom Search Engines (e.g. Google CSE’s) can uncover valuable information.

However, when it comes to deeper/information rich human capital data such as resumes, there aren’t actually as many CV’s/resumes on the Internet as some people would have you believe. Moreover, it could easily be argued (and perhaps proven with a little more effort) that the majority of the resumes that ARE retrievable with Google or Bing are those of Information Technology professionals. There are many professions for which there are remarkably few CV’s/resumes, if any, retrievable via Internet search.

When it comes to shallow/information poor human capital data, such as press releases, lists, directories, etc., I’d argue that there isn’t even as much of that as most people assume (some people make BIG assumptions), and it’s certainly not evenly distributed across all job types and industries.

Beyond Boolean: Sourcing Methodologies

Sourcing is so much more than Boolean strings. I’ve written about this extensively before, so I won’t go into too much of my supporting argument in this post.

What I will stress here is that Boolean logic is simply the simplest way to construct a query, which is a formal statement of an information need, which in turn is the basic building block of an information retrieval process.

The AND, OR and NOT Boolean search operators are just the “glue” that combine all of the actual search criteria together into a single query.

It doesn’t get any simpler than “I want all of these things” (i.e. AND), “I want at least one of these things” (i.e. OR), and “I don’t want these things” (i.e. NOT). If it wasn’t simple, my daughter would not have learned Boolean logic in the first grade in public school - Venn diagrams and all.

When it comes to sourcing, what I feel is significantly lacking is a focus on the information retrieval process, which involves the analysis and interpretation of the data retrieved, and is infinitely more important than any specific search string or Boolean search operator.

The most critical component of any query are the search terms and phrases that are included or strategically excluded (and I am not talking about negating false positives), which should be arrived upon through a consistently applied iterative and incremental process.

In spite of this, much of what can be found online in terms of sourcing advice is focused on how to use Boolean operators, Internet search commands and site specific hacks.

There is a reason why companies that create software utilize and follow software development process methodologies - and they have been doing so since the 1960′s! Why? It’s simple – because they are seeking to find repeatable, predictable processes that improve productivity and quality.

Inconsistent processes lead to inconsistent results. So will a complete lack of a process.

I strongly believe that if the global sourcing community moved more in this direction, focusing more on the sourcing process and data analysis and interpretation rather than the searches themselves, and more specifically Boolean operators and Internet search commands, sourcing would gain more respect, attention and investment, especially from leaders and executives who currently equate sourcing to searching Google for people.

The Future of Sourcing: From Internet Hack to Data Scientist

Have you heard the term “Big Data?” How about “data scientist?”

I would not be surprised if most sourcers, recruiters and HR professionals had no idea what “Big Data” meant, or what it could possibly mean for the future of talent acquisition.

I also would not be surprised if most people immediately rejected the idea that data scientists already have a place in recruiting teams and HR departments.

That’s unfortunate, because I think it’s time more people in our industry started to think outside the box of that has been limiting progressive thought and preventing advancements in the utilization of technology to create a competitive advantage in talent acquisition.

As such – I highly recommend you read this report from McKinsey Global Institute – Big data: The next frontier for innovation, competition, and productivity. While you’re reading it, the whole time think about how the concepts and practices could be applied to human capital data and hiring decisions.

Imagine a future where human capital data that can be readily found online in the public domain is automatically retrieved and captured (or live-linked to, find.ly style) and combined with private sources of human capital data into structured data sets that enable predictive analytics and that can be manipulated via highly precise and specific information retrieval capabilities that make Google look like a child’s toy.  Literally!

I can’t say when this future will become a reality, but it can’t come soon enough for me!

I firmly believe the future of sourcing will involve an evolution from Internet Hacking to Data Science.

What is largely done manually now via Internet search will be automated and information from disparate sources will be aggregated into proprietary data sets that will become a company’s competitive advantage when it comes to identifying, engaging, recruiting and making hiring decisions to acquire top talent.

Companies are already sitting on mountains of human capital data that they have captured over the years in their ATS/CRM systems, yet few if any companies are really tapping into the latent power of that pool of data.

It doesn’t take a huge mental leap to see that today’s (top) sourcers will be tomorrow’s data scientists enabling the companies that they work for to identify top talent and make better hiring decisions – read this article on the Rise of the Data Scientist, which is from 2009 no less!

Data scientists are already an integral part of competitive intelligence efforts for many companies, mining and analyzing data to help their companies to gain a competitive advantage. Unfortunately, it seems that pretty much all data scientist jobs today appear to be focused on everything BUT enabling talent identification and acquisition.

You may also find this interesting – LinkedIn has a Principal Data Scientist, and he came from Google. Of course, he’s focusing his talents on creating a better LinkedIn solution. I believe it is only a matter of time before companies begin to harness the power of data science to enable faster and better hiring decisions.

Final Thoughts

I believe that technology-enabled talent sourcing and recruiting is perhaps a decade behind what it should and could be at this point, and a huge contributing factor has been the fact that for the last 10+ years, many people still equate sourcing with Internet search.

Searching through unstructured Internet data that everyone has equal access to with dummied-down and non-specialized search engines doesn’t afford a company with a strategic competitive advantage.

You don’t see companies doing this with their product development, marketing, financial analysis, or business intelligence efforts – their data and BI analysts and data scientists are not limited to using Google for information retrieval and analysis, nor are they limited to searching information solely in the public domain.

There’s a reason for that. Using specialized information retrieval, analysis, and visualization solutions to gain intelligence from a proprietary mashup of data affords companies with information and insights that their competitors won’t be able to match.

Going back to the MacGuyver comparison – he was able to make use of mundane materials to create unorthodox solutions to any problem he faced. Imagine if he actually had the right or the best tools available?

If you’re one of those few MacGuyver-level sourcers or recruiters who can make magic from mundane resources everyone else has access to – good for you.

However, imagine what you could accomplish if you had highly specialized tools specifically designed for mining vast and proprietary human capital data sets dynamically compiled from multiple sources that enabled predictive analytics, empowering you to leverage data to more quickly identify, engage and recruit people who are more likely be ideal additions to your company.

Can anyone help me make this dream a reality?

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.

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.

Many people equate sourcing candidates with simply creating and running Boolean search strings.

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 data.

I had the distinct honor of delivering the keynote presentation at SourceCon 2010 which was held at the International Spy Museum in Washington, DC. I spoke about a specialized form of information retrieval and text/data mining which I call talent mining, defined as querying and analyzing human capital data for talent discovery, identification, and ultimately acquisition.

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

Companies have been leveraging financial, product, customer, marketing and many other types of data for decades now, building data warehouses and using business intelligence solutions and analytics to make better, fact-based decisions.  I believe that we are just beginning to enter an age in which companies will start to understand and appreciate the power of leveraging human capital data to predictively identify more qualified candidates, make better hiring decisions, and make them faster and more efficiently than previously thought possible.

My vision and prediction of the next frontier in human capital and talent analytics involves companies building talent warehouses and specialized talent intelligence solutions that will enable them to quickly and predictively discover, identify and acquire top talent. While this may be many years off for most companies, it is quite possible to more effectively leverage the vast amount of human capital data available to just about everyone today. During my keynote, I detailed the 5 levels of talent mining, the specific advantages that talent mining affords over any other method of talent discovery and identification, and a glimpse into the future of sourcing and talent acquisition.

Below you can view the expanded version of the slide deck I used for the presentation, and you can view the video here. Enjoy!

This question has been burning in my mind for quite some time – why is it that so many ATS/recruiting CRM vendors offer poor or limited candidate search functionality? I’m not talking about ATS vendors you’ve never heard of – I’m talking about some of the biggest names in Applicant Tracking/Candidate Relationship Management applications.

I’m well aware that ATS’s serve many critical functions beyond searching for the candidates contained within them, but let’s pull no punches here – you can’t hire someone, or begin to automate candidate relationship management with someone you haven’t FOUND in the first place. And just because a candidate is buried somewhere in your database, it doesn’t mean you’ve actually found them (or can find them when you want or need to).

The bottom line is that data is of little to no value if you can’t retrieve the information you want, when you need it. What is the point of storing human capital data if you can’t precisely retrieve exactly what you want, when you want it? 

Deficiencies Defined

I won’t get into automated/system-side semantic search and match in this post – I’m going to focus on the ability to manually enter search strings to find candidates.

When I say “poor/limited” candidate search capability, I mean at least one or more of the following:

• Unnecessarily short search fields (e.g., 100 characters, including spaces!)
• Lack of full Boolean search (e.g., inability to use AND, OR, and NOT, nesting, etc.)
• Lack of stemming/root word search (e.g., admin* yeilds administrator, administration, etc.)
• Lack of field-based search (e.g., most recent experience, most recent title, education, etc.)
• Lack of searching by zip code radius

Critical Candidate Pool

A company’s internal candidate database is made up of people who have responded to that company’s job postings, people who went to the company’s website and entered their resume and information (not in response to a specific job), and people who were identified elsewhere (employee referral, LinkedIn, Twitter, Monster, niche job board, the Internet, etc.) and entered into the database by an employee. 

One could easily argue that this pool of candidates should be the first place sourcers, recruiters and hiring managers look when they need to find candidates. Unfortunately, this is not the case.

ATS = Candidate Source of Last Resort

A relatively common observation/complaint I hear from recruiting managers in corporate and agency staffing environments is that when it comes to running searches to find potential candidates, their sourcers and recruiters tend to search LinkedIn and the job board resume databases they have access to first, or at least before they search their internal ATS/CRM application. In many cases, recruiters with access to job board resume databases will only use their own ATS as a “source of last resort.”

A recent survey conducted by TalentDrive, which polled over 8,000 companies and staffing firms, confirms this to a shocking degree. They found that “98% of the companies surveyed did not find Talent from within the existing Company ATS.” In other words, candidates can check in, but they don’t check out.

Not quite as shocking, but equally disturbing is that an Online Sourcing Survey conducted by TalentDrive found that almost two-thirds (64%) of the employers represented by the survey’s participants did not know how many qualified candidates were in their own ATS databases.

I think I know one of the major contributing factors to both statistics - most ATS’s aren’t very searchable!

Strong Candidate Search Capability is Out There

I believe the reason why Applicant Tracking Systems are often used as the “source of last resort” is because most ATS’s have candidate search functionality that is far inferior to what sourcers and recruiters have available to them in LinkedIn, any of the major job board resume databases, and even Google. Can we blame recruiters for going first to sources they have access to that actually ENABLE them with the power and control to quickly find the people they need?

If you take a look at large repositories of deep human capital data, such as those offered by LinkedIn and the “big 4″ job board resume databases (Monster, Careerbuilder, Hotjobs, and Dice), you’ll find robust search capability. All accept full Boolean logic, accept relatively long/complex/precise search strings, feature zip code radius search, and offer field-specific searching. Monster takes Boolean search one step further by offering proximity search with the NEAR operator, and Careerbuilder offers advanced AI matching with their R2 functionality(which I think it powered by Sovren – can anyone confirm this?). 

Regardless of how many excellent candidates may be buried in a company’s ATS/CRM, if recruiters can’t run appropriately precise searches to quickly and easily retrieve highly relevant results, they are actually incentivized to use other sources to identify candidates. Sourcers and recruiters will naturally gravitate to what works for them, and unfortunately, in many cases, it isn’t their ATS.

The Customer is Always Right?

When I recently challenged a major ATS vendor regarding their extremely short candidate search field (100 characters, including spaces), their response included this interesting and unanticipated angle - they claimed that 99% of their clients are statisfied with their short search field. In other words, very few prospective or current customers of their ATS asked about, commented on, or asked for improvement of the short search field.

A representative of another well-known ATS chimed in on Twitter and said they also don’t come across many clients asking for more than 100 characters in the candidate search field.

I can only assume that their customers either aren’t very proficient at talent mining, don’t understand the value of human capital data, or worse – both. Regardless, we’ve already seen the statistics from TalentDrive’s surveys - most companies don’t even use their ATS to identify candidates. If they’re not using their ATS to find talent, why would they care about the length of the search field, or even if it supports basic Boolean logic? 

So what we have here is ATS vendors who are not developing and offering robust candidate search capability because their customers aren’t asking for it. Okay, I understand “the customer is always right,” but it’s a sad state of affairs when companies who create talent/human capital solutions are not incorporating strong/advanced candidate search capability into their products because their customers don’t understand the value and full potential of human capital data.

Whatever happened to educating and informing your customers, providing training, and offering a product that exceeds your customers’ expectations and provides them with a true competitive advantage?

100 Characters is Not Enough

I conducted a very informal poll on Twitter and Facebook, asking sourcers and recruiters what they thought of a 100 character candidate search field limit, and 100% of those who responded all felt that it would handicap their ability to find the right candidates. By comparison, Monster’s resume database has a keyword search field that accepts up to 500 characters, LinkedIn’s search field is bottomless (I just crammed 6003 characters in the keword field and LinkedIn laughed and asked, “Is that all you got?”), and even Google accepts up to 32 search terms (at an average term length of a little as 5 letters, that’s still 160 characters, NOT including spaces or operators). 

The TalentDrive survey discovered that the number one sourcing challenge facing companies currently is filtering through the mass of resumes and increased number of applicants. In other words, the candidate “haystack” is getting HUGE, and it’s becoming more challenging to sort through it to find the needles.

Ultimately, short and basic candidate searches are imprecise and yield a high volume of imprecise results, riddled w/false positives. Without more room to create search strings that are appropriately precise, relevance will suffer, and with more resumes to search through – the issue is exacerbated.

The Future of Staffing and Recruiting

I firmly believe that the one aspect of recruiting that has the most potential to improve the speed of talent identification (the time to find metric) and increase the quality and quantity of candidates identified is electronic talent discovery and identification. With each passing day, there is more data available on more people somewhere – on a social network, in a resume database, or in your ATS – and it will only increase and accelerate. The ability to slice and dice human capital data will afford companies a HUGE competitive advantage.

I will never get tired of quoting this passage from Google’s blog: “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.”

The ability to extract value out of human capital data is already, and will continue to be, THE complimentary scarce in recruiting and staffing – but most people just don’t know it yet. ATS/Recruiting CRM vendors need to step up, recognize this, and offer their clients solutions that enable them to truly capitalize on their human capital data and offer them a competitive advantage.

If anything, I feel that employers and staffing firms should provide their recruiters access to MORE powerful and capable candidate search functionality than publicly and widely available resume databases or social networks. If they don’t, their ATS will continue to be the candidate source of last resort.

I believe that ATS/CRM apps should essentially serve as talent intelligence solutions, not unlike business intelligence solutions and decision support systems. The power lies primarily in the the human capital data/information stored within, and the ability to retrieve and analyze that information for talent identification and to make hiring decisions. 

One Thing has Changed

I don’t believe that the majority of the recruiting life cycle has changed over the past 20 years, or will change all that much in the future. Building relationships with current and potential candidates will always be at the heart of the recruiting process.

However, the one step in the recruiting process that has changed dramatically is sourcing, or talent discovery/identification. Information systems and applications have evolved rapidly over the past 20 years, and will likely continue to do so. With more information available about more people growing with each passing day, information retrieval becomes absolutely critical.

The ability to instantly retrieve information about the right people at the right time can accelerate a company’s ability to build relationships with more of the right people more quickly, leading to faster and higher quality hires with less effort.

If you find that concept interesting, I suggest you read these two posts about Lean/Just-in-Time recruiting.

A Call to All ATS/Recruiting CRM Vendors

If you work for or use an ATS that has strong candidate search functionality – Congratulations, you are among the fortunate few! Vendors – make sure your customers fully understand and leverage that power. Users – take full advantage of the candidate search capability, and be sure to not use your ATS as a source of last resort. Those candidates in your ATS are there for a reason – either they expressed interest in joining your company, or someone in your company expressed interest in them! 

If you work for an ATS vendor with poor/limited candidate search functionality - Why do you offer sub-par candidate search capability? Recognize that the future of human capital information systems lies primarily in talent discovery and identification. Either build in your own robust candidate search capability, or simply integrate any one of a number of excellent 3rd party text search and/or resume parse/search/match applications that are available. Educate your current and potential customers and explain to them the value and potential of human capital data. CRM functionality is great, but is of little value without the ability to find the right people to begin to manage relationships with in the first place!

If you currently use an ATS with poor/limited candidate search capability - Send this article to your vendor. Let me know how they respond, and if/how they can answer the question of why they offer such poor/limited candidate search functionality. They’re essentially putting you at a competitive disadvantage!

I recently received a request from a reader to come up with some example Boolean Strings for finding software engineers on Facebook who are from Top 10 schools (Stanford, Berkeley, MIT, CMU, etc) and live in the Silicon Valley.

***Quick disclaimer***
I am definitely not a Facebook sourcing guru – I don’t see it as a high yield source for proactive and highly precise sourcing as it is a relatively “shallow” source of information, it’s search interfaces are quite limited, and when x-raying into Facebook you can’t see much information. I’d invite anyone reading this that has suggestions and best practices to please add them.

Okay, now that I got that out of the way, searching inside Facebook for people that you don’t “know” (they aren’t your “friends” yet) has become more and more restricted over time. There are a few ways to search for people within Facebook – I will cover 3.

Inside Facebook

#1 Facebook’s basic search interface
http://www.facebook.com/srch.php?ref=ffffc

As you can see from the screenshot, on this page you can search by school OR employer. Not a real help for what you’re trying to accomplish – finding web developers from specific schools. Although you can search by school here, it doesn’t allow us to drill down into interests, combine a school with an employer, or even pick a location.

#2 Another Facebook search interface

On the previous screenshot’s interface, I actually ran a search only choosing Berkeley and did not pick an employer. Here are the results:

Once you get the results – you can see it’s over 500 and they are from all years/graduating classes and locations. However, if you scroll down to the bottom, you can now add a specific company. I chose Sun Microsystems – that narrows the results down to 227

And you can sift through the results to check location and see that many of the results are in fact in the Silicon Valley, and also view each person’s friends to try and find more people. You could do this systematically covering all of the top schools you’d like to target and all of the top employers in the Silicon Valley you’d like to see people from. This can be a good start to reaching out to some of these people and becoming their “friend,” or taking the information you find here and performing some more research, cross-referencing LinkedIn, blogs, and other sites to find out more about them. However – with this search inside Facebook, we still don’t have any control over what these people actually do.

#3 Facebook’s “advanced” search
http://www.facebook.com/advanced.php

Here you can find Facebook’s “advanced” search interface – where you can search within your network and Friends or your location. So if you live in the Silicon Valley, you should be able to search profiles in your desired area.

Although this is supposedly an “advanced” search interface – you can see it is actually quite limited. On this page you cannot search by school. However, you can search by academic concentration (you could shoot for Computer Science and such), activities and interests (web development and design…), as well as company and position (target company and software engineer, etc.). However, because you cannot search by specific school – this interface does not enable you to achieve your goal of targeting people by skill/experience as well as college/university. But for other searches – this interface can yield results for you.

Outside of Facebook
We can use the site: command on most major search engines to look specifically within Facebook and try to find people.

• Sometimes people don’t enter all of their information – like school, experience, titles, skills, specific location, etc. If it’s not there – you can’t find it.
• When using a search engine to search into a site like Facebook, you’re not searching structured data. So searching for Stanford might yield a result where the word “Stanford” is mentioned on the profile somewhere, but it isn’t where the person when to school. That’s what I call a false positive.
• To my knowledge, you can only find public profiles using the site:command

I was not provided any specific programming languages or frameworks to target in my searches, so I just chose a few as an example – they can be switched up as you see fit. I also started with 2 of the schools that were mentioned – feel free to build out the list.

Using a search engine like Google:
site:facebook.com (java | php | python | C#) inurl:people “silicon valley” (stanford | berkeley)

You’ll notice that when you run that search and check the results – you won’t find a large number. I found 2. The issue goes back to the point I made about if a person doesn’t explicitly mention a specific search term you’re looking for – you simply can’t find them by using that search term. Facebook isn’t a resume database – most people don’t fully flesh out their profiles with tons of technical detail with regard to their skills and experience.

When it comes to reviewing the results – you won’t see the programming languages or even the college names (sometimes) because you aren’t connected to, or in Facebook-speak aren’t “friends” with the people – so you can’t see their profile page where the search terms are. However, they are in there somewhere.

Let’s look at one of the results I got:

You can try to add people like this as a friend and then contact them, or simply try sending them a message, or view his friends – and from checking them, several went to Stanford. But without digging more into each of them, we can’t tell from the surface what they do (web developers or lawyers…), or where they live. If you click on Kai you can see he is a Stanford ’05 alum.

You could run the same search but go a little North to the bay area just to check it out:
site:facebook.com (java | php | python | C#) CA inurl:people “san francisco” (stanford | Berkeley)

You could also search for groups that might contain web developers:
site:facebook.com inurl:group (software | web | internet | online | java | php | python) (~develop | ~design | ~architect) “silicon valley”

Here are a few I stumbled across:

Silicon Valley CodeCamp
http://www.facebook.com/group.php?gid=6125701762

Stanford Entrepreneur Network
http://www.facebook.com/group.php?gid=5950997467

Bay Area Girl Geek Dinners
http://www.facebook.com/group.php?gid=20463663760

LinkedIn

Since searching Facebook was a low-yield exercise, I’d feel guilty ending this post without getting you better results. So let’s focus the same kind of searches on LinkedIn and see what we get:

site:linkedin.com (Berkeley | Stanford) (java | php | python | .Net) (web | internet | software) (develop | design) “san francisco bay area” -intitle:directory -intitle:updated

569 results – and I only tried 2 schools. I used a San Jose, CA zip code in LinkedIn – it considers the San Jose area part of the “San Francisco Bay Area” – a 50 mile radius. Searching into LinkedIn using the site: command is fraught with the same issues as we encountered when searching Facebook – it’s unstructured data. So do expect some false positives – it won’t be 100% perfect. I checked through the results and many are local developers who have gone to Stanford or Berkeley.

Of course, you could also search inside of LinkedIn – but that’s another post.

The Internet

And if you wanted to try and take a shot at finding resumes on the Internet itself:

(inurl:resume | intitle:resume) (java | asp | C# | perl | python | php) (design | develop) 94002..95156 CA (berkeley | Stanford | MIT) (web | internet) -job -jobs

That’s a 25 mile radius search from 95125 and yields 586 results. Sweet.

So can anyone see why I’m not crazy about using Boolean strings to search Facebook? Can you find great people in Facebook? Sure! Can you run precise queries to quickly find large volumes of highly matched people with specific skills and experience? No.

Would an oil company avoid drilling the largest oil fields they have access to and instead spend tons of time and money looking for and drilling the smallest oil deposits they could find? It only makes logical sense for sourcers and recruiters to leverage high yield sources first and low yield sources last.

I hope you found this helpful!

-Boolean Black Belt

By my definition, Talent Mining is a simple adaptation of Data Mining, which according to Wikipedia is the process of sorting through large amounts of data and picking out relevant information, or “the nontrivial extraction of implicit, previously unknown, and potentially useful information from data” and “the science of extracting useful information from large data sets or databases.”

I define Talent Mining as the science of sorting through large amounts of human capital/talent-related data, typically found in resume databases, on the Internet, in social networking profiles, blog posts, etc., and extracting out relevant and useful information from the data that can be used for talent identification and acquisition.  

Talent Mining is commonly performed manually and automatically, through the creation and execution (or saving for routine execution, as in the case of search agents or alerts) of Boolean search strings to retreive human capital/talent data from which a recruiter can use for knowledge discovery and talent identification and acquisition.

True Talent Mining goes well beyond “buzzword matching,” and in the hands of an expert Talent Miner, Boolean search strings can be used to perform Semantic Search - using semantics, or the science of meaning in language to produce highly relevant search results – even from unstructured data. How’s THAT for sexy?

Look for a post on Semantic Search coming soon.