There are individuals in the HR/recruiting industry who believe that searching databases, the Internet, and social networking sites to source talent is relatively easy and that it can be automated through the use of technology.

I am happy to say that unfortunately for them, it’s not that simple.

While anyone can run a basic search and find some people, there are actually many different levels of talent mining – which I define as leveraging human capital data (in the form of resumes, social media profiles, etc.) for talent discovery and identification.

In this article, I am going to explain how there are at least 5 distinct levels of candidate sourcing – most of which cannot be replicated by software solutions and require a person with specific skills and abilities. 

Level 1 Sourcing / Talent Mining

Level 1 sourcing is essentially “buzzword bingo.” It involves little more than taking job titles and required skill terms from job descriptions, using them as search terms, and then performing straight lexical (word for word, title for title) matching. 

As such a superficial level of keyword sourcing and matching, Level 1 sourcing does not require any deep understanding of the roles, skills, responsibilities, or technologies involved in the hiring profiles or the candidates.

This level of basic keyword and title searching and matching will produce results, and this is where some people get the false sense that sourcing is easy. Here’s the catch - the results are limited to only those people who happen to match the titles and keywords search for. Which is never all of the best candidates that you have access to.

A single search cannot find all qualified candidates, as it will both include and exclude qualified candidates.

The danger of Level 1 sourcing lies in the fact that it will not (and can not) find people who are qualified but do not happen to have the exact titles searched for, nor those people who actually do have the right skills and experience but who 1) simply don’t happpen to mention all of them in their resume or social media profile, and/or 2) express their matching skills and experience using words that differ from those used in the job description and required skills – and thus those used in the search.

Level 1 sourcing creates hidden talent pools – entire populations of qualified candidates that you have access to, but your searches never retrieve them. If you didn’t find it, it doesn’t exist, right? :-)  

The good news is that level 1 sourcing works, gets results, and can be easily be performed by “junior” personnel/researchers, because almost anyone can match titles and keywords. Additionally, Level 1 sourcing can be completely automated using software - why pay people to match keywords when matching applications can do it for considerably less than $5 per hour?

The bad news is that in addition to creating huge hidden talent pools of fantastic people who will not and can not be found, level 1 sourcing provides no competitive advantage. If two companies are performing level 1 searching for the same types of people, they will find the same candidates. Same titles and keywords = same results. Interestingly enough – they will also NOT find the same people.

Think about it.

Level 2 Sourcing / Talent Mining

Level 2 sourcing goes beyond literal lexical matching and takes a step into conceptual search territory. Instead of relying solely on the exact titles and experience keywords provided in a given job description, level 2 sourcing involves the utilization of synonymous terms and concepts.

For example, let’s say you were sourcing for a position with a title of “Safety Physician.” While a level 1 sourcer would search only for the exact title of “Safety Physician” and find people who happen to have that title, a level 2 sourcer would perform research and discover that other organizations use a variety of other titles to describe the same role, such as Associate Director of PVRM, Pharmacovigilance Physician, Senior Drug Safety Associate, Global Safety Senior Medical Scientist, Global Pharmacovigilance (Contract) Physician, and Medical Director, Drug Safety & Pharmacovigilance. 

A level 1 sourcer using only the title “Safety Physician” in their search could not find appropriately qualified candidates that used one of the above titles instead of ”Safety Physician.” To the level 1 sourcer, those other candidates simply don’t exist – they are unware of their existence. However, a level 2 sourcer would find them.

At the skills search level, a level 1 sourcer looking to find software engineers with “Ruby on Rails” experience would search for that exact phrase, and would find only those people who happen to mention it. A level 2 sourcer would perform research and discover that some people with that experience may instead express “Ruby on Rails” as Rails, Ruby, or simply RoR. As such, the level 2 sourcer would be able to find candidates that the level 1 sourcer cannot.

Level 2 sourcing can be automated - there are many vendors (including Monster’s Power Resume Search and TalentSpring) offering applications that will take basic title and keyword searches and automatically search for synonymous titles, words, and phrases.

However, there limitations with automated solutions, and there are a few aspects of level 2 sourcing that can only be performed by humans:  

1. It takes a human being to interpret and understand the hiring need, which can not be effectively conveyed soley by a job description, titles, and required skills, to determine what search terms to use (and which ones not to use!).
2. Only a human sourcer can analyze the relevance of the results from initial searches and adaptively learn from them to creatively refine successive searches to increase both the quantity and the quality of relevant results.
3. Applications have no awareness of hidden talent pools - only human sourcers have the ability to be aware that their search criteria may actually eliminate qualified candidates. This awareness enables them to take appropriate action to alter their searches to uncover candidates that previous searches eliminated. 

Level 3 Sourcing / Talent Mining

Level 3 sourcing involves searching for and identifying what isn’t explicitly mentioned by candidates – in other words, searching for what isn’t there.

The fact is, most people have skills and experience that they do not directly express in their resumes and social media profiles. This is because: 

• People cannot effectively be reduced to and represented by a text-based document or form
• Job seekers are NOT professional resume writers
• Candidates don’t mention every skill they have or responsibility they’ve had, nor do they describe every environment they’ve worked in
• Most people still believe shorter resumes are better, which means that they are removing experience (data/info) from their resumes which can no longer be searched for
• There are many ways of expressing the same skills and experience
• Employers often don’t use the same job titles for the same job functions
• Candidates don’t create their resumes thinking how you will search for them
• Sometimes candidates don’t even use correct terminology 

This phenomenon creates HUGE volumes of resumes, candidate records, and social network profiles of people who have skills and experience that cannot be directly searched for because it isn’t there. Most sourcers and recruiters simply aren’t aware of these people because they can’t be returned by standard (level 1 and 2) search tactics.

Level 3 sourcing involves incorporating an understanding of the intrinsic limitations of resumes and social media profiles detailed above into sourcing strategies and tactics, and is a skill that can only be developed over time from observation and direct experience.

For example, let’s say a manager has an opening for someone with Rational Unified Process experience.

• A level 1 sourcer would search for “Rational Unified Process.” 
• A level 2 sourcer (human OR otherwise) would/could search for synonymous terms (RUP OR “Rational Unified Process”). 
• A level 3 sourcer would be able to find people with Rational Unified Process experience without actually searching for the terms by researching which companies use RUP and searching specifically for people who have worked for them but who do not say (RUP OR “Rational Unified Process”) by using the NOT operator.

A level 3 sourcer is capable of finding the same candidates someone who employs only level 1 and 2 sourcing tactics, as well as candidates level 1 and 2 sourcers cannot. Additionally, a level 3 sourcer can find candidates that matching applications employing level 2 sourcing concept/semantic search cannot – this is because an application cannot effectively search for words and concepts that cannot be found because they simply aren’t there.  

Level 4 Sourcing / Talent Mining

Level 4 sourcing involves searching for responsibilities and capabilities, not keywords or titles.

Moreover, level 4 sourcing takes concept searching beyond synonymous words and phrases (level 2 sourcing) and targets meaning at the sentence level – specifically targeting what people DO, not just what they SAY.

To the best of my knowledge, there are no applications available today that perform dynamic sentence-level (not static phrase level) semantic search via verb/noun combinations. However, any human sourcer can perform level 4 sourcing manually by searching for verb/noun cominations using a search engine that supports proximity search.

That includes Monster “classic” – which supports the NEAR operator (fixed proximity within 10 words), the Exalead Internet search engine, and nearly any ATS/CRM application which uses Lucene or dtSearch as their text search engine.

Search Example 1

Let’s say you’re looking for someone who has had experience performing administrative support for C-level executives.  Using Monster, you could use a search something like this:

support* near (CEO or CFO or CTO or CIO or “C-Level” or chief*)

Essentially this search is looking for any permutation of the verb “support” to be mentioned within 10 words (forwards or backwards) of one of the many ways of expressing a C-level title. This can effectively target sentences in which people express the responsibility of supporting C-level executives.

Here are snippets from 3 different resumes. Notice that no title search was necessary due to the power of targeting sentence-level meaning:

Search Example 2

If you were looking for someone who had experience configuring Juniper routers, you could run a search like this on Monster:

config* near juniper near router*

This search is essentially looking for people who mention that they have experience configuring Juniper routers, because some permutation of the root “config” has to be mentioned within 10 words of Juniper, which also has to be mentioned within 10 words of router or routers. In most cases, due to the proximity specifications, these 3 words variants will be found in the same sentence – expressing Juniper router configuration responsibility.  

Does it work? You decide.

Search Example 3

If you use PCRecruiter (which uses Lucene for text search) and you were looking for people who had experience creating Access databases, you could run this search:

“created access database”~7

That search is asking the database for any result in which the words “created,” Access,” and “Database” are all within 7 words of each other. And it works. 

Notice that this is not an exact phrase search - in the relevant phrases, the words are actually in a different order than expressed in the search above, yet the concept is the same.

Level 4 sourcing is user-defined, grammatical natural language search.

As complex as that sounds, it’s essentially intelligent keyword search empowered by proximity search capability (extended Boolean) that effectively enables semantic search targeting verb/noun combinations. Best of all, it produces highly relevant results, matched at the responsibility level – what people have done and can do, not just words they happen to mention. 

Level 5 Sourcing / Talent Mining

Level 5 sourcing is a creative use of human capital data in which sourcers deliberately search for the “wrong people” in order to find the “right people.” 

This can involve #1 searching for under/overqualified professionals – people who do not have enough years of experience for a specific position, or those who are very experienced and likely to be looking for compensation above what you can offer for a given position, as well as #2 searching for people who likely work with or know the professionals you need to find.

In some ways this isn’t much different than cold calling, yet it has the advantage of specificity and candidate variable control. For example, let’s say you’re looking for C# software engineers with at least 3 years of SharePoint portal development experience, and you know from experience that people with more than 5 years of applicable experience tend to want a higher level of compensation than you are able to offer.

Once you’ve exhausted all searches/sources for direct matches to your need (C# software engineers with 3 to 5 years of SharePoint portal development experience), you could deliberately search for people with precisely the right experience, but less than 3 years or more than 5.

While you may not be able to immediately assist these people, by identifying them ahead of need you can effectively and proactively build your candidate pipelines for junior and more senior C#/SharePoint portal developers, and you afford yourself the opportunity to network with these individuals to identify people they know who do have 3-5 years of applicable experience. 

Going one step further, you could search specifically for people who would have experience working with your target candidate pool. This could include software testers, business analysts, development/project managers, etc. By searching for, identifying and contacting testers, business analysts, and managers who have experience working on C#/SharePoint portal projects, you can proactively build your pipeline of candidates with these skills, as well as network with them in an effort to identify C# software engineers with SharePoint portal development experience.

Beyond the 5 Levels 

I believe that it is all too easy for people to oversimplify the sourcing role and function, as well as suggest that sourcing is easy, that it can be effectively mastered and performed by junior personnel, and that it can be fully automated through the use of search and match applications.

All of which is precisely why I took the time to analyze talent mining and share with you the fact that there are at least 5 distinct levels of candidate sourcing. 

I say “at least” because I am not satisfied to say that there are only 5 levels - there may be more than 5 distinct levels of talent mining and candidate sourcing. I’m looking forward to the sourcing and recruiting cognoscenti to digest my assessment of sourcing/talent mining and offer their thoughts and feedback.

You’ll notice that in my assessment, only level 1 and to some extent level 2 sourcing can be performed solely by search and match applications without human involvement.

Some aspects of level 2 sourcing can only be accomplished by a living, breathing, thinking person. For example: Interpreting and understanding the hiring need, analyzing the relevance of the results from initial searches and adaptively learning from them to creatively refine successive searches to increase both the quantity and the quality of relevant results, and leveraging an awareness of hidden talent pools to take appropriate action to alter searches to specifically uncover candidates that previous searches eliminated.

Similarly, only people are capable of interfacing with and searching databases and Internet sites to perform level 3 – 5 sourcing.

I believe that the solution to the talent sourcing challenge lies in:

1. The ability of  people to truly understand the positions being sourced for, an awareness and appreciation of the intrinsic limitations of human capital data, and the ability to employ sound search/data mining tactics and strategies to go beyond these limitations and leverage human capital data to find all of the best candidates, both directly and indirectly.
2. Companies finally “getting it” by understanding and appreciating of the true value of human capital data, which is directly proportional to the ability to quickly retrieve exactly what you want when you want it. This should lead companies to offer their sourcing and recruiting teams better search capability and technology (for both internal databases and external resources). 

Here is the expanded slide deck from my SourceCon 2010 Keynote: Resume Sourcing and Matching – Artificial Intelligence vs. Human Cognition. It contains all of the talking points as text so you are not left guessing as to what I spoke to during the live presentation.

You’ll learn about the intrinsic and often overlooked challenges associated with sourcing resumes (it’s deceptively complex), what artificially intelligent semantic search and match applications claim to do and how they actually work, the limits of artificial intelligence, what people can do that semantic search applications cannot, the 5 levels of semantic search,  the 5 levels of talent mining, and what I think is the ideal candidate sourcing solution.

Are you attending or thinking about attending SourceCon 2010 in San Diego in March?

I am going to be the keynote speaker for the event, and I will be presenting on Artificial Intelligence vs. Human Cognition when it comes to sourcing and matching resumes.

If you’re curious to know what kinds of things I’ll be addressing during the session, here is a sneak peek:

1. The intrinsic and often overlooked challenges associated with sourcing resumes
2. What artificially intelligent semantic search and match applications claim to do and how they actually work
3. The limits of artificial intelligence
4. What people can do that semantic search applications cannot
5. The 5 levels of semantic search
6. The 5 levels of secondary/e-sourcing
7. What I believe would be the ideal candidate sourcing/talent identification solution

If you’ve ever wondered about the fantastic claims that some of the semantic search application vendors on the market make as to how their solution can mimic a senior recruiter when finding candidates, then you will be very interested in hearing what I have to say about the reality of what they can do.

If you’re a sourcer and you’re concerned that your role/position might eventually be replaced by sourcing software, you will be encouraged by my analysis and supporting arguments that explain why the abilities of creative and investigative sourcers will always be in demand – tomorrow and 50 years from now.

I hope you will be able to attend SourceCon 2010 – I know I’m looking forward to it!

If you’re unable to attend, the good news is that the presentations will likely be streamed. Additionally, I plan on posting my expanded slide deck, including all talking points – so you won’t be stuck staring at some pretty pictures wondering what the heck I talked about.

Many individuals and organizations treat the sourcing role and function of recruiting – searching for and identifying potential candidates – as an entry level position, and/or a simple and basic task that does not require much skill or experience. 

I agree.

I believe that it does not take much skill or experience to simply transcribe job titles and required skill keywords into LinkedIn, Monster, or an ATS and click “search.”

However, that oversimplified view of sourcing talent only describes the most basic level of talent identification, of which, I believe there are at least two.

Level 1 Sourcing

Level 1 Sourcing involves little more than taking titles and words from job descriptions and entering them into Internet search engines, social networks, job board resume databases, and applicant tracking systems to search for candidates.   

This is the proverbial “buzzword bingo,” and does not necessarily involve any real understanding (beyond surface level) of the roles, skills, responsibilities, or technologies involved in the hiring profiles or the candidates. These basic search terms produce search results that are then cursorily reviewed for keyword matching. 

Level 1 Sourcing involves a level of matching potential candidates to hiring profiles that is often superficial and generic – very little, if any, interpretive analysis is performed. This level of sourcing can in fact quite easily be performed by “junior” personnel/researchers – almost anyone can match keywords.

Not only can Level 1 Sourcing be performed by junior associates, it can (and often is) outsourced for $5 – $7 an hour.

However, don’t be fooled into thinking you are getting something fantastic for that $5 – $7 an hour – you’re getting exactly what you’ve paid for. Which is Level 1 Sourcing.

In my opinion, there is no need to outsource Level 1 Sourcing, because it does not require any deep understanding of the roles being sourced for, nor does it involve any true analysis or creativity. As such, Level 1 Sourcing is well suited for total automation. Why pay people to match keywords when matching applications can do it for you for considerably less than $5 per hour?

Many people are blissfully unaware of the fact that Level 1 Sourcers from any company will essentially find the same potential candidates as any other Level 1 Sourcer. It’s a simple equation: same keywords = same results. This is one of the reasons why Level 1 Sourcing provides no competitive advantage. 

Additionally, while Level 1 Sourcers can and will find SOME candidates, they will not and can not find ALL potentially qualified candidates available to them in the sources they are searching - that would be impossible, for many reasons that I’ve written about previously that are beyond the scope of this post. 

And finally, Level 1 Sourcers are typically unaware of the people that are in the ATS, job board resume database, or social network that they are searching that their queries did not return. In fact, to them, anyone that they don’t find simply doesn’t exist. 

Level 2 Sourcing

This is the good stuff. Level 2 Sourcing moves well beyond simple keyword matching and most certainly beyond a basic mastery of Boolean logic. 

Boolean logic is easy to learn – after all, there’s only 3 main operators! However, the “magic” of leveraging databases and information systems for talent identification does not lie in the Boolean search operators themselves, but in the following process: 

1. Analyzing, understanding, and interpreting job opening/position requirements - including elements which may or may not be explicitly mentioned in the position description or BQ’s
2. Taking that understanding and intelligently and creatively selecting titles, skills, technologies, companies, responsibilities, terms, etc., to include (or to purposefully exclude!) in a query employing appropriate Boolean operators and query modifiers
3. Analyzing 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. Repeating steps 3 and 4 until an acceptably large volume of highly relevant results is achieved   

The “real” work of creating effective Boolean search strings lies in the interpretive analysis of the need, in determining what terms to include and exclude from searches and in what specific combination, in the analysis of the relevance of the initial search results, and in the adaptive process of learning from the results to creatively refine the Boolean search strings to find well qualified candidates – people who are highly likely to be (or know!) the right match for any particular hiring need. 

Unlike Level 1 Sourcing, Level 2 Sourcing involves and in fact requires a deeper understanding of the roles, skills, responsibilities, and technologies involved in the hiring profiles being sourced for. In this regard, Level 2 Sourcing goes well beyond explicit keyword matching and deep into implied experience and capability matching.

In addition to finding all of the candidates that Level 1 Sourcers can find, Level 2 Sourcers can also find the candidates that Level 1 Sourcers have access to, but can not and do not find. Interestingly, all Level 2 Sourcers will not find all of the same candidates, specifically due to their varying experience and creative and analytical ability.

And unlike Level 1 Sourcers, Level 2 Sourcers are acutely aware of the candidates they have not found, because they understand that every Boolean string and search strategy will find some candidates, and exclude others.

Level 2 Sourcing is Not a Junior Role and Cannot Be Automated

It is precisely because of the aforementioned reasons that Level 2 Sourcing cannot be performed by  junior level associates – it is not an entry level role, nor can it be outsourced for $5 – $7 an hour. Okay, it CAN be outsourced at those rates, but you won’t get Level 2 results. Remember, you get what you pay for.

Level 2 sourcing cannot be automated, regardless of what the vendor representatives of “artificial intelligence” resume parse/match applications may claim. This is because Level 2 sourcing requires “a posteriori” knowledge – which comes from experience, which comprises knowledge and skill gained through involvement and exposure. 

Applications do not accumulate experience or gain knowledge or skill, in the true sense of the terms.

AI matching applications essentially perform a form of pattern recognition to classify data through parsing resumes and employing a keyword/phrase taxonomy, which is built based on “a priori” knowledge/information extracted from the patterns and programmed into the matching logic. 

I recently spoke at the PDS Technology Conference and had the honor of seeing Dr. Michio Kaku present on the world of 2020 and beyond. Dr. Kaku believes that “Progress in artificial intelligence may come to a gradual halt around 2020. The two problems facing AI are pattern recognition and common sense.” 

I was happy to hear that Dr. Michio Kaku believes that the employment market of the future will be “dominated by jobs involving common sense (e.g. leadership, judgment, entertainment, art, analysis, creativity) and pattern recognition (e.g. vision and non-repetitive jobs).  Jobs like brokers, tellers, agents, low level accountants and jobs involving inventory and repetition will be eliminated.”

That’s great news for anyone performing Level 2 Sourcing, primarily because it requires creativity, interpretive analysis, judgment, and common sense (a natural understanding based upon experience) - four things that machines and applications are intrinsically incapable of.

Unlike AI matching applications, Level 2 Sourcers intrinsically understand that resumes and social media profiles are imperfect and incomplete representations of the people who created them, and that they often do not explicitly mention all of their skills and experience. As such, Level 2 Sourcers are not only able to find qualified candidates based on the words they use - many can also specifically search for and find people who have experience that they do not mention. In other words, some Level 2 Sourcers can find people based on what they don’t say. This is a skill that can only be developed over time from observation and experience.  

Final Thoughts

Level 1 Sourcing can be performed by entry level associates or be completely automated, as the level of matching produced is superficial and based primarily on explicit keyword and phrase matching. This can be quite sufficient for static and repetitive hiring needs for simple hiring profiles, where title searches will often suffice.

The value and the results provided by Level 1 and Level 2 Sourcing is vastly different - this is why some organizations see the sourcing function as a low level or junior role, simply outsource it for $5 – $7 and hour, or completely automate it. Interestingly, there are sourcers who make $50 to over $100 an hour, and they are worth every penny for the competitive advantage  and value they provide to the organizations they support.

Dr. Michio Kaku would classify Level 1 Sourcing as ”commodity based capital,” in that it is a product that is the same no matter who produces it - man, woman, or machine.

People who perform Level 2 Sourcing are true knowledge workers, whose value is intellectual capital – based in creativity, judgment, analysis, ”common sense” and “a posteriori” knowledge developed over time based upon experience – similar to senior Financial Analysts, Business Analysts, Data Analysts and Business Intelligence Analysts. Level 2 Sourcers produce a product that is quite different based on who produces it, and it cannot be reliably replicated by machines.

To be sure, one could easily break Level 2 Sourcing out to at least 3 different levels, because to lump everything more advanced and sophisticated than Level 1 Sourcing into one broad category is horribly limiting, but for the purposes of this article, it shall suffice.

Human Capital Data data is the sword of the 21st century – those who wield it well are the Sourcing Samurai.

There are many software vendors who offer solutions to HR, recruiting, and sourcing organizations that claim to have automated candidate search and match capability. These applications can take your search, a job description, or an example resume and claim to leverage semantic search, fuzzy logic and/or Artificial Intelligence search technology to return relevant results. 

I’ve had the opportunity to use and evaluate 4 “big name” semantic and intelligent search/match applications for identifying candidates, and I am currently implementing one of them, including cutomizing the taxonomies and ontologies - so I have quite a bit of hands-on, practical experience with semantic search applications. While I do think that intelligent semantic search and match applications definitely have a place in the sourcing and recruiting process, they should not be looked at as solutions to a problem, challenge, or a deficiency in your skills or your team’s.

Before implementing a semantic search application, it is important to first understand the manual search process, how automated semantic search works, and the intrinsic limitations of of semantic search applications. 

People Do the Work, Computers Move Information

Eiji Toyoda, the fifth president of Toyota Motor Corporation, who collaborated Taiichi Ohno to fine tune the concept of Kaizen as well as to develop the core concepts of the ‘Toyota Way’, explains brilliantly: ”Society has reached the point where one can push a button and be immediately 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. We must remember that in the end it is the individual human being who must solve the problems.” 

As I have written before, thinking is the most critical step in the candidate sourcing process, and regardless of “Artificial Intelligence” and semantic search marketing hype, applications do not have any true cognitive power, nor do they have the ability to be creative or learn as people do. Thus I could not agree more with Jeffrey Liker’s (author of The Toyota Way) assessment - ”People do the work, computers move information.”

Do Not Automate What You Cannot Already Perform Manually

If you are not a highly proficient Talent Miner, capable of manually querying databases and systems with Boolean logic and a high degree of precision to find not only the obvious and easy to find candidates, but also the those residing in Hidden Talent Pools – how can you even HOPE to begin to evaluate what an automated semantic search solution is claiming to do?

You can’t.

If you’re going to buy and use product or service of ANY kind, and you don’t really understand what it does or even exactly how it does it (beyond the marketing hype), and you can’t tell if it REALLY does everything it claims to – how and at what level can you determine if the product or service truly meets your needs and will provide true value?  

If you or your organization struggles with the challenge of finding the right candidates at the right time on a consistent basis, implementing an automated search and match application will not magically solve this “problem” for you. The real underlying problem is likely that you or your organization currently does not #1 possess the right skills or the right people who are highly proficient at candidate sourcing and/or #2 have documented highly effective candidate sourcing processes and best practices that are consistently trained to and followed. No software application can fix either or both of those issues.

Jane Beseda, Group VP at Toyota, believes that it is best to “First work out the manual process, and then automate it. Try to build into the system as much flexibility as you possibly can so you can continue to kaizen the process as your business changes” because “…you can kaizen (continually improve) people processes very easily, but it is hard to kaizen a machine (or application).” I agree wholeheartedly.

Master the Sourcing Process Manually, THEN Introduce Automation

If you or the people on your team don’t fully understand how to effectively leveraging technology for talent identification and you can’t perform it manually, I do not recommend implementing an automated search/match solution. It is critical to #1 First develop your skills and ability (or your team’s) to manually source candidates, #2 Document your sourcing best practices and processes, #3 Make sure that they are consistently trained and applied, and #4 Strive to continually improve them. THEN you can go about evaluating automated search and match solutions because you will actually have the ability to truly assess and understand what the products do, you will be able to determine whether or not they meet your needs and can provide real value to your organization, and you will be able to assess how you can possibly best leverage them into your sourcing efforts.

For example, if you don’t really comprehend the concept of semantic search and how it can be applied in candidate sourcing (e.g., you can manually leverage semantic search in your Boolean strings at an expert level or you have someone on your team that can), you won’t be able to tell whether or not a application claiming to leverage semantic search is really effectively doing so, if it will provide any real value to your sourcing efforts, or if it is finding all of the best possible matches in the sources it is searching.

Speaking of Semantic Search

Many automated candidate search and match solutions claim to leverage semantic search. I’ve written many articles on the topic of how semantics can be leveraged for sourcing candidates – so I won’t go into too much detail here – but semantics refers to the study of meaning, as inherent at the levels of words, phrases, and sentences.

Sourcers and recruiters can leverage semantics in their sourcing efforts to more quickly find more relevant results. When the search results match the intended MEANING of the search (the INTENT), there is a semantic similarity between the INTENT of search and its results. In other words – you get what you’re looking for. When search results simply match the search terms but not the INTENT of the search, there only a lexical similarity between the search and its results. In other words – the words match, but you don’t get what you’re looking for.

For example, if I were manually searching for Linux systems administrators – that is the INTENT of my search – to find people who have been primarily responsible for administering Linux systems, regardless of exactly how they express that experience in their resume. However, with basic keyword and title search, I will find a mix of relevant results (people who have been responsible for Linux administration) and irrelevant lexical-only match results (people who happen to mention the search terms of Linux and systems and administraion somwehere in their resumes or profiles, but who have not been primarily responsible for Linux systems administration).

However, as a thinking human being, I can truly learn from the search results and continually improve and adapt my search strategies and keywords to leverage the language used specifically by people who have been primarily responsible for administering Linux systems (using semantics – the actual meaning of specific words and phrases), ensuring more of the results returned actually match the INTENT of my search.

So How Do Applications Achieve Semantic Search?

To provide relevant search results, various automated search and match applications claim to:

• Have “pseudo-AI” – these perform semantic “concept matching” based on a taxonomy/ontology (lists of terms, equivalent terms and their relationships)
• Perform “natural language” search – using pre-programmed phrases and/or proximity matching
• Execute “context-aware” semantic matching of jobs and resumes
• Perform fuzzy matching – returning results matching words that were not specifically searched for by the user, but that the application “thinks” is likely to be relevant
• Have “full AI” – the software is designed with algorithms to create relationships between words, abbreviations and phrases dynamically and without human intervention

Applications Are Not Mind Readers

No software application is capable of determining the INTENT of any search – it is critical to recognize and understand this, because it’s at the very core of semantic matching, although it’s not often written about when it comes to sourcing candidates. No application can actually *know* what you’re looking for – only YOU know what you’re trying to find, which is a person who has had some sort of specific experience performing certain responsibilities, often in specific environments.

Essentially, automated search/match applications take either your search terms, a job description, and/or an example resume and make their “best guess” as you your intent. Don’t get me wrong – some applications do a very good job of taking what you give them and providing some relevant matches. However, you must always be aware that these are just that – guesses – and on top of it all, they’re guesses made by an application, not a person.

Output is Limited by Input

Search/match aplications are only as good as their input - if a user is not especially adept at crafting search strings the quality of the results the application can produce will be limited. Additionally, we are all aware the resumes and job descriptions are FAR from the best representations of skills and experience, so the results produced by an application interpreting a resume or a job order is instrinsically limited.

There is no replacement for the cognitive and interpretive power of the human mind – people are much more capable of “reading between the lines” of resumes and job descriptions and getting to the true essence of the required skills and experience to determining how to best approach sourcing efforts to find the right people. Applications can claim to do this, but it’s an apples to oranges comparison.

Let’s also remember that a sourcer or recruiter can discuss the position’s requirements with the hiring manager (in most cases – if not, they should be able to!) – this information is typically beyond what any example resume or written job description can (or does) convey. Armed with this critical information and detail, a sourcer or recruiter can translate what they’ve gathered verbally from the hiring manager/team into a sourcing strategy, down to the Boolean search string level. The last time I checked, an application cannot do this. There is no replacement for good analytical and interpretive ability – it’s precisely why you can’t fully automate a business analyst’s, a data analyst’s or a financial analyst’s job.

Part of the instrinsic challenge faced by automated search and matching applications is that they are expected to make the leap from “give me what I said” (match the words) to “give me what I want” – matching your intent. Considering that an application is incapable of “understanding” your intent, how can it hope to deliver results that match your intent when your intent cannot be interpreted from an example resume, a job description, or a poorly defined search? 

Intrinsic Limitations of Automated Semantic Search/AI

Be aware that any automated semantic search/match application is only as good as its programming, taxonomy, and ontology:

• Pre-programmed lists of “relevant” or related keywords may in fact not be relevant or related to your specific search, and can get outdated quickly
• Fuzzy matching is by definition “approximate” or “inexact” matching, the very opposite of “precise”
• Applications are essentially “guessing” the intent of your search based on the keywords, resume, or job description you feed it – but finding the best talent is not a guessing game
• Some systems will return results with related words – which may in fact NOT be relevant to your specific need – will you be able to determine which are and which aren’t?
• Applications that claim to “learn” may not actually improve the relevance of results over time

The Tough Questions

Are you capable of evaluating an application “under the hood?”  When a vendor tells you that their application is “trained to identify the equivalent meanings of terms found in resumes,” do you know how to get to the bottom of exactly how their application accomplishes this, and whether or not their application can actually do it for your specific hiring profiles? Could you manually run searches to objectively evaluate the vendor’s claims? If you don’t already possess the ability to manually source candidates from information systems, what will you do when your automated search/match application fails to produce the right results, or enough of them? Do you have the ability to search your own database to find the candidates that the search/match application can not find/misses? 

My Experience and Opinion

I’ve had the opportunity to use a number of automated semantic search/match applications. I’m also currently implementing one and I’m involved in customizing the taxonomies and ontologies. I’m very excited about the features and capability of the semantic search application I’m using. However - I’ve found it does have it’s limitations, and that there is no perfect solution that magically produces the best candidates available with the push of a button, nor can an automated intelligent/semantic search application fully replace a skilled sourcer or recruiter. Augment and empower - yes. Replace – no.

As someone who is highly proficient in manual Boolean and semantic search, from everything I have gathered, most automated search and match applications simply automate basic sourcing best practices, but none that I have used do so completely or flawlessly.

In fact, some search/match apps appear to do little more than heavily leverage common title and skill terminology matching – which any sourcer or recruiter of average skill can accomplish. Other applications go overboard and get sloppy in the process, suggesting and incorporating alternate search terms it *thinks* are related and relevant to your search – and in some cases the terms are actually NOT relevant to the intent of the user’s search, causing more harm than good. But if you don’t know what to look for (past the marketing and past the huge cloud of “related” search terms the app suggests) and you don’t know how to get to the core of the search logic and the true relevance based on the intent of your search, you might just actually believe you have the solution to all of your candidate sourcing problems.

While some industries can benefit heavily from automated search and match applications – for example,  those with simple and highly consistent titles, lexicology, and phraseology(such as finance and accounting), others vary widely and change rapidly (such as Information Technology) and pose serious challenges for vendors of automated search and match solutions.

Check Your Reasoning

If you’re looking to implement an automated candidate search/match application, simply ask yourself why – and dig down to the very root of it. Is it because Boolean searching is “too hard?” Is it because you think the application can reduce your costs by reducing headcount? Do you think a search/match app can speed up the talent identification cycle?

I’ll let you in on a little secret – Boolean search isn’t that hard with the proper training. It’s more science than art, really. Which is good news, because manual talent mining via Boolean search strings can be broken down to repeatable steps, including the interpretive and analytical processes, which can be continually and dynamically improved – whereas an application cannot (easily, or at all). 

Also – semantic search/match applications are not a replacement for people, because the applications don’t actually perform real work or deliver value to the end customer. When I refer to “work” and “value,” I’m referring to how Lean and the Toyota Production System define those terms. Recall Jeffrey Liker’s assessment, “People do the work, computers move the information.” The author of The Toyota Way has also explained that ”…the only thing that adds value in any type of process…is the physical or information transformation of that product, service, or activity into something the customer wants.”

By customer, Jeffrey means the END customer – the internal hiring manager or the external client. Automated search/match apps don’t transform or produce a product that the end client wants – which is a talented person who is a great match for their need. Automated search/match applications produce resumes and profiles (i.e. “computers move the information”) and the sourcers and/or recruiters analyze and transform those resumes/profiles into fully screened, closed, and qualified candidates for the end customer (i.e., people do the work). 

My Suggestions

Automated search/match applications definitely have their place in world of sourcing and recruiting. I think they are best used to facilitate and augment the talent identification efforts of sourcers and recruiters. Some can be especially useful in quickly and simultaneously searching multiple online sources of resumes and social media profiles and parsing large volumes of results into your internal ATS/CRM – this quick and permanent human capital data capture can be a major benefit of using some search/match solutions.

Ultimately, I feel that sourcers and recruiters using AI/semantic search applications should utilize the results they produce and return as ”suggested reading” - but I would never rely solely on an application to exhaustively identify top talent, just as no one would trust a plane full of people to take off and land on autopilot. 

Whether or not you are considering purchasing an automated search and match application or service, or you’ve already implemented one, I strongly urge you to #1 First develop your skills and ability (or your team’s) to manually source candidates, #2 Document your sourcing best practices and processes, #3 Make sure that they are consistently trained and applied, and #4 Strive to continually improve them. THEN asses the best way to leverage an automated matching solution to augment your talent identification efforts.

Do not take a poorly functioning sourcing process or team and expect to fix it using an automated search/match application. Fine tune your sourcing process and best practices, develop your sourcers and recruiters with exceptional training, and then surgically insert matching technology to enhace them. Technology is a tool that exists to support your people and your processes – it is not a solution to a problem nor a replacement for a process or a person. 

Conclusion

I am in no way against inserting semantic search applications into the sourcing function (remember, I’m using one now!) – but I feel it must be done for the right reasons AND with a full understanding and capability of performing the sourcing processes manually, else you cannot continually improve your sourcing processes, nor will you be capable of picking up sourcing where the application fails to deliver.  

If finding some candidates is your end goal, then you can feel comfortable using automated search and match solutions to do all of the “heavy lifting” for you or your associates when it comes to talent identification. However, if you goal is to find all of the best candidates, then you should use an automated matching application as a tool to support your sourcers and recruiters who are well trained and effective in manual sourcing best practices.

Caveat Emptor

If you are looking to purchase an automated candidate search/match solution, or you’ve already implemented one, and you do not have the expertise or experience associated with assessing and implementing such solutions – I STRONGLY urge you to seek a neutral, third party HR/Recruiting technology consultant and involve them in the process. It is all too easy to be sold by a vendor’s marketing and messaging – but you are at a distinct disadvantage if the vendor knows more than you about the sourcing and matching function – be it manual OR automated.

Be wary – do not seek to automate a process that which you do not fully understand how to perform manually.

Partial List of Vendors of Matching Technology:

• Pure Discovery: http://www.purediscovery.com/
• Actonomy: http://www.actonomy.com/
• Semetric (Engenium): http://www.krollontrack.com/semetric/
• TalentSpring: http://www.talentspring.com/
• Sovren: http://www.sovren.com/
• BurningGlass: http://www.burning-glass.com/
• ResumeMirror: http://www.talenttech.com/

Are you a vendor and want to be added to this list? Would you like me to evaluate your product? Let me know.  Thanks!