Sourcers and Recruiters – Don’t Fear Watson or Semantic Search

I’ve read a few articles recently talking about IBM’s Watson and how the technology they developed may be the harbinger of unemployment for people in many professions.

Here’s one from Fortune magazine, asking if IBM’s Watson will put your job in jeopardy.

Here’s another suggesting that those who train others in Internet, social media, ATS, and resume database sourcing techniques and strategies will be eventually eliminated by semantic search solutions.

Watson Winning at Jeopardy isn’t Surprising

First, let’s first recognize that it’s an apples to oranges comparison between Jeopardy and sourcing/recruiting.

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

With trivia, all of the facts and information are readily accessible, completed and identifiable on the Internet, or in the case of Watson, saved on a multi-TB hard drive array.

It’s not really shocking that a highly specialized $900,000,000 to $1,800,000,000 (estimated 3 year cost of developing Watson) NLP (Natural Language Processing) computer can sort through 200 million pages of structured and unstructured content, including the full text of Wikipedia, to retrieve information faster than a human relying on memory alone.

Why is anyone surprised that Watson spanked people?

I wasn’t.

However, I can’t pass up the opportunity to point out that Watson did make mistakes – here’s one example in which Watson thought the answer was “Who is Picasso?” when the correct answer was “What is modern art?”

Who knew that the err is human, as well as inhuman?

:-)

The Unique Challenge of Human Capital Data

Unlike finding the answers to trivia questions, when it comes to finding and identifying qualified and talented people based on their resumes and social media profiles and updates, the information is often incomplete, and in many cases, critical bits of identifying data are simply not present.

For example, how do you find someone with Spring MVC experience when many people don’t mention it on their resume, nor on LinkedIn, Twitter, blogs, etc.?

I recently gave the world a tiny glimpse into the Dark Matter of LinkedIn – direct keyword, title, and even concept/relational search methods, used by humans or algorithms, can only retrieve results based on existing text.

Quite simply – if the text isn’t there to be retrieved or analyzed, a semantic search/NLP algorithm can’t do anything with it.

Good sourcers really do “read between the lines” of both the job description and requirements as well as the human capital data they are searching for and analyzing.

There is much more to high-level sourcing than keyword and title search/match.

There have been semantic solutions on the market for quite some time that can do keyword, title and concept matching reasonably well (as well as some that claim to, but don’t). The issue with those solutions that no one seems to (or wants to) realize is that they have limitations – they find some matches, exclude some, and bury others.

The real question is who, how, and why are some matches found and ranked highly, while others are excluded, and others ranked lowly but actually represent the best talent?

What Do I Know?

I have hands-on, practical experience (read: trying to find people to fill real jobs) with many of the “top shelf” semantic search applications out there, specifically designed for human capital data, so when I write or speak on the matter of semantic search, I’m not throwing around empty opinions.

I’ve seen what these solutions can do, and I’ve also directly experienced their limitations, including what they simply can’t do.

Unlike many people who write on the subject of semantic search, I have to personally find people and help others find and recruit talented, qualified candidates with highly specialized skills and experience within 24-48 hours of receiving a client request on a daily basis. If semantic search solutions (including the one I have access to) could speed up that process and help me find more and better candidates faster – trust me, I would use them!

I’ve witnessed a sourcer with 9 months of total experience find better qualified matches (and faster) than a big-name semantic search solution in front of one of the senior technical managers responsible for developing the product. It was eye-opening and even somewhat confusing for them, to say the least.

I’ve also spoken with sourcing/recruiting managers at Fortune 500 companies who have evaluated leading semantic search solutions and they passed on purchasing them because the solutions did not find more and better results faster than their sourcing/recruiting team.

Ultimately, it’s not about humans vs. technology – it’s about results.

The Solution is Part of the Problem

I’ve found the creators of semantic search products don’t seem to like it nor do they seem to really listen when you point out the flaws and limitations of their creations – and I’ve had exchanges with people who hold patents in this space.

I’ve also gotten the sense from talking with semantic search solutions providers that some of these folks believe that sourcers, recruiters and HR professionals don’t (and/or can’t!) really understand semantic search and more complex information retrieval strategies.

To their credit, if their perception (based on experience or otherwise) is that recruiters and HR professionals struggle with Boolean search – the most basic query “language” – why wouldn’t they assume that the average recruiter could not possibly understand and appreciate what’s going on “under the hood” of semantic search solutions?

However, it is folly to apply that stereotype to all sourcing, recruiting and HR professionals – there are plenty of us who actually know more about the specific challenges posed by human capital data and the practical needs and concerns of recruiting organizations than the people who are developing the solutions that we are supposed to intrinsically trust to automatically find the best people available.

I don’t hate – I appreciate semantic search. I simply want these solutions to live up to their hype. Semantic search vendors – listen to your current and potential customers – they just want your product to work better!

I’d like to extend an open invitation to any semantic search/NLP vendor – I will happily evaluate your product and make suggestions for improvements…for free! If you’re very confident in your solution, I’ll also write a review online. If you’d rather not have your product exposed publicly, I can also evaluate products privately. I really do want to accelerate the efficacy of semantic search applications for sourcing and recruiting!

I also want to educate others who may be buying these kinds of solutions so they are more knowledgeable and informed as to the pros and cons, capabilities and limitations of these solutions, and not sold simply on impressive sales pitches, techno-speak and “see how many results?” demonstrations. If you’re a potential customer of semantic search solutions, please be sure to include your best sourcers/recruiters in the evaluation process – if the only people who are evaluating a semantic search solution are HR, management, and procurement professionals who don’t actually search for top talent on a daily basis and won’t be using the proposed solution, you can easily be sold on a product that doesn’t actually work as well as you might think based on the sales presentation.

If you’re looking to buy a new flat screen TV or car, anyone can read reviews online, test drive them and compare them to competing products.  I find it interesting (and telling!) that you can’t do the same thing when it comes to recruiting and HR software.

When you buy a house – you get it inspected by a specialized professional before you buy it so you really know what you’re getting beneath the surface. Before you buy a semantic search solution, you should have it evaluated by a person who specializes in human capital information retrieval (who is also ideally a neutral third party!).

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About Glen Cathey

Glen Cathey is a sourcing and recruiting thought leader with over 16 years of experience working in large staffing agency and global RPO environments (>1,000 recruiters and nearly 100,000 hires annually). Starting out his career as a top producing recruiter, he quickly advanced into senior management roles and now currently serves as the SVP of Strategic Talent Acquisition and Innovation for Kforce, working out of their renowned National Recruiting Center with over 300 recruiters. Often requested to speak on sourcing and recruiting best practices, trends and strategies, Glen has traveled internationally to present at many talent acquisition conferences (5X LinkedIn Talent Connect - U.S. '10, '11, '12, Toronto '12, London '12, 2X Australasian Talent Conference - Sydney & Melbourne '11, '12, 6X SourceCon, 2X TruLondon, 2X HCI) and is regularly requested to present to companies (e.g., PwC, Deloitte, Intel, Booz Allen Hamilton, Citigroup, etc.). This blog is his personal passion and does not represent the views or opinions of anyone other than himself.

  • Tom Furlong

    Your comments regarding semantic search vendors rings true- the vendors realize that many large recruiting departments want the latest technology available and are supplying a solution that meets this need- not the need to properly identify the best available talent in the marketplace. How many times are these systems purchased by HR/CIO executives in part to meet a preset deliverable to “upgrade” the candidate identification process?

    A recent conversation with the recruiting manager from a Fortune 500 company covered how their department was relying on their ATS program as a first step filter- the company would not consider any candidate that had been rejected by this system. He had complete faith in their preliminary selection process and rejected the notion that reliance on only one method of talent screening yielded an incomplete set of candidates. It seems that “good enough” is an acceptable solution for his needs; the pursuit of excellence (in any endeavor) requires using technology as a first step, not the only step, in the process.

  • http://www.booleanblackbelt.com Glen Cathey

    Wow.

    Regarding that F500 company – #1 the executives and hiring managers should be *horrified,* and #2 if the people interested in working for that company knew that is how they are being evaluated and rejected…talk about scary!!!

    I whole heartedly agree – technology is the first step, never the entire step in a process. Computers and applications move and sort information, but people (should!) do the analysis and decision making.

  • Dave

    Nice post Glen. Yes, it seems every time a new technology surfaces everybody thinks they will be out of a job. The Watson Jeopardy episode was indeed an important step for cognitive computing, but a long way from thinking and *reasoning* like a person. Sourcers should be more excited than ever with the explosion of the social web and the emergence of the interest graph. Never before in the history of recruiting has there been a more fertile digital land for collecting and understanding people’s interests and associating those interests with the needs of employers. As the web continues to become more structured and the social web continues to form a fabric for relationships and interests the opportunity to leverage this unprecedented connectedness will only get better and better. There has never been a better time to be in recruiting/sourcing. Exciting times!

  • Dave Copps

    One more thing…what a great time to be writing semantic software for this market. Get ready for some really cool stuff coming down the pipe from us and other vendors.

  • Tom Bolt

    “…unless all they’re doing is basic keyword and title searching.” Artificial intelligence will win only if humans surrender. The wake-up call for shallow sourcers is to master the ability to search like a computer but also bring something to the table that adds a value not available by anyone or anything else.

  • http://twitter.com/Wise_Man_Say Wise Man Say

    Glen, an excellent post, as always.

    The problem isn’t that you are wrong, it’s that other people will not see your argument. And that means – regrettably – that your conclusion that Semantic Search will not lead to job losses in the sourcing discipline – is incorrect. It will do just that and the fall out will be tremendous.

    Semantic Search is a very ‘sell-able’ idea. It will come in a box, demo amazingly and present the sort of ‘turnkey’ solution to sourcing that will quickly become must-have in the recruitment industry. It will be much easier to sell that concept, that it will be to refute it.

    Your only mistake is to assume that the recruitment leaders who will make the decision on semantic search solutions are as smart as you are, or will do the due diligence you did. They aren’t, and they won’t. Principally, they are salespeople, not analysts, and will see SS as a method of reducing costs, reducing risk (of star sourcers leaving their business) and improving productivity (i.e less time sourcing = more time selling). And will they go for it in their droves.

    Is it the end of Sourcing? Of course not. Sourcers will still be around, but in lesser numbers as less skilled ‘Consultants’ (read salespeople) find themselves being able to compete with skilled sourcers in finding candidates. They may still not do it as well. But they will almost certainly sell it better.

    I hope I am wrong. But, as you know, we’ve seen this before.

    Best wishes

    Hung

  • Arron Daniels

    We can only see where this goes. With any new technology comes explosive innovation to tweak, change, push the limits of the product. Sourcers would be the “tip of the spear. However, I agree with @Wise_Man_Say when sourcing (as we know it now) will be in fewer numbers.

    As the article states, there is always that needed human interaction. I was speaking with a former co-worker about sourcing and he equated sourcers to record players. “Great for their time.” I whole heartedly disagree. Sourcing can evolve and change with the industry just as sourcing has evolved from file cabinets and phones to computers and mobile applications.

    Let’s also not forget that this technology costs an arm and a leg. Wel… for that mater a few arms and legs. Like @tombolt said. Don’t fear Watson for now and be prepared to change and evolve. That’s my two cents!

    Respectfully,

    Arron Daniels

  • http://www.booleanblackbelt.com Glen Cathey

    Dave,
    Honored to have you stop by and comment!

    I only wish I had the technical know-how because I’d be tinkering away at machine learning algorithms myself!!!

    I’d be happy to give you a private, 3rd party opinion on anything you’re working on, including signing an NDA…..
    :-)

  • http://www.booleanblackbelt.com Glen Cathey

    Agreed Tom – the value humans add to the equation is the analysis of the results returned by AI matching, as well as the ability to specifically seek out that which semantic search solutions bury amidst results, or exclude altogether!

  • http://www.booleanblackbelt.com Glen Cathey

    I agree with you Hung.

    It may regrettably take too long for companies to realize after buying and implementing semantic search solutions that they do only sort and move information and do not provide any real value otherwise.

    Intelligent and insightful people are required to analyze the results returned by matching algorithms and make decisions.

    They are also required to realize that resumes and social media profiles and updates only offer a fractional picture of the people they represent, and to allow a matching algorithm to be a final/authoritative screen of potential applicants would be gross folly!

  • http://www.booleanblackbelt.com Glen Cathey

    Excellent points Arron.

    Ridiculously expensive data warehousing and business intelligence applications crunch and move massive amounts of data, yet they also require people to make sense of the information provided. Companies are not making critical business decisions based on reports generated by ERP/BI apps – they are making critical business decisions based on the analysis provided by people after they’ve made sense of the information provided by the applications.

    That’s why the applications are called DSS – decision SUPPORT systems.
    :-)

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