Recruiting in the Age of Big Data: A Guide for Recruiters
Few understand what “Big Data” means – much less what it hopes to accomplish.
Hiring for Big Data
If you what to know what these terms mean to employers, and how they’re accomplished, a few similarities and differences help point the way.
Big Data is about the massive quantity of data (information), and its potential for:
- targeting and interpreting consumer behavior for advertising and marketing, also called “data-analytics;”
- modeling financial events and expectations, also called “quant-finance or quantitative finance;” and
- making sense of drugs and therapies that work for some but prove toxic or dangerous for others in a healthcare context, also called “bio-informatics.”
Big Data for Recruiters
To illustrate the approach to problem solving and how it works, consider the pile of resumes on your desk, or the mass of applicants in your data-base.
Since hiring someone might be the near term objective, we’ll consider that result to be a perfect score. The whole process is complicated, and job applicants can improve their chances with cover letters, resumes, follow-up, and referrals. Lots of things take place during this process.
Big Data helps determine how instrumental (causal) these things are to the objective or goal. Some things may actually be unhelpful and lower the chance of being hired, and some things may not improve the chances at all, or improve them very little.
In the world of Big Data, all of these events are scored against their ability to improve the chance that an applicant will be hired. The math can be very complicated, but the results are never any better than the information and quality of what goes into the process.
Mathematicians Don’t Make Good Recruiters
Computers, like the math jocks that use them, speak their own language, and that language (math) does a poor job describing or expressing an applicant’s likeability, charisma, temperament, or any of the dozens of things recruiters look for before hiring someone.
One thing for certain is that Big Data does not work, nor even begin, before the problem being described can be translated and represented mathematically. Processes must be identified, outcomes scored, events observed, and their impact or usefulness inferred.
Every field that wants to work with Big Data has its own special problems and advantages. Most of the people doing the heavy lifting have serious math skills, but someone also has to be able to translate the information.
Demand for Big Data and Employees is Growing
There’s a long history stretching back thousands of years for modeling events – perhaps beginning with observations of the moon – and more recently its more formal training which began with theoretical physics.
Employers have their own examples to explain how we extract knowledge from information, and the massive and growing amounts of data available to each field (data set).
There are challenges to each field, and companies such as Amazon.com, Google and Facebook have become expert at meeting them. Thousands of people are engaged in helping predict online behavior, so that the millions of people using the service get suggestions and recommendations they find helpful, information they find relevant and useful, and software that helps anticipate what we want.
New Tools Are Helping Fuel Hiring Demand
New tools such as Hadoop and MapReduce can help address the bottleneck and challenges of working with such massive amounts of information. Just as the typewriter and later the personal computer put a printing press in the hands of every would-be author, the potential to use Big Data is becoming more democratic and widespread.
Companies require this expertise to do an expanding list of things well, including CRM, social media, data-analytics and user recommendations. These companies need more people who can do the work, and they need recruiters who can find and assess potential hires.
Training and Hiring the Right People (Hollywood calls this “the Talent”)
There is a far longer explanation for each of the marketing, finance and healthcare fields, and their history using Big Data, but consider the example of James Simons and Renaissance Technologies – one of the world’s most successful hedge funds which helped make Dr. Simons worth more than ten Billion dollars. Jim Simons was not a finance guy. He was and remains an accomplished mathematician, scientist, and now also philanthropist.
Simons was early to understand the approach, and the potential to use Big Data for finance. Most people attribute his success to a science (vs. finance) based approach, a belief underscored by his firm’s hiring people with math training from MIT and Princeton. In the case of Renaissance, financial types need not apply, and this bodes well for everyone else – the people who can help find and measure the right information – and the math jocks who can help crunch it.
The shared talents of the people trained to do this work, and quality of how they approach answering “what will happen next” – i.e. generating predictive models – holds the potential for marketing programs that succeed, financial systems that won’t collapse, and safer and more effective healthcare.
As for recruiting – that will need to be done by those with the training.