Q&A: 5 Minutes with CareerXroads Principal Gerry Crispin
1.What does “big data” mean to you (and should recruiters believe the hype?)
We used to sit around as graduate students and dream up hypotheses we could test “if only” we could access and crunch enough varieties of data from multiple sources. Now we test infinite streams of data until it dreams up a hypothesis. Recruiters should seek the source of the data and the map drawn between the results and any analyst’s conclusions before accepting the ‘hype.’
2. What are some of the most common myths or misconceptions about analytics that recruiting and HR pros need to know?
The process of examining large data sets containing a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information has three basic flaws:
- GI=GO. It is as fundamental a law as gravity. ‘Garbage-in’ will always negate ANYTHING that comes after it.
- Random Correlations are a ‘certainty.’ Finding “hidden” statistical connections in large sets of data is 100 percent certain and is neither a stopping point nor a reason to publish. It is merely the first step in the scientific method requiring a hypothesis BEFORE one tests it out on a fresh set of data to replicate the finding.
- Analysts are Human. Drawing a conclusion from a set of data results and statistical correlations is a discipline. That means people study the science and art of doing it. Most people without the discipline of the discipline draw bad conclusions from good data. The least of which is the degree of causality of the connection.
3. What are some of the greatest opportunities or outcomes employers should focus on in terms of utilizing data or analytics? Any advice for getting started?
Yes, make it personal. Own it. Collection, analysis, and improvements should in the end answer these questions:
What is the business connection to the function you are paid to perform or oversee? Job family by job family?
- e.g. How does your focus on hiring (time, cost, quality) affect business plans or recruiter outcomes and new employee performance?
What are the ‘drivers’ in the supply chain you’ve mapped as a sequence or a parallel series of step by step practices?
- e.g. How do sources, recruiter practices, and selection methods influence decisions of candidates you eventually hire and those you don’t?
What are the unintended consequences of the way you are doing it now?
- e.g. How are recruiting practices contributing to the success of other functions: compensation, development, competitive intelligence, etc.?
4. When you look at using analytics to improve recruiting, who’s getting it right? Any specific examples you can share (personal or otherwise) as an example of someone who personifies a “data-driven recruiter?”
It isn’t a matter of getting it right. It’s a matter of investing in just doing it. Fear of not getting it right will undoubtedly delay the inevitable or cause a leading firm to fall behind its competitors. Employers get it with customers and have been improving their ability to get it right for a decade or more. HR is just starting down this road and needs to catch up vis-a-vis candidates and employees. I don’t know if companies working at it (in HR) like GE, Nike, Bank of America, HP, Amazon, Disney, PepsiCo are getting it right, but I admire the journey they’ve embarked on.
5. In short, why are analytics important? Give us your best elevator pitch.
How we hire should be driven by the evidence. We need it to offer candidates experiences that empower them to make better decisions in parallel with corporate selection practices, and to embrace technology-based networking tools as a primary means to build two-way candidate pipelines.