Friday, October 21, 2016

Hiring Analytics

Recently my wife asked me about hiring analytics. Though I am not into HR analytics, I had read enough of IBM Watson Analytics and Hadoop, Spark use cases and in my MS course in Texas A&M, I had studied Neural Networks - and I had wide range of data to talk about Hiring Analytics.

So, I could wing it off in a discussion and I decided to write what I said in this blog ( My Wife's suggestion to blog this)

Why use Analytics for Hiring?

In today's fast paced economy, companies tend to hire people with relevant skills from outside. For example, a bank is willingto hire a business analyst from a manufacturing sector - rather than train a banker in analytics.

In short, hiring is critical to build capabilities quickly. Therefore it becomes important to hire employees who can meet its requirements and fit into its corporate culture. There in lies the challenge: "How to hire someone from outside - who has relevant knowledge needed in banking and who will fit in with the existing corporate culture."

This challenge can be solved by using data analytics during the selection process.

Today, every individual creates tonnes of digital data and also leave a wide digital trail behind. By looking at this digital data trail, and other digital data, one can develop fairly sophisticated analytics tools for hiring.

The two most popular tools in Hiring Analytics are:

  1. LinkedIn Talent Solutions 
    Ebook on using LinkedIn Talent Solution can be seen here:
  2. IBM Watson for Hiring 

The benefits of hiring right candidates are well know: Will more likely perform better and stay longer.

What goes into this Analytics

Hiring Analytics works best when we use standard verifiable Biometric Data - things that can be verified:

  • How many jobs the person had till date?
  • How long they stayed in those jobs?
  • How many promotions they had? 
  • Level of Education?
  • Photos of the candidate on Internet.
  • Industry relevant skills?
  • Public records.
  • Frequency of continuous learning and development.
  • Affiliations to various organizations - cultural, political etc.
  • Cultural Background

In addition to basic resume, Data from Social media: Facebook, Twitter, and LinkedIn are used for this analysis. The Business insights from this analysis can provide insights on candidate sentiment on various organizational factors such as productivity, business growth, or other objectives.  

For a particular role, say for example a system architect, a company can then set a basic set of requirements which defines the basic talent pool. Once this requirements are collected, automatic tools from Monster, LinkedIn etc. can be used to scour through millions of profiles and each matching profile, all the relevant data points can be collected and modeled or numerically analyzed with various analytics tools - Sentiment Analysis using MapReduce, Skill level analysis using Recursive Neural Networks, Cultural fitment analysis using RNTN etc.

The results of this analysis can then be used to short-list a set of potential candidates from a large pool of potential candidates.

During the interview process, further information regarding the candidate can be captured using a basic survey or psychometric tests or other testing tools. Data from these tools can then used to identify & rank candidate based on company specific parameters such as:

  • Willingness to Join
  • Time to Join
  • Onboarding process
  • Skills & training development needs,
  • Retention schemes 
  • Cost to Company, (level of Salary expected by candidate)

The main objective of hiring analytics is to automate hiring process as much as possible and provide hiring managers with necessary information about the candidate - so that they make the right hiring decision.

Apart from actual hiring, Hiring analytics can also be used on existing staff or new staff - to know the retention ratios and plan for future hiring as well.

Closing Thoughts

Information Technology is rapidly transforming hiring process. Industry had come a long way from the days of placing recruitment advertisements in newspapers. Today companies can rapidly analyze millions of profiles and short list potential candidates - not just based on key word search, but based on candidates digital data trails - which gives a bigger picture than just the standard resume.

Hiring analytics is still in its infancy and company is still taking small baby steps. The power of analytics is enormous. Technical advances in data-driven analytics is being used for hiring. Companies are adopting predictive analytics to make hiring decisions and HR strategies. Data analytics can be used for other HR functions such as attrition risk management, employee sentiment analysis, employee skill training plan development, etc.

Someday in future, the analytics tools will themselves find & hire the right candidate - with no human involvement.

Recall the movie "Gattaca" -where companies use DNA analysis to hire candidates!
While the technology for DNA analysis exists today - it is not used in standard hiring, however, I guess agencies like NASA, NSA, CIA may be using it today but in secret!

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