6 Things Recruiters Need to Know About Big Data
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Today’s Question: It’s become fashionable to talk about big data, but many recruiters and HR pros still feel uninformed about what, exactly, “big data” means and why they should care about it. What tips and insights do you have for these recruiters and HR pros looking for a little assistance?
1. Case Studies Are Your Best Friend
Over the last decade, as more of us have started using technologies like mobile, social networking, etc., we have been generating a tremendous amount of data that is unprecedented. For example, 15 years back, the only customer information Walmart might have is someone’s name and phone number. Today, Walmart can store customers’ names, email addresses, phone numbers, cell phone numbers, Facebook profiles, purchase histories, etc. Multiply that data by 50 million customers, and that right there is your big data problem.
The best way for recruiters to learn about big data is to read case studies like the following:
— Binny Mathews, DeZyre
2. Big Data Can Make Your Life Easier – and It May Even Replace You
Combine thousands of data points and hundreds of millions of career profiles, and the power of big data is manifest. Big data and smart algorithms will help recruiters save thousands of man hours normally sunk in sourcing candidates and increase the success rate of placement for even the most difficult requisitions, like CTO or head of data science.
Imagine finding hundreds of the most qualified candidates for any job requirement in a matter of minutes and getting in touch with them with the most relevant job recommendations, as well as the ability to filter out unqualified candidates automatically. You don’t have to read resumes and cover letters when the skills or culture match is not even there. All you have to do is deliver candidates that are a great fit to the job.
Now why should you care? Research suggests that telemarketing jobs have a 99 percent chance of being fully automated in the near future – and traditional recruiting can be very similar to telemarketing.
— Ninh Tran, HireTeamMate
3. It’s Not Always Complex
Basically, big data is large-volume data brought about by the ability to interact with and capture huge amounts of data through Web platforms (i.e., websites) and technology (e.g., CRMs).
We have the technology to use that data to make predictions and qualify assumptions that lead to better decisions – say, the ability to take all the tech-related jobs and skills listed on a site like Indeed.com, cross it with graduation levels and average pay, and then predict which degree you should take this year based on the future need for that industry and the skill sets that will be needed when you graduate in a few years.
Don’t panic: Big Data isn’t necessarily complex data – it’s about using technology and different ways of thinking to simplify large amounts of data into insights that give you a competitive advantage.
– Domenic Brooks, Datalabs
4. Big Data Doesn’t Remove the ‘Human’ Element of HR
No one working in finance or marketing would ever suggest a solution to a problem without the relevant big data in front of them, so why is this not the case with HR, where trust and relationships are still the main currency? With big data, companies can approach recruitment and other “people” decisions with the same rigor they give to engineering decisions, taking intuition out of the equation.
In order to find the perfect candidates, recruiting has become less about process and more about marketing. Google, for example, takes a data-based approach to recruitment, known as people analytics. A collective score from a series of interviews gets fed into an algorithm, which is used to predict that employee’s suitability for the job. It is proving to be highly successful, due to the fact it relies much less on human intuition – something that is naturally prone to bias.
Big data is opening up a wealth of new possibilities for HR. Annual performance reviews should be replaced by ongoing talent management based on performance data, while employee engagement surveys are helping managers understand how to improve employee motivation and productivity. Rather than taking the “human” element out of HR, data is actually making humans much better at, and more suited to, their jobs.
— David Godden, Thymometrics
5. You Have to Measure Outcomes, Too!
Well-known author Bernard Marr offers a very pragmatic definition of big data. Everything we do is increasingly leaving a digital trace (or data), which we (and others) can use and analyze. Big data therefore refers to that data being collected and our ability to make use of it.
The big data that’s relevant for HR and recruiting can include social media data from Facebook, Twitter, and, of course, LinkedIn. The trick is to bring all this data together and to identify the prospective candidate across all of these disparate sources. The quality of the data and the references to sources require a modern data management platform that can handle both the volume and variety, but also the compliance related to the personal data that is gathered. Once a reliable data foundation is established, analytics can go to work to find the patterns and matches to score, rank, and narrow the candidate pool to open positions, using a combination of machine learning and human professionals to curate and assess the relevance of the information. Finally, the success of the recruitment process and the productivity of the candidates hired must be correlated back to the analysis for accurate measurement of results.
This closed loop is often overlooked in the enthusiasm of leveraging big data. You can’t improve on your process or criteria if you can’t measure the actions that result accurately. Like all industries and fields, HR and recruiting will continue to see big data become an integral part of an improving discipline, but all professionals need to have confidence that the data they are looking at, regardless of size, is reliable.
— Ramon Chen, Reltio
6. The Name’s a Bit of a Misnomer
“Big data” is a bit of a misnomer. Essentially, “big data” is best called “any data.”
Usually, the goal of creating a “big data” infrastructure is to first aggregate any and all relevant data within a company or organization and then apply analytics to said data in an attempt to gather new and unique information or metrics. For example, if a company has a CRM database and has been collecting names, phone numbers, and addresses, a basic query could be how many addresses fall within a certain zip code. A more complex one, given just the criteria mentioned, would be to understand how many males are within a five-block radius of the business – or how many phone numbers have area codes that do not match the location of the address entered.
Each of these examples is not something normally searched for, but a company may want to understand something deeper about its data. Perhaps men that live near the company drive a lot of business by the numbers, and therefore aggressively marketing locally makes better sense. Maybe those phone number area codes from different locations indicate that their customers move around a lot – or that they have a lot of fraud to deal with.
Big data is anything you want to make it, really – and it can potentially give you better information about your company, customers, employees, vendors, partners, and anything else that is relevant to your bottom line if you use it the right way.
— Nick Espinosa, BSSi2
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