Hey Recruiters! Youre Sitting on a Mountain of Big Data #BigDataHR - Workology

Hey Recruiters! You’re Sitting on a Mountain of Big Data #BigDataHR - Workology The ATS is Your Big Data Warehouse This week on Blogging4Jobs we are focusing on the theme Big Data sponsored by  Jibe. Jibe  provides cloud-based recruiting technology solutions that enable talent acquisition teams to strategically identify, attract and engage candidates. Join us April 10th 2014 at 3pm to talk Big Data on Twitter using the hashtag #BigDataHR and  join our webinar, Whats the Big Deal with Big Data in HR Recruiting on April 17th at 11a EST. Follow the week by  bookmarking us!   It’s Big Data week here on Blogging4Jobs and that means you’re in for a real treat.   Before we dive too deep, let’s first define what it is that we’re talking about. This definition from Wikipedia seems to sum up “big data” rather nicely: Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization. Is it just me or does that remind anyone else of applicant tracking systems? The ATS is Your Big Data Warehouse Let’s assume that you represent an organization that has an ATS. How many years have you been collecting applicant information? How many data points (i.e. location, years of experience, educational level, etc.) have been captured within your ATS? Now, how much insight do you have into this data? Recruiting and the Influx of Big Data Over the past 10 â€" 15 years, HR and recruiting have acquired phenomenal amounts of applicant and new hire data within applicant tracking systems. In more recent years, some companies have begun collecting even more data within their talent communities. .ai-rotate {position: relative;} .ai-rotate-hidden {visibility: hidden;} .ai-rotate-hidden-2 {position: absolute; top: 0; left: 0; width: 100%; height: 100%;} .ai-list-data, .ai-ip-data, .ai-fallback, .ai-list-block {visibility: hidden; position: absolute; width: 50%; height: 1px; z-index: -9999;} In many cases, we’re literally sitting on top of mountains of data. But what are we doing with it? Is it easily extracted and understood?  A lack of insight into existing data often keeps recruiters in reactive mode. A new job requisition opens. Recruiter posts job. More new applications come in. What if the perfect candidate was already in your ATS or talent community? Let’s take it a step further. What if the perfect candidate was already employed by your company? Using Big Data for Internal Mobility Recently, I attended a luncheon hosted by an HR tech provider and what I saw was some very cool technology. Essentially, the company’s product could easily take your existing candidate and/or new hire data and parse it out by location, skills and a whole host of semantic search terms. Data could then be easily viewed, downloaded or visualized via a geographic heat map. Some studies state that it’s four times more expensive to hire externally than to hire (fill / promote) from within. And, recent surveys say that nearly three out of four employees would consider leaving their current employers. So, what’s the connection? .ai-rotate {position: relative;} .ai-rotate-hidden {visibility: hidden;} .ai-rotate-hidden-2 {position: absolute; top: 0; left: 0; width: 100%; height: 100%;} .ai-list-data, .ai-ip-data, .ai-fallback, .ai-list-block {visibility: hidden; position: absolute; width: 50%; height: 1px; z-index: -9999;} During this vendor luncheon, the presenter ran a few searches using an existing client’s account. We could see in which locations this particular client had employees who fit their open reqs. Recruiters could then identify employees and share internal job postings with them for consideration. Understanding and using employee data could reduce cost per hire, time to hire, time to on-board, and possibly increase employee engagement and retention. I recognize that not all corporate cultures would be OK with this sort of model due to hiring managers’ perceptions of cherry-picking. Nonetheless, I thought it was an excellent example of how some organizations are using technology to better understand, and more importantly, better utilize their big data. Have a Case Study to Share? Is your company currently using similar technology? Are you using Big Data to help promote internal mobility? I’m sure we’d all love to hear more real world examples. Please comment below.