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Finding In-House Talent In The Digital Age using Big Data - the shape of things to come in HR


http://www.forbes.com/sites/netapp/2014/04/29/big-data-in-hr/

NetAppVoice: Big Data in HR: Finding In-House Talent In The Digital Age - Forbes

Enterprises hire a lot of people, but in a world where change happens fast and often, they can’t anticipate every need.

One solution is to hire contractors for new or temporary projects. But that involves recruiters finding people—but they won’t already know the company’s systems and culture.

A better way is to find someone in-house, but in a company with hundreds of employees, that can be difficult—unless you let big data do the heavy lifting.

Read that: http://www.forbes.com/sites/netapp/2014/04/29/big-data-in-hr/

Then read this: http://www.forbes.com/sites/netapp/2013/05/29/recruitment-big-data/ 

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I would think if anyone could do this, it would be Google, Facebook, or LinkedIn.

Yet I have never heard of any of those companies doing this. 

Couple of points here... 

Let's consider Facebook and LinkedIn - the information that those companies have is largely fictitious. I could create a completely false persona in both of those systems or a real persona with false credentials - perhaps saying I went to certain schools or worked at certain organizations. To accurately carry any analysis out, we do need better authentication on this data. 

I use the example of LinkedIn a lot when I do 'data mindset' training. LinkedIn, to you, me and the recruiter looks like a bag of resumes. To a recruiter who is paying LinkedIn, this is organized with metadata so resumes can be filtered. However, to LinkedIn, who can aggregate the data and look at it any way they want, they can see other stuff. 

1. They see different bags of resumes. Each bag can represent 'school alumni for a particular course and year', people who worked for... also worked for... , they can also see the current employees of each company.

2. They see fast movers and other profile characteristics. Let's say they are looking for the 'strongest candidates', they'd look for the best course at the right school for the job role. They can see people who moved up the ranks fastest by correlating job title with year. They can see people who know other people who are strong candidates. That's just three simple measures, got to believe that their smart data scientists are finding other markers of 'strongest candidate'

3. The final one is where it gets really powerful: they can see people moving between companies. Now adding the data from 1 and 2 together, they can see which companies are growing with the smartest people. 6 months down the line, a company that is successfully hiring a smart team of machine learning engineers - what might their potential be? 

So if the data is accurate and you have the right mindset to collecting and leveraging data, all of this is very easily possible. 

At Mo-Data we are working on some of these hiring problems, and the potential is huge. 

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