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Predictive Analytics In Human Resources: SPSS Modeler

Brendan Lys
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Brendan Lys
Brendan Lys

Operating at the intersection of Human Resources and Data Science, I leverage extensive specialist experience within Human Resources, with the methodologies and approaches of Data Science. This focus on the discovery of actionable insights from data, has been applied to areas such as: remuneration & benefits, workforce planning, recruitment, health & safety, diversity, and training. But what does the application of data science to HR challenges and opportunities actually look like. Within an HR framework the data we work with typically comes directly from our HRMIS, an advantage of using data science methodologies is that we can bring in additional data either held within the organization or from external sources - data which is out of reach from a pure HR analytics approach. Consider for example position descriptions, these contain a wealth of data that we typically ignore as its not in a analysis ready format. A side project I'm working on currently (April 2019) is using text mining on job descriptions to provide insights into which job family the position may fit into. The insights of my work have been enjoyed by organizations across a diversity of sectors including: Government (Australia and New Zealand), ASX and NZX listed companies, utilities, not for profit and higher education.

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Recently I’ve been researching and looking into a tool from IBM called SPSS Modeler, many readers may have heard or even used of SPSS Statistics, and Modeler is part of that family. What I’ve seen so far has been very encouraging. Essentially what SPSS Modeler does is to mine through your data, and find risk […]

Predictive Analytics In Human Resources: SPSS Modeler

Recently I’ve been researching and looking into a tool from IBM called SPSS Modeler, many readers may have heard or even used of SPSS Statistics, and Modeler is part of that family. What I’ve seen so far has been very encouraging. Essentially what SPSS Modeler does is to mine through your data, and find risk factors or predictors of an event. Let’s say for example that you have turnover data, with this data you can feed it into Modeler and it will find commonalities within the data, taking this information you can then apply this model to current data and gain a list of those current employees who may be at risk or exiting your business.

Currently, I’m awaiting the IT team to install the software, so I haven’t had any hands-on time with this tool yet. The following video (not mine) does give a really good introduction to this software, and I recommend you take the time to have a look at it.

I’ll be hoping to share my experiences with the software as it becomes available on my work PC. I think like many other data-focused HR Practitioners, I’m wanting to offer my employer more than just reports that tell them what happened, I want to provide insights into what may happen in the future.

Brendan Lys
By Brendan Lys

Operating at the intersection of Human Resources and Data Science, I leverage extensive specialist experience within Human Resources, with the methodologies and approaches of Data Science. This focus on the discovery of actionable insights from data, has been applied to areas such as: remuneration & benefits, workforce planning, recruitment, health & safety, diversity, and training. But what does the application of data science to HR challenges and opportunities actually look like. Within an HR framework the data we work with typically comes directly from our HRMIS, an advantage of using data science methodologies is that we can bring in additional data either held within the organization or from external sources - data which is out of reach from a pure HR analytics approach. Consider for example position descriptions, these contain a wealth of data that we typically ignore as its not in a analysis ready format. A side project I'm working on currently (April 2019) is using text mining on job descriptions to provide insights into which job family the position may fit into. The insights of my work have been enjoyed by organizations across a diversity of sectors including: Government (Australia and New Zealand), ASX and NZX listed companies, utilities, not for profit and higher education.

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