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Often HR teams can have a reasonable number of projects or initiatives going on at any one point in time, as the HR manager or HR Business Partner it can be challenging to keep track on all these. Indeed what most managers and business partners want is ‘progress on a page’. And the following YouTube clip I put together will show how you can easily do this with excel. This tutorial came out of some work I did to support a large Employment Relations team, who were tracking all incoming requests, what they wanted to do was provide their Director with an on the page quick reference to how they were progressing.

Also if you haven’t seen my Lookups tutorial – which is my most popular tutorial, take a look at the below clip also. What Lookups allow us to do, is to take two lists and merge them together. In HR we do this a lot, for example I’ve used this in remuneration reviews where I’ve taken a current list of employees and their remuneration levels, and then merged information from the previous remuneration review. In that case I wanted to be able to show managers the employees current information (salary, position etc), but also add in their performance rating from the previous remuneration review.

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.