IQ Navigator Launches IQ Labs – Using Big Data to Drive Hiring Insights

IQ Navigator (IQN) is now the largest independent VMS provider - their software helps organisations manage their contingent (interim) labour and related spend areas. Their big rival, Fieldglass, was swallowed up by SAP recently.

Recently, we had a session with the team from the IQN Labs, including Anne Zelenka, VP of Data Science and pictured here, who are looking at how they can use the wealth of data that sits in VMS systems to provide interesting output and intelligence to IQN customers and users. This is really a tangible example of the much discussed “big data” – taking the huge amount of data around jobs, assignments, people, fees, and so on that sits within VMS systems and turning it into something that can help organisations make better decisions around staff (contingent and permanent labour), jobs and hiring issues.

Zelenka described how the team is taking “almost a venture capital” approach, developing a range of projects with clients and accepting that some will provide a good return, whilst others will fall by the wayside. The projects are carried out on a proof of concept basis, looking to “productize” once the early results suggests there is real value there. “We are evolving reporting and basic business intelligence into predictive analysis and suggested actions”, she explains.

In fact, the input to the analysis can even go beyond what is in the IQN system. It can also include “human” input such as judgements from users, external data (e.g. from LinkedIn) or from non-VMS transactional systems. “Having collected data, we can explore patterns, run experiments, train predictive models and generate suggestions” explains Zelenka.

This all leads – in theory at least - to improved business intelligence and process. And ultimately, this is all aimed at helping organisations acquire and manage talent better, whether that is internal staff or external contingent labour. The new service doesn’t replace standard IQN customer reporting of course, but looks to provide and embed “predictive analytics” to help customers.

Now that all sounds rather pretentious, but when we get into practical examples, we’re talking about in some cases very down to earth and pragmatic issues. For example, when you have a situation where multiple recruitment agencies are submitting candidates for a temporary role, should you limit the number of CVs / candidates each firm can submit? And to what number?

The team has looked at this, and run analysis to see how you can get better submissions more quickly. The results were actually counter-intuitive. Changing the number of submissions allowed per agency from 3 to 2 actually gets you more submissions more quickly! We can hypothesize as to why that might be, but the key point is that analysing large amounts of data gives you some useful intelligence that would not have been arrived at simply by instinct.

That might seem a relatively minor insight, but if getting these details right means you have access to the best candidates before your competitors, there is a real advantage here. Taking that further, if IQN can build a predictive model for submissions, you can manage the rules dynamically over time and depending on the role or circumstances.

Zelenka also explains that the evidence suggests “machine intelligence plus human input beats either alone”. So we’re not looking at a totally automated recruitment world here, rather how to use data and machine intelligence to support human judgements and decisions. However, there are some interesting implications if for instance we can automate some short-list selection decisions. That might help to overcome human bias, which has been proven in many experiments (whether that is based on racial, sexual, or simply the “choosing people who seem like me” factors). More automation could also help enforce “vendor neutral” contingent labour situations, helping to make sure that there isn’t bias within the choice of agency.

My personal feeling is that this will really take off if you can start linking performance information to the more objective factual data within the various systems. How does the performance of interim staff vary and is that linked to which agency they come through, their employment history, the length of their assignment or other factors? We’ve probably all had experience of working with interim staff who were brilliant; and with others who weren’t worth a fraction of their day rate. So you can imagine some really powerful analysis that might give a strong steer into how you best choose, structure and manage your contingent workforce – and maybe even take that into the permanent staff world!

Anyway, there is more information about the IQ Navigator initiative here, and we will keep an interested eye on their progress with the work.

 

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