Article: Using digital footprints of previous hires to manage future workforce

HR Analytics

Using digital footprints of previous hires to manage future workforce

Digital footprints of previous hires can be used to make better decisions on future hiring, engagement, career growth, and retention
Using digital footprints of previous hires to manage future workforce

The HR data model is like a company’s DNA – it is unique to a company, but might have similarities with companies in the same region and/or industry


The future of HR is really in the ability to understand what type of people to hire, how to ensure high engagement, how to retain people, and what kind of people succeed in a particular company industry or role. While this might seem like its more of an obscure art, my belief is that data analytics is the key to all of this. 

Organisations have tonnes of data on their employees – for every employee that is hired, there is a possibility that 20 are rejected – that’s data.  Out of the people that companies hire, some stay and some leave in a very short period of time.  Of the ones who stay, some have tremendous career growth and some don’t. If you look at the underlying reasons for this, it would be quite hard for any individual to assess what makes a person stick around and succeed in a company. A data model can, however, be built to give us deep insights into such success factors.  The HR data model is like your company’s DNA – it is unique to your company, but might have similarities with companies in the same region and/or industry.

The goal for us is to figure out how data can help in recruitment, engagement, retention, as well as career growth within an organisation.

Recruitment: Most people we hire, especially in skilled roles, have a significantly large digital footprint. We should be able to use that data and map it to the company’s DNA to figure out if the person is a good fit.  One can easily weed out people who have a low fitment score based on the company’s/industry data model. Not to say that homogeneity is a good thing, but you can definitely use data analytics to determine who definitely wouldn’t fit in.

Engagement and Retention: Imagine if as an HR manager, one could get triggers for people potentially on the look-out for opportunities on a real-time basis; perhaps an intervention can be made to ensure that engagement is increased with those who are more likely to leave. Data can even give us ideas about who might be unsatisfied in their jobs – based on appraisal metrics, home town, background, and so on.  All these can be triggers to help us understand our employees better.

To start doing this in a meaningful way, HR personnel would have to have structured access to data not only within the company but also between companies in the industry. There also needs to be a seamless connect between the ATS, HRMS, and training and development platforms. More access to data ensures better internal data models. At IDfy, we are looking at how people data can help in making decisions about people. Our software/platform looks at data as the way to help HR make decisions and our data models make personal identifiers anonymous, but use the core underlying information to drive decisions, be it recruitment, engagement, retention or even career growth. And we can aggregate the data across industries or companies, thus giving much more meaningful insights into people, leading to better people decisions. 

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Topics: HR Analytics, Employee Engagement

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