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HR data management in 2021

At the start of 2020, organizations were either thinking about, embarking upon, or in the midst of, their business transformation. But sometime soon after March 2020, regardless of what stage of their journey they were on, they had to pivot and either pause or accelerate their strategy.

Fast forward to the start of 2021 and organizations generally now fall into one of three categories; survival mode, business continuity mode, or growth mode and are now preoccupied with establishing what the short-to-medium-term future looks like. The only aspects of the workplace future that are semi-certain now are the global macro changes such as distributed work, digitization, skills for the future, and new ways of working/living.

In response to these changes, there has been an emergence of technologies enabling organizations to plan, deploy, measure and pivot their talent strategies. HR and people leaders need to keep up — and ahead — with a deluge of potential data and it wouldn’t be fair to assume that knowledge about this area is equal.

Talent management has become a revolving door and tech platforms that can enable success should be rigorously proven to do good for both business and people.

Why is talent data so important?

Distributed Work

Talent leaders need insight into who can do what, from where, and when, in order to start uncovering the DNA of their workforce. There are tools for measuring productivity, engagement, and output that helps once the teams are set up, however, the first step is to be able to understand the capabilities inside your workforce to build a successful hybrid framework.

Identifying what skills already exist in the workforce and which skill sets need to be in the office, partly in the office, or can be fully remote, will allow organizations to plan real estate, office setups, organizational structure, and hiring strategies.



HR plays a pivotal role in the digital transformation of an organization. Digitization is most predominant in customer-centric industries to keep up with the ‘anything, anywhere’ expectation of frictionless transactions, whether that’s consumer purchasing, service or engagement.

Digitization requires agility, transparency, and an evolved mind and skillset to be successful in this new environment. And the skills required to build the systems are new, emerging, and ever-evolving.


Future Skills

This transformation has led to the ‘skills for the future’ focus amidst much awareness of both employees and employers alike. 46% of employees surveyed in ANZ mid-2020 said that they were self-funding their upskilling, whilst over 50% said that they believed it was the responsibility of their employer.

Either way, upskilling and reskilling are high on the agenda to keep both relevant, and retain good talent. Organizations that have a handle on current, in-demand, and gaps in their skills ecosystem are able to plan the future with precision, and be ready to pivot.


The new way of work

Whilst it’s been a refreshing change to see the evolution in the way people work; the rise of portfolio professionals and the normalization of gig and knowledge workers, for talent managers that has meant rethinking their tactics. Intelligent insight into the skills and capabilities of a workforce means the ability to manage and mobilize entire teams becomes quickly efficient and cost-effective for the business and valuable for employees.


All data is not created equal

Unfortunately, there can be an element of licking a finger and sticking it up in the air when it comes to deep talent insight. The world is moving faster than ever and trying to predict or respond to priority talent requirements has been near on impossible. To be able to implement new strategies, there needs to be a comprehensive amount of data.

5 reasons traditional data management has been made redundant


1. Bias

The persistent problem is that it has been incredibly hard for organizations to trust whether the data that goes into their systems, comes out in an ethical, explainable, and trustworthy manner.

AI is not inherently biased; it fully depends on how the algorithms have been instructed to behave. Take the example of training an algorithm based on ten years of hiring data when an organization has traditionally hired men — the algorithm learned to become biased against female applicants. Any reference to the word ‘women’, as in ‘Women at Work Group’ would cause the algorithm to rank the employee or candidate lower.

When choosing technology to boost HR data, it’s imperative to ask ‘why’ and ‘how’ the technology protects against any historical bias.


2. Incomplete Data

Reejig data shows that on average, less than 29% of employees complete their skills profiles when they join a new organization. So, a workforce of 5000 might only have ‘current’ knowledge of around 1450 of their employee’s skills and capabilities.

Of those 1450 employees, each profile is likely to only be around 50% complete, as employees tend to think about the skills relevant to the role they were hired for, rather than all the skills they’ve accumulated throughout their career. So, from 5000 employees, there may be actionable insights into around 15% of the total workforce.


3. Outdated

Even if the data may actually have been good when it was generated, it’s unlikely to have been updated and is now irrelevant. To enable leaders to make successful business decisions about the workforce, data needs to be continually refreshed. Organizations function daily in continuity mode — business as usual — but to move into growth mode they need to operate in the future with ethical, predictive AI.


4. Single source of truth

HRIS, CMS, ATS, ERP, spreadsheets — the list is endless. Not only do talent managers and workforce strategists have to ensure that the data in those systems is complete and up-to-date, but they have to also hope it doesn’t lose integrity when it’s aggregated – traditionally an arduous, manual task.

Augmentation takes a complex task that is absolutely inefficient for humans to manage. Humans however need to influence the algorithm decision-making, and that is where you need to have trust in the people building the technology.


5. Trust

Trust should underpin every single decision that technology makes about people. Trust that the data is correct, in the humans who designed the algorithms, in the people using the outcome of the data, and trust that the technology is doing the right thing with the data it manages.

It is absolutely ok not to trust blindly, but to ask for reassurance, to implement training and education, and ask for proof of the data and privacy of any cloud-based technology.

What now?

There has been a rise in HR data analyst roles as organizations realize the importance of having people who understand this space and the right questions to ask. Or at the very least, partners they can trust their people data with.

Workforce Intelligence is more than just mobilizing talent, it’s about understanding trends, insights into competitor business strategies, supply and demand, learning, and much more.

Reejig solves all of these difficulties with our award-winning workforce intelligence platform and the world's first independently audited talent Ethical Talent AI. We enhance existing talent data, from multiple systems and sources and enrich it with current skills and capabilities from publicly available data.

We’d love you to have the opportunity to talk to one of our workforce strategists to understand what you can do to make ethical, explainable, and secure sense of your talent data.

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