HR data management in 2021

HR talent management in data-driven world

At the start of 2020, organisations were either thinking about, embarking upon, or in the midst of, their business transformation. Sometime soon after March 2020, regardless 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 organisations generally now fall into one of three categories; survival mode, business continuity mode or growth mode and are now preoccupied 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, digitisation, skills for the future and new ways of working/living.

In response to these changes, there has been an emergence of technologies enabling organisations 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
Whilst fully remote work isn’t the way of the future, a hybrid workforce is. Which means that 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 which helps once the teams are set up, however the first step is to be able to understand the workforce capability 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 organisations to plan real estate, office set ups, organisational structure and hiring strategies.

HR plays a pivotal role in the digital transformation of an organisation. Digitisation 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.

Digitisation 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 is high on the agenda to keep both relevant, and to retain good talent. Organisations 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.

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 normalisation 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 mobilise 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.

Traditional data management however, has fast been made redundant, for five key salient reasons:

1. Bias
The persistent problem is that it has been incredibly hard for organisations to trust whether the data that goes in 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 organisation has traditionally hired men – the algorithm learnt to became 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 organisation. So, a workforce of 5000 might only have ‘current’ knowledge into 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. Organisations 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, 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 ever 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 to ask for proof in the data and privacy of any cloud-based technology.

What now?

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

Workforce Intelligence is more than just mobilising 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 the world’s first Workforce Intelligence Platform, with independently audited talent ethical AI It enhances existing talent data, from multiple systems and sources and enriches 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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