The market thinks:
All the big players are using data, so HR teams should too.
Thomas knows:
Dealing with data can potentially harm your business.
Over the last few years, many big-name businesses have harnessed the power of artificial intelligence (AI) and machine learning (ML) to do new and exciting things. Now, with data-led technology – including HR tools – becoming more mainstream, many smaller organisations are following suit.
What do we mean by data?
Data is the information you gain through recordings or observation. You can then analyse this data, and convert it into insights (facts, stats, trends and so on). You can then use these insights to inform actions. AI can be used to mimic or enhance human input. And ML refines AI predictions and behaviour over time.
In an HR context, you might collect data around:
- Applications.
- Interviews.
- Performance.
- Employee satisfaction.
- Productivity.
And you might use data-driven tools to do things like:
- Sort through applications.
- Find the best candidates.
- Keep your people productive.
So, using data to do things better is becoming the norm. But it hasn’t been all plain sailing.
A cautionary tale
If you want your organisation to become more diverse, you might turn to AI in a bid to eliminate all those human biases we talked about before. However, AI cannot always be trusted. Sometimes, it can ingrain biases deeper within your business.
Research has shown that some AI models, despite people’s best intentions, can lead to discriminatory outcomes.
But why? Well, if your track record in a certain area has been biased, the resulting data model will be too. Use it, and you’ll only be reinforcing the same behaviours and performance in your organisation.
The good news is, research has also shown that new ways of testing AI could help scientists build fairer, more effective algorithms.
A little or a lot?
If you’re a mid-sized organisation, it’s likely that you’ll have access to some data, but not very much. If this is the case, your biggest challenges will be maximising the value from the data you do have, and understanding what data you should aim to capture next.
If you’re a large organisation, your biggest challenge will be knowing how to use all the data you’re capturing to achieve your goals. But this can be easier said than done, because AI and ML models are only as good as the data that informs them.
How can I make sure my data helps, rather than hinders?
Whether you’ve got a little bit of data or reams and reams of it, maintaining objectivity is key.
First you need to understand what you want to measure, what outcomes you want to achieve and what behaviours/performance you want to reinforce.
Then, you can vow to capture accurate, unbiased recruitment and performance data from here on out. For this, you need an objective foundation which can capture objective data – and help you make objective decisions. Enter Thomas.
Top takeaways
- Companies are turning to data to do better. Which is great. However, the problem arises when that data is flawed, or doesn’t support what the business wants to achieve.
- Our assessments can give you strong, science-backed data points from which you can strengthen your people processes.
New to psychometric assessments? Get the lowdown here.
