Why is data science considered as a lynchpin for HR's Success?

By arvind |Email | Sep 18, 2018 | 4395 Views

For the longest of time HR professionals have felt that they cannot advance to the CEO or other senior leaders, because the image of their work among them would prevent them from getting there. Almost 47 percent of the HR professionals think they are not able to speak in the voice of the boardroom.
Data analytics is reducing the gap between the HR and the teams, acting as a tool that provides insight to them into the costs and success rate of everything from individual recruitment ads to engagement initiatives or training programmes.
While the HR department is not able to take the nice and easy approach to data analytics like the finance and other departments in a company, we can see a flood of programmes that are targeted towards this function in the coming years. These will measure people performance in all areas - from sales through to teamwork. Alongside data and insight from third parties, such as employee benefits providers, these tools will provide HR teams with unprecedented insight into their workforce. 

This data and insight will only have value if HR professionals have the capability to analyse and draw meaningful conclusions from it. To do this, they need to approach HR like a data scientist. These are the steps they can take to achieve this.

1. Start at the end
A data scientist wouldn't dive into a set of a data without first establishing what results they are looking for. Likewise, HR teams need to understand what problems they are trying to solve, be they people issues or business challenges. Trying to do everything at once by exploring reams of data will not get you anywhere. Instead, define your specific outcome.

2. Set about exposing untruths
The next step is to articulate those questions, in the form of hypotheses. The generally agreed scientific method is that you can't ever truly prove anything - just get closer to the truth by disproving things that are untrue. Continuing the theme of benefits and retention, our initial hypothesis would be that employee benefits have no impact on retention rates. But that hypothesis is easily disproven. And the data that helps you disprove it, such as a correlation between certain benefits and retention rates, will point you in the direction of your next hypothesis, showing you where you should look in more detail. 

3. Challenge yourself
This brings me to my next point: don't just use the data you have readily to hand. Top-level insights on benefits take-up and retention might help you disprove your first hypothesis, but it's more likely to say that a relationship exists than explain why it exists. To really explore the trends and complex relationships that underpin your hypothesis, you need to closely examine whether the data you have is deep and rich enough for the task at hand. If it's not, then work out where the gaps are, and how to fill them. 

4. Little and often
Most people tend to veer towards big projects: they are challenging and therefore rewarding when completed, and they also give you a chance to demonstrate your skills and abilities. But, when looking at people analytics, we should go the other way. Little and often is key.  Run regular, small projects to disprove small hypotheses, and build up a steady body of evidence. This will also mean failures are less damning. Rather than waiting until you're halfway through a big project before realising your hypothesis is wrong, you can take your small failures in your stride and move onto the next test.

5. Don't be afraid to fail
This ties in well with my previous point, failure isn't always a problem. It's usually a critical step in the journey to success. As you analyse your data, you won't always get the results you want. The important thing is to work out why something isn't working and take that on board for your next test. Sometimes, your hypotheses will seem unsolvable. When that happens, there's no shame in changing it or moving to the next one - not everything will be easily discovered through data. If you return to it later, you might find that you've gained new skills - or better yet, valuable new data - that help you tackle it.

6. Build a data-based argument
Eventually, you'll build up a body of evidence that you can use to support your arguments and suggestions. Now it's time to present your findings to your decision-makers, drawing on the right data and facts to make an undisputable business case - and ultimately, effect positive change in your organisation.

Source: HOB