Four key steps to get started with HR analytics

Four key steps to get started with HR analytics

HR analytics offers huge opportunities for organisations, but getting started can be daunting. Michael Carty reports from last week’s CIPD HR analytics conference.

HR can and must “seize the data”. This is according to Dr Giles Slinger of OrgVue, host of the CIPD HR analytics conference in London.

HR analytics involves using multiple HR metrics to gain insights and inform decisions. It can create “an opportunity for HR to take a new role in the business world,” Slinger told delegates. Through analytics, HR can use data to help drive organisational success. This in turn can help build HR’s strategic influence.

The apparent complexity of HR analytics can be off-putting to many in the profession. But it need not be.

“Analytics is not about data and it is not about HR. It’s a change journey and a capability journey,” said Unilever HR vice president Placid Jover. “It’s about using data to build bridges with the business.”

Here are four key steps to help HR professionals to get started on the HR analytics journey.

Step 1: Be clear on the problem you are trying to solve

HR analytics without a clear purpose is a lost cause. Before embarking on any project, it is vital to identify the objectives, the problem that needs to be solved and the metrics required to solve it.

The most valuable HR analytics work involves looking beyond the needs of the HR department, and assessing how HR data can be used to help the wider business.

“The essential thing is to keep coming back to that issue of what is the business problem,” said Saberr CEO Tom Marsden. “This roots us in getting the language right and the data right.”

Step 2: Just dive in

HR analytics can be daunting. Many projects fail to get off the ground due to uncertainty over where and how to begin. HR often procrastinates over concerns and even embarrassment at how “messy” its data is, said Slinger. But “the reality of the HR world is that mostly the data is a bit messy”. The solution is just to dive in.

Pick a starting point and “start light”, advises Mark McGuire, HR manager at global healthcare company Baxter International. In 2014, McGuire launched a project to harness HR analytics to resolve problems and create high-performing teams across the business. The organisation’s HR data was stored across multiple systems, making it difficult to collate and analyse.

Attending last year’s CIPD HR analytics conference helped McGuire decide where to begin: “I was inspired by the fact that you could start light, with just a spreadsheet, and pull analytics into it.” He used Excel spreadsheets as the data centre, and HR analytics has quickly flourished at Baxter.

McGuire’s team now provides the business with highly valued data on a range of metrics, including new hire success, compensation and benefits benchmarks and employee engagement levels. The project is set to expand over the coming year, with plans to hire data specialists to join the HR department, and to move towards predictive analytics.

Step 3: Build HR’s credibility with the business

HR analytics is about HR speaking the language of the business. “HR analytics must talk to the organisation,” commented McGuire. “Otherwise it risks being seen as pink and fluffy.”

It is essential to avoid HR analytics projects sliding into HR for HR’s sake, said Neal Barnes of Tullow Oil: “This isn’t about HR reporting for the sake of HR reporting. This is about what managers want.”

To drive his initiative, Barnes reached out to managers across the business to find out what data would be of greatest use to them. He also forged strong connections with the finance department.

At the same time, his HR department took ownership of its data, and how and when it was updated. Previously, HR data had been scattered across multiple systems, and was updated on an ad-hoc basis by HR and finance.

But HR assuming full responsibility for its data created a new challenge: HR now had to get the data right. Barnes’ HR team has focused on developing its analytics skills, accordingly. “If data is not seen as credible, the business will not look at the reporting,” he said. “Check the data again. And again. And again.”

Step 4: Simplify the outputs

HR analytics data has most impact when it is presented in a simple, clear and accessible way.

Reports should use terms that are accessible across the business, avoiding HR jargon. Marsden said: “Make the language of analytics simple, don’t hide behind complex language that obfuscates rather than provides clarity.

“You need a clear, common language that underpins the data.”

Some organisations create a “data dictionary” to ensure clear and consistent language is used in all HR analytics reports.

Using dashboards to make data accessible in a clear and visually appealing form has proven highly successful for Unilever. Data visualisation and HR data dashboards are invaluable tools in helping HR analytics outputs move from providing insight to provoking action, said Jover, advising that “you can put HR analytics on the map when you are clear on the message you want to land, and have well-designed tools to land it”.

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