Three principles of effective people analytics

Michael Carty reports from the CIPD HR analytics conference, and identifies three principles of effective people analytics.

“Data capability is one of the biggest muscles HR will have to use in the next 10 years.” This is according to Greig Aitken, group head of people strategy and insight at RBS, in his keynote address to the November 2018 CIPD HR analytics conference in London. Using data to drive evidence-based decisions – and communicating data-derived messages in a powerful and compelling manner – is now essential for HR, he says.

The CIPD shares this view. Its new HR profession map places evidence-based decision making at the heart of the work of the modern HR department. “If we don’t have evidence, we’re far less likely to make decisions, and we’re far less likely to have impact in our organisations,” says CIPD senior research advisor Edward Houghton, speaking at the conference. “Therefore, as HR becomes more professionalised, more impact-driven, we’re putting a greater emphasis on evidence.”

Three key principles of effective people analytics emerged throughout the conference.

1. Understand the business

For people analytics initiatives to be effective, they must be grounded in a thorough understanding of the organisation’s strategic objectives, and of how people data can be used to drive the achievement of these objectives. So says Elaine Mahon, head of people analytics at ONS. “Everything we do in people analytics has to have that golden thread right through to what is good for the business,” she says.

“Truly understand your business and where the customer pain points are.” This is fundamental to achieving any success in people analytics, according to Michael Collins, senior digital learning and user experience (UX) specialist at River Island. On joining the organisation, Collins looked at how HR could use data and analytics to help River Island deal with the strategic challenges common to all modern retailers. “Customers are now in charge,” he says. “This is a challenge for retail and for HR.” Collins’ team applied UX and customer experience approaches to their use of data and analytics to help boost employee engagement. They chose to treat employees and potential employees as customers of the organisation, and worked to improve the experience of each accordingly. One aspect of this work involved the introduction of regular, internal customer insight pulse surveys to provide constantly-updated employee engagement data. HR now produces regular “heatmap” visualisations of employee engagement score results, which make it easy to identify stores and departments with lower levels of engagement, in turn enabling the team to take targeted action.

HR must also be clear on the precise question that it is seeking to use data and analytics to answer, says Andrew Marritt, CEO of Organization View. For example, Marritt considers the best way for HR to frame the question of labour turnover and retention: “It’s not about asking ‘how do we reduce attrition?’, but ‘how do we reduce the negative impact of attrition?’ It’s about clarifying the question.”

2. Make smart use of resources

No matter the size of the organisation and the resources available to the HR department, high-impact people analytics activities are possible. At one end of this spectrum, Collins described how River Island’s HR team took a self-starting, DIY approach to people analytics, using free tools such as Google Analytics to process, analyse and generate reports on people data. However, Collins emphasises the point that HR must always bear data protection considerations in mind, particularly in the General Data Protection Regulation (GDPR) era. All data uploaded to Google Analytics was fully anonymised, and at no point was Google Analytics connected directly to the organisation’s HR information system.

At the opposite end of the resources spectrum, Aitken gave an overview of how he helped RBS to create a single source of people data for its entire global workforce. He says that GDPR was an opportunity to standardise data usage. “All people data at RBS is now centralised,” he says. “People can’t squirrel away data in spreadsheets locally. We now have a single, golden source of people data. Our managers who deal with data can only access aggregated, anonymised data.”

People analytics technologies enable Aitken’s team to manage, analyse and use the huge amount of people data generated by its workforce. For example, data and analytics has transformed employee engagement surveys at RBS. The previous system involved multiple engagement surveys in multiple languages created by individual teams across its international locations. Aitken’s team replaced these with a single biannual survey, which generates a net promoter score for each department.

Not all people data is numeric, in Aitken’s view. The data resources available to HR take numerous forms. “Don’t think of being evidence-based as being quantitative,” he says. “It can also be qualitative. And it can be even more powerful when you draw quantitative and qualitative data together.” For example, the RBS employee engagement survey typically generates around 67,000 text responses on what employees believe constitutes a “great place to work.” RBS employs artificial intelligence to process this data. They use IBM’s Watson technology to conduct an anonymised sentiment analysis, identifying key trends from these text responses to provide evidence-based qualitative, insights on what employees want. HR sends these insights to leaders in each department, together with a list of 15 recommended actions to help make their department a better place to work.

But the most critical resource for successful people analytics is the capability of HR practitioners themselves, argues Marritt. HR professionals must have an understanding of numbers to thrive in the modern business environment. “I don’t think HR is a place where you can take pride in or hide behind not knowing how to use numbers any more,” he says. However, this does not mean that everyone in the HR profession need necessarily retrain as a data scientist. “HR people need a base-level understanding of numbers. Does it need to be better than a refresh of a GCSE in maths? I don’t think it does.”

3. Tell the story

For people analytics activities to have any impact, HR must be able to communicate the findings in a compelling way that drives action. “Data is only important if you do something with it,” says Aitken. “Don’t just show the data. Tell the story.” HR must learn to draw out a narrative from data. Data visualisation can be a powerful tool to help achieve this. “The business loves a nice chart, a league table of some sort,” he says.

HR must “explain the why” of people analytics, Kim Saunders, senior people analyst at ONS says. When she joined ONS in 2017, ONS was ranked in the middle of the market for employee engagement, but wanted to be at the top. Saunders analysed the organisation’s extensive employee engagement data and identified key problem areas in which she felt that the organisation needed to take action, including dealing with departments experiencing higher absence rates. Saunders recognised that she had to communicate her findings in a compelling manner, to give senior leadership “the motivation to change.” She favours the direct approach, telling senior leaders a story that includes recommended actions: “Be hard hitting. Don’t hold back. Make them sit up and listen.”

Saunders produced a presentation ranking senior leaders on employee engagement scores, and communicated these findings to them in front of their boss. The presentation included a cluster analysis to discern correlations between absence and departmental performance and between absence and employee engagement for each department. Saunders’ high-risk approach paid off, with senior leaders taking prompt action to resolve the situations she highlighted. “If you create a crisis, you create a change,” she says. “Make it uncomfortable for them to continue doing what they are doing.”

However, this is not the only way to communicate people analytics findings. Collins sees data insights as the starting point for a conversation. He says that data had a transformative, positive effect. “Data and analytics have enabled us to have meaningful, informed conversations with managers, based on tried and true data,” he says. “This has been revolutionary in what we do.”

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