Once pigeon-holed as a number-crunching operation that did little more than keep the auditors happy and churn out monthly reports, the finance function in today’s high-performing organisations is a source of invaluable business intelligence. If HR needs a role model in its efforts to redefine itself, it doesn’t have to look much further than the finance department, writes Keith Rodgers
Once pigeon-holed as a number-crunching operation that did little more than keep the auditors happy and churn out monthly reports, the finance function in today’s high-performing organisations is a source of invaluable business intelligence. If HR needs a role model in its efforts to redefine itself, it doesn’t have to look much further than the finance department, writes Keith Rodgers
Old-fashioned finance departments produced reports in a way that reflected their own rigid traditions and experience, using formats that made little sense to business managers and working to timescales that meant much information was out-of-date before it even landed on the CEO’s desk. Today’s finance professionals are able – if they choose – to provide up-to-date information that can be analysed from multiple angles and presented in a way that supports the needs of board members and line managers. Statutory and traditional management reporting still happens – but a new breed of analysis has been layered on top.
The HR department is some years behind finance in its evolution, but the route it’s taking is very similar. While its historical focus on administration and process remains a key component of its activities, particularly in terms of compliance, it’s under pressure to provide strategic value to the organisation, and human capital intelligence is one way of doing so. Uptake of HR analytics is relatively low, particularly compared to the finance function, but a wide range of tools is now available on the market. Once the mindset has changed – as it long ago did in high-performing HR functions – then the apparatus exists to support it.
These tools are designed to address a combination of metrics. At a process level, HR needs data to analyse the cost and effectiveness of activities such as recruitment and training, as well as its traditional administrative tasks. In addition, it’s under pressure to provide mechanisms for better individual employee measurement, as well as generating meaningful information about key performance indicators such as departmental and company-wide attrition rates. And at a more strategic level, a number of applications have emerged to help with workforce modelling and planning, and to bring new insight to compensation management.
From a practical perspective, adoption of these kinds of HR analytics now centres on four interconnected factors. The first step is for HR functions to create sufficient time to focus on business intelligence, primarily by freeing themselves from their administrative burden through investment in techniques such as employee and manager self-service. Second, they need to develop means to collate and manage the data they plan to analyse. Thirdly, they need to expand the narrow sets of metrics that they’ve traditionally used in order to deliver a far broader range of both operational and strategic analytics. Finally, they need a means to distribute analytical output.
Building the analytical infrastructure
The value of any analytical exercise is determined first by the quality of data input, but for many HR functions, much of the information that’s needed to support human capital intelligence is hard to come by. In some instances, it’s stored in unconnected paper-based systems – in others, it’s simply not recorded at all. Automating HR processes provides one answer to that problem.
In the past, the arguments in favour of HR automation have tended to centre on practical benefits such as improved efficiency and lower costs. But the ability to capture raw data provides an additional strategic edge. If they can manage benefits enrolment online, for example, companies are able to aggregate statistical data about levels of uptake and employee preferences. Because it’s based on actual commitment rather than expressed preference, this information provides a useful complement to traditional employee surveys. Learning applications are a good example of how process provides the core data for a broad range of analytical activities. Much of the emphasis in the first generations of e-learning applications was on the process and cost benefits – not least the fact that online learning cuts the travel and accommodation costs associated with classroom-based training, and also provides greater flexibility through techniques such as self-paced learning.
But today, many of the learning specialists are as keen to stress their analytical capability as their infrastructure offerings. Learning management systems capture data that can be interrogated to provide meaningful insight into a wide range of factors, including course popularity, course completion rates, cost per learner and so forth. Ultimately, it forms a virtuous cycle: learning techniques are automated and measured, and the analytical output helps to refine the way that processes are executed.
Leveraging process-based implementations in this way is important because it allows analytics projects to feed off other HRIT – and sometimes non-HRIT – initiatives. One of the many reasons cited for the low levels of analytics adoption is cost, particularly where companies have ambitious plans that require extensive data collation. The more that analytics projects can dovetail with other IT initiatives, the more economies of scale can be enjoyed.
In addition to these internal sources, the analytical infrastructure requires data that resides outside the HR function itself. As well as extracting compensation information from payroll applications, financial systems also provide data that helps HR cost out specific activities. Customer-facing applications can also be a good source of data – customer satisfaction levels, for example, are useful metrics for measuring employee performance in areas such as customer service or logistics. Most organisations will also look to pull in data from third-party benchmarking organisations to analyse attrition rates and other strategic indicators against industry norms.
Managing these multiple data sources is not an easy task, given that it’s distributed across the enterprise and beyond. One key requirement is for a central data repository – often referred to as a data warehouse – where information can be collated and interrogated. Historically, data warehouses have had a mixed track record, plagued by horror stories of giant implementations that went badly over budget and came in well past deadline, and it’s no mean task to establish this kind of infrastructure and then manage the disparate types of data that populate it. The leading software vendors have taken steps to reduce the complexity of these implementations, but it’s still important for organisations to be focused on the results they want and keep the projects firmly in scope.
New measurement techniques
Once data has been collated in a central repository, the analytical options are vast. At a basic process level, traditional HR metrics provide invaluable insight into the effectiveness of HR activity, from headcount assessment – still a major problem for multinational organisations – to turnover analysis. In the recruitment cycle, for example, comparing days to hire between different departments can throw forward significant disparities and point to problems either within the HR function or in lines of business.
But by bringing together data from numerous sources and examining human capital in a broader business context, HR intelligence can provide managers with far deeper insight into their human capital assets. Take, for example, an analysis of the impact of employee attrition among middle managers. Traditionally this would have been viewed by managers in terms of the direct costs that are incurred, particularly in terms of hiring fees: there may also be an implicit acknowledgement that the loss will impact the smooth running of the business, but this is unlikely to be quantified. But an exhaustive analysis of employee attrition would incorporate many more factors. See the two tables outlining the costs with respect to new employees and former employees.
Measured in this way, managers have a whole new insight into the true costs of losing key personnel – an insight that may encourage them to take preventative action to head off future losses, or to revise compensation policies and other factors that influence attrition levels.
These kinds of calculations take HR analysis away from pure process to provide far more strategic input to the business. A growing number of vendors now provide software to help with this kind of insight, ranging from specialist business intelligence tools to applications containing pre-built metrics. In addition, some vendors offer workforce planning applications, which allow HR to assess and model future recruitment and development needs in the context of their broader business planning and financial budgeting. Compensation planning applications also allow organizations to assess overall compensation policies in the context of industry trends and drill-down to specific departmental issues.
This kind of analysis is underpinned by established HR disciplines such as competency management, which is a key component of both individual performance management and workforce planning. Growing numbers of organisations are rolling out programs to define the core skills and competencies required for specific job types, and then providing managers with templates to assess individual employees. This data is fed from a variety of HR applications including recruitment systems (which contain details of an individual’s resume) and learning applications (providing details of training courses undertaken). Managed either within the core HR management system or a learning management application, this competency data can also be aggregated to provide insight at corporate level into what skills exist across the organisation and where critical shortages need to be addressed.
Distributing the output
The final piece of the analytical infrastructure is the method of distributing output. Just as the finance function has moved away from producing bulky management reports that were largely unread by line managers, so HR has to get its message out in a language that business managers can understand.
From the distribution perspective, many organisations are turning to employee portals, which provide a single view into the organisation where employees can access IT applications and find information relating to their day-to-day jobs. The language issue, however, is as significant as the physical distribution mechanism. Data will have most meaning when it’s presented in a familiar context, so it may be necessary for HR to adopt revenue/profit calculations more frequently than it’s used to. Above all, the key is to make the data relevant. Telling a line manager that absenteeism has increased in their department won’t make much of an impact: quantifying that statistic in terms of lost productivity gives it instant meaning.
Keith Rodgers is co-founder of Webster Buchanan Research (www.websterb.com), a market research organisation specialising in Human Capital Management and IT. He can be contacted at [email protected]
