Hit the mother lode
It will not have escaped readers’ notice that we are living through an age that is rich with data currency. The means to obtain, store and access it have grown exponentially, but as Albert Einstein once said, “information is not knowledge”, and indeed, data is not an end, but a means, to help construct strategy and measure deployment. So why are the majority of organisations merely scratching the potential of data?
Article by Mark Witte & Jeff Fox, Principals – Aon Employee Benefits.
Global research (Bright & Company) found that less than one third of global HR professionals rate themselves as either ‘Advanced’ (24 percent) or ‘Expert’ (seven percent) at HR analytics. However, across the board, one HR practice that uses data very effectively is pay and reward. It’s clearly essential to support organisational needs, requiring analysis and benchmarking, yet it’s now also a compliance matter. In an effort to facilitate greater pay equity, the UK Government recently introduced a mandatory disclosure requirement in the Gender Pay Gap Reporting Regulations. This places an emphasis on employers scrutinising reward practices and disclosing gender pay gaps. Beyond compliance, this data offers insights into how family-friendly leave policies should be reframed along with flexible working arrangements. Plus the disclosure of comparative differences between average male and female pay in a public forum exposes the quality of data.
HR analytics, however, requires a far broader perspective. HR strategy encompasses getting the best out of people, keeping the healthy ones healthy, understanding absences, dealing with presenteeism, mental health and so forth. Analytics can provide insights into the DNA of an organisation and information needs to convert to knowledge, to drive organisational strategy. Yet the process can appear complex and resource-hungry. By simplifying the journey, it is possible to gain an understanding of particular elements of the business, strengths and weaknesses, what success could look like and a platform to measure results. Start by understanding the goal. Will the insights be used to improve programmes? Reduce costs? Target absence or presenteeism? Highlight critical issues to the business? Demonstrate success of a particular project? Below are key streams that show employee benefits examples, but these can be cross-referenced to other elements of HR.
Management information/benefits claims data, is frontline usage of big-ticket material. It includes data from providers of medical insurance, income protection, life assurance, employee assistance programmes, health screenings and employer’s liability. Claims data and utilisation statistics are largely available and can be an ideal starting point. Insurers have developed standard management information (MI) reporting capabilities and, on a standalone basis, with the right interpretation, this can lead to some informed conclusions about prevalent health risks. Areas of the business that disproportionately contribute to a claims experience can be flagged, and positive action taken.
However, looking at data from one source may not provide the clearest picture. Benefit lines may only cover certain parts of the workforce, and MI available from some lines may not yield rich enough data to draw meaningful conclusions. It is also important that positive impacts of certain benefits and services are taken into account. Private Medical Insurance, Occupational Health and Employee Assistance Programmes are good examples of positive interventions and high utilisation in certain areas (especially for say, mental health and musculoskeletal conditions) is not necessarily a bad thing. To support this argument, however, analysis of other data sets can determine whether a positive return is being achieved through the current strategy, with Income Protection Claims data being one clear example.
Absence data, offers a number of valuable insights including an understanding of the impact of current health strategy and quantifying financial impacts. Assuming the ability to capture data is robust, then it can offer a company-wide profile of health risks and absence trends. Analysis of this data can be particularly powerful, given a number of other defining characteristics, not least that by nature, incidence of absence is far higher than claim or utilisation rates on health services and benefits and, as such, the data is much richer. It should hopefully enable segmentation across different parts of the business or different elements of the demographic. Also, with a defined indexing of reason for absence, underlying key health trends can be identified. With this, employers can start to build a true cost of health within their organisation and provide one defined metric to measure ROI. Analysis of incidence of absence cases versus the duration of cases also helps employers focus on health risks that represent the greatest impact.
Where absence analysis becomes even stronger is when it is overlaid against other sets of absence-related data. By comparing absence trends (key health risks, durations etc) against Occupational Health data or Income Protection claims (or even IP claims notification data) an employer can open up some real insight into the performance of their strategy. This can include both identifying where in the business absence policy and practice is being executed as planned, as well as understanding whether the existing health resources and interventions are aligned to the highest risk areas. For many, the real challenge is the ability to effectively trap robust absence data in the first place. There is a range of absence recording solutions available and there’s potentially a clear value in doing so. However, before embarking on any health and wellbeing strategy, employers should ask themselves; “in a year’s time, what would I like to evidence that demonstrated effectiveness”? If part of that equation is lower absence costs, reduced number of long-term absence cases or evidencing that spend on health-related services was appropriately aligned with the highest risk areas, then effective absence recording will be an imperative. If other metrics are important, then analysis of other data sets will become more important.
Advanced behavioural data is about employers looking to gain the greatest understanding of their company’s health profile, looking at claims data or absence data will still not give them the clarity they need. Claims and absence data largely measure health risks once they have already occurred and will not highlight underlying behaviours prevalent that drive these outcomes. A study conducted by the World Health Organisation in 2010 highlighted that there are eight common behavioural health risks that drive the 15 most common chronic conditions that in turn account for 80 percent of health claims costs worldwide. The most robust data analysis would, therefore, aim to understand behaviours and risks at the earliest stage and use this to help guide future strategy.
However, availability of data, or even understanding what data sets may be relevant, can be a real challenge. Technology plays an important role in accessing new data streams; activity trackers, wearables and mobile health apps can generate significant volumes of data that give employers insights into many of the behaviours referenced in the WHO study. If cultural and employee engagement challenges can be addressed then the data flowing from these new sources should be valuable in aligning wellbeing initiatives and highlighting effective communication channels. In addition to these data sources (or, instead of, if the tech solution is not appropriate), it is possible to get creative in data collection points to evidence health behaviours. Examples include choices from onsite canteens, take-up rates for “healthy” benefits such as gym membership and cycle to work and unused annual leaves entitlements. One firm with little in the way of benefits or absence data was able to look at trends of waistline measurement of the staff uniform to help align and measure the effectiveness of their wellbeing strategy.
It goes without saying that the Employee Engagement stream is vital to HR strategy, but can it be measured and proved through data? The answer is yes, with big data. Taking large data sets across workforces reveals behavioural patterns and trends. The stakes here are high. Our recent study* showed that engagement can pay dividends; a five point increase in employee engagement is linked to a three percent increase in revenue growth in the subsequent year. However, employers need to go beyond judging engagement from simplistic data like employee website clicks or benefit selection. Far more insightful measures including repeat selections, benefit advocacy or productivity, engagement is where data needs blend with attitudinal research.
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