Analysing benefits data can make HR strategy more effective and help provide more relevant benefits communication, says Victoria Furness
Over the past decade, there has been tremendous innovation in how employee benefits are managed. Absence management systems, flexible benefits systems and total reward statements are just a few of the new applications that have been installed by HR and benefits departments to give employees more choice, and render benefits managers more automated structures and processes.
Such innovations have brought with them a significant amount of management information on employees, their benefits entitlements, absence patterns, benefits preferences and other aspects of employment. While some managers grab any data they can find with magpie-like zeal, others view analysing each system’s data as yet another task to add to their already-heavy workload.
Yet whatever their attitude towards benefits data, there is no escaping its existence or its potential importance in making a benefits strategy more effective.
Benefits data has always existed in some shape or form, however, technological developments have now made it more accessible by simplifying the collection, retrieval and interrogation of information. With HR departments under continual pressure to improve the recruitment and retention of staff, unlocking this data could provide managers with fresh insight into their workforce and pave the way for employers to provide more relevant benefits communication.
At the very least, this data can be used to justify benefits spend. Paul Avis, corporate development manager at Ceridian’s LifeWorks division, says: “There is nothing better than data to prove a positive return on investment. I know of one organisation that has only a tenth of a normal income protection providers’ claims experience for long-term absences after two years, and yet it uses ill-health retirements and has never quantified the costs of these. In effect, the occupational health unit could be literally saving millions in pensions money, but is routinely viewed by its employer as a luxury item.”
One of the advantages of data is that it paints out benefits take-up and preferences in black and white, so it’s a way of finding out what employees really think. Philip Hutchinson, principal at Mercer Human Resource Consulting, says: “Data provides a better way of understanding the various stakeholders you have to deal with and enables you to do some trend analysis, so you know what kind of benefits to invest in.”
It’s also a potentially useful way of auditing an organisation’s compliance with anti-discrimination legislation. Martha How, head of reward consulting at Hewitt Associates, points out: “More companies are monitoring and looking at employee data to assess their risks around various discrimination legislation. For example, what’s the balance [of benefits] by age and if there’s no-one [aged] over 50 [years] in the workforce, is that indicative of age discrimination?”
At the very least, employers should be doing a basic analysis of the take-up and benefits preferences of individuals from each system, particularly in flexible or voluntary benefits schemes. This data can then be benchmarked against external industry data to determine if the take-up of each benefit is in line with the sector average.
The HR team at computer manufacturer Toshiba Information Systems analyses the company’s benefits data to improve decision-making, look for trends and monitor whether there are any key issues it needs to address. Susan Stevens, HR director, says: “With flexible benefits, we analyse take-up rates for each benefit each year, and we use that to decide whether we’re going to continue with each benefit at renewal or whether we’re going to drop it.”
Collating data can also indicate to employers where any potential problems lie within an organisations, for example, around high absence levels. Strategies and policies can then be set to tackle these.
Similarly, mileage capture tools can be used to monitor the distance covered by company car drivers. Employers can then use this information to identify drivers who may be covering a particularly high number of miles and see, for example if they are using the most cost-effective type of car or driving within safe limits. Passing this information onto drivers may also have an impact on their fuel consumption if they are able to identify shorter routes, particularly for regular journeys.
In order to improve their analysis to gain deeper insights into their workforce managers must know what they are looking for. “One of the problems of having too much data is that you wouldn’t know a trend if you saw one,” explains Hutchinson.
Once employers have decided on their objectives they can start to interrogate the data in detail; for example, by looking at it in new combinations, cross-referencing data from different systems or segmenting the workforce by various parameters, such as department, location or job grade.
The HR team at PricewaterhouseCoopers, for example, is currently embarking on an ambitious cross-referencing exercise to integrate data from its absence management system, health screening programme and employee assistance programme (EAP). The aim is to spot any trends in its benefits health data and also to identify any gaps in reporting across its healthcare providers.
Connecting systems in this way can also help an organisation deliver a genuinely joined-up benefits strategy. For example, if a benefits manager wanted to assess an organisation’s provision of care for staff with cancer, it could consolidate the firm’s private medical insurance (PMI) claims data, EAP calls, health screening information and occupational health case data in one place. “The idea would be to assess the cancer journey, the value of each intervention and to optimise such expenditure. So, for example, when an employee calls the PMI provider to authorise cancer treatment, an EAP pack is also sent to the employee focusing on what support is available,” says Avis.
At the same time, employers can be more targeted in their communications, enabling them to promote specific benefits that are thought to be most relevant to individual employees. This is one area that Toshiba’s Stevens is keen to explore: ” I want to start using our data is to target communications to certain individuals within the organisation. So rather than sending out blanket communications, we can target [these] to the people we think are likely to be interested in certain benefits to make it more effective.”
The concept of total reward statements – which show employees the value of their entire reward package – fits neatly into this vision of joined-up benefits. Given the variety of systems they touch – from payroll to bonuses and benefits – total reward statements tend to necessitate a data consolidation and cleansing exercise in their own right. As Peter Christie, director of reward consulting at the Hay Group, points out: “If you’ve pulled all this information together [for data analysis], why not do total reward statements, as it is such an easy win?”
Yet it is often this task of pulling such a quantity of information together in the first place that holds many organisations back from embarking on total reward statements and general data analysis. This is partly because few IT systems – particularly older versions – can ‘talk’ to one another, so time is lost in downloading information from different sources into the same format in order to analyse it consistently.
Data warehouses provide one of the best ways of storing and collating benefits data, but like the large back-office IT systems used to manage HR information (such as those delivered by SAP and Oracle), they also tend to be very expensive. This is not to say that data can’t be collected or analysed on an Excel spreadsheet, but the task becomes more complicated – and potentially more erroneous – the larger the employee base.
The advent of online-based HR and benefits platforms from providers such as Aegon Benefits Solutions and Thomsons Online Benefits has helped to simplify the administration, collection and analysis of benefits data. The advantage of these and other emerging systems is they have a simple front-end, so don’t require managers to undertake lots of number crunching in order to gain some quick insights. Chris Bruce, director of marketing and technology at Thomsons Online Benefits, says: “We’ve effectively automated the translation of benefits data into meaningful information, so that in a snapshot, a reward specialist can access and use this information to make an informed decision on their reward strategy.” The more accessible the data is, the more likely benefits managers are to take analysis from it and act on this, on a regular basis. If the data is held in one place, it is also likely there will be fewer inconsistencies. This is important because if an organisation is basing its entire benefits strategy on data trends, then that information needs to be accurate.
Data accuracy is also one of the key principles behind the Data Protection Act 1998, which organisations must adhere to at all times when dealing with staff data. The simplest way to ensure compliance is to check that all staff are treated anonymously. “You might say employee number 12345 makes active choices in flex and then group people who make active choices in flex together as a population, so you’re tracing individual records but not personalising them with names,” says Hewitt’s How.
Most automated systems can also limit access, so benefits managers and others can only view the areas they have permission to. This is how it works at Rolls-Royce. Sharon Ambrose, team leader of the payroll and expenses department at the car manufacturer, says: “Our SAP system is set up by different profiles, so I’d have different access to someone in taxation and HR.”
Establishing such rules from the outset is important – if only because the need for data protection isn’t going to go away.
Ultimately, the biggest challenge facing benefits managers in making sense of data is finding time to sit down and perform the analysis. Toshiba’s Stevens concludes: “The challenge for most people is finding the time. The data is there. But if you can get it out and analyse it effectively, you can use that data to make more informed decisions.”†
Be data aware
Laurence Rees, a partner in the employment team at law firm Reed Smith Richards Butler, highlights some of the main areas of the Data Protection Act 1998 that employers need to consider when collecting and using employee data.
- Broadly speaking, any personal data has to be processed fairly and lawfully, in accordance with a series of principles under the Data Protection Act.
- The Act distinguishes between two categories of data: personal data and sensitive personal data. The latter relates to a number of subject areas, of which the one most relevant to benefits is data concerning an individual’s health.
- Processing of sensitive personal data has to be carried out with the consent of the employee. So don’t use sickness records where absence records will do – and only disclose sickness information where you have to.
- Guard against routinely or randomly passing on certain [employee] information on the assumption that it must be of some benefit to have this on file.
- Familiarise yourself with the Employment Practices Data Protection Code, which sets out guidance for employers in performing their obligations under the Act.
ING Direct’s HR data is brought to account
Direct savings bank, ING Direct, has experienced an significant increase in the amount of data available.
Mike Jeffrey, its interim head of compensation and benefits, says: “It is undoubtedly true that technology has produced more data. But generally we also have better tools to work with the data and analyse it.” The company saw a surge in data at the start of the year, when it introduced a flexible benefits programme for its 570 UK employees. While its provider Vebnet has produced useful management information about employees’ benefits’ preferences and take-up that will help the bank refine its selection and communication in the scheme’s second year, the surge in data has also increased the workload for the bank’s internal HR team as its HR, payroll and flexible benefits systems are not yet fully integrated.
As a result, “there are several intermediate stages to get data processed accurately into payroll at the moment”, explains Jeffrey.
Consequently, his team has been cross-referencing each employee’s choices against Vebnet’s data to ensure there aren’t any inaccuracies in their records.
ING Direct has also been able to roll out total reward statements by downloading salary information from the payroll system into a spreadsheet and passing this to Vebnet to upload and integrate with information held in the flexible benefits system.
“In the longer term, we have a project to introduce a totally new HR system, which hopefully will help to smooth some of these problems out and help us avoid double keying information, problems with spreadsheet transfers and so on,” he adds.
In the short term, ING Direct’s focus is firmly placed on the return it can achieve from its flex scheme. “It is a question of where we get the best return from our investment in benefits analysis. And at the moment, our return on investment is focused on the flexible benefits scheme,” Jeffrey adds.