By fully embracing your analytics software you can use HR data to inform the success of your company
The label, “The Information Age” may be something of a cliché but it’s also accurate; in all walks of life, we live in a sea of data. The world of HR is no exception and your organization has a choice of whether to sail that info-sea boldly or simply paddle in it.
The potential benefits of better use of your HR data include enhanced performance against KPIs and other targets, more objective corporate insights, smoother team working and collaboration, and better management and resources decisions, at all levels.
A quick word about Big Data
The likelihood is that your HR information doesn’t qualify as Big Data, not yet at least. Big Data is macro data and unless you’re a multi-continental organization with an employee population in the tens of thousands (or more) then you’re dealing with a less-than-Big dataset. However, with a HRMS as a centralized data collection point, you’re still harvesting large quantities of data offering new opportunities and Big or not, the value you derive from your data depends on the technology you use to store, access and crunch it.
Before you begin, you should set the right HR KPIs. Traditionally HR metrics cover employee satisfaction, turnover and skills which are all perfectly fine. But your HRMS can provide deeper insights about position, role, team or location and allow you to compare these metrics with your direct competitors or industry sector. For example, when looking for suitable KPIs, productivity can be a rich source especially through the inclusion of issues such a collaboration across teams, workload and shift management.
Here are 4 ways in which your HRMS might help you better use your HR data1:
#1. Predictive analytics
Let’s get this one out of the way first. Not because it deserves to be quickly dismissed – quite the opposite – but rather because HR analytics and reporting probably come to mind first thing when thinking about HR data. Analytics and reports can be categorized four ways:
- Basic operational quantitative reports that answer the various How many?questions. Pockets of information that are nice to know but on their own have limited application.
- Expanded operational reports that tap into wider sources of information to benchmark and analyze performance – a first level of insight.
- More strategic outputs, applying segmentation and statistical analysis to the data to develop ‘people models’ to inform longer-term decision-making.
- Predictive analytics that draw on multiple data sources for scenario planning and detailed risk analysis, providing a genuine strategic contribution.
Historically, HR has focused mainly on the first two levels of reporting. The challenge is to not only to develop a better appreciation of the possibilities contained in multiple sources of business data, the development and application of the correct formulae and algorithms, and specialist data organization, metrics and modeling skills. And then, of course, to ‘sell’ those possibilities to the C-suite in terms of succession planning, talent management strategies, retention analysis, and diversity.
It’s not surprising that many find the scope of predictive HR analytics exciting considering that forecasting using HR data can be helpful in a number of ways.
Understanding recruitment in terms of employee turnover
Your HRMS can help with any difficulties you might face when it comes to employee turnover. In most cases, people will leave a company and then the employer with recruit to replace them. But if the employer had an indication of the overall numbers of people that are likely to leave in the next year, they could have replacements at the ready. This type of predictive turnover model also offers insight into reasons why employees leave such as compensation or employee tenure. In this case, knowledge is power and companies can use this information to work to bring down turnover rate.
Take steps to mitigate absenteeism
Most employers will track when their employees are absent as well as any corresponding reasons for absenteeism. This method is usually centered around individuals rather than taking in the bigger picture. However, a predictive model of absenteeism takes into consideration past absence patterns and then looks at those in relation to data about other employee demographics such as roles within the workplace. With this kind of analysis, employers should be able to understand potential areas within the organization which much cause problems in the future. Drawing conclusions from a range of HR data, including a combination of qualitative and quantitative data, should mean better results.
#2. Better quality data for all
Naturally, better use of HR data holds opportunities for more than just the senior decision-makers. However, the key challenge is bringing that data to the rest of the workforce. Most HRMS packages include some form of self-service which, however simple, involves data access. The key is enabling access to the right data and making that access easy – and that is a question of having a well-designed UX or user experience.
Picture a user-customizable personal dashboard. Most employees may be limited to accessing their own personal data but even that offers real efficiency benefits because you’re effectively devolving responsibility for the quality of this data (the foundation of any HR database) to the people best-qualified to determine its accuracy. Managers, meanwhile, will be viewing the KPIs, targets, productivity and performance ratings, and personal data for their teams and divisions relating to payroll, benefits, absence rates, and even the progress of the latest recruitment campaign. For these HR managers, embracing the functionality of HRMS to manipulate and extract data for their own analysis is beneficial in terms of trend identification.
Such dashboards don’t have to be over-complicated – in fact, the simpler they appear, the more they’ll be used – especially in their first generation. And to boost engagement, giving users a degree of control over layout and functionality goes a long way.
#3. Real-time analysis
Traditionally, data analysis has been about using the past to decide the future: what do the figures for last year tell us and how can we use that knowledge in the year to come?That kind of thing.
And while the same principle applies now, the volume and instant availability of the information has upped the immediacy considerably. Now, it’s more a case of: What just happened and what do we do now? Especially when it comes to ‘shop floor’ resources and day-to-day decisions about priorities and allocations. Your HRMS can help with efficiency in the short term as well as the long-term by allowing managers access
At a very basic and impactful level, a couple of examples of real-time workforce data, accessed via the HRMS and used in workflow planning might be:
- Identifying gaps are in the workforce (caused by unexpected sickness, for example) via scheduling and time attendance data.
- Fulfilling daily skills needs to meet targets by cross-referencing with training and development records.
Of course, the potential exists for much more than filling unexpected gaps in a work schedule… by drawing on up to date talent data, succession plans and individual development goals, as real-time development opportunities arise, rapid notifications can be authorized and sent to exactly the right people. The crux of it is that real-time data can improve visibility, which in turn promises that HR becomes a more integrated part of your business.
#4. Better insights for the HR team
Finally, let’s not forget the HR administrators and managers in your organization. With varying degrees of detail and insight, most systems will enable access to data concerning:
- Turnover – including rates for different teams, roles and offices, including contributing factors.
- Retention – including rates and hotspots with the organization, allowing targeted efforts for improvement.
- Talent management – identifying high-flyers, potential leaders, and assessment of effective development programs.
- Recruitment – factoring such issues as sources of new hires and effectiveness of current campaign strategies.
- Productivity – moving beyond a blunt KPI volume or quality-based performance result to examine issues such as workload and shift management, skills needs, and cross-team working and collaboration.
The final issue – though perhaps ‘barrier’ is a better word – is the question of whether you and your HR team have the necessary skills to take full advantage of the data you’re collecting and storing. The traditional HR skillset has rarely focused on data measurement, statistical analysis, and evidence-based decision making. Not that such skills are lacking in most organizations, just that they are more often found in teams such as Finance, Logistics and Marketing. While this highlights an obvious need to recruit such skills into HR, this is also a question to which technology may hold part of the answer: internal social communication tools (increasingly part and parcel of HRMS software) can help break down past functional silos and departmental barriers, encouraging inter-team collaboration and an ongoing cross-pollination of data-related skills and knowledge. And who knows, maybe those other departments might also learn something from their HR colleagues in the process…
About the author
Longtime HRMS World contributor Dave Foxall has worked as HR Manager for the Ministry of Justice for a number of years. He writes on a broad range of topics including jazz music – and – of course, the HRMS software market.
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