The modern employee demands a working culture that delivers flexibility, work-life balance, ongoing professional development, a strong sense of purpose, and most importantly, fair compensation. If these requirements aren’t met, they’re more willing than ever to switch jobs and find opportunities aligned with their personal objectives.
The offshoot is that employers worldwide are evolving their value proposition to retain and recruit talent. Strategies include everything from loyalty bonuses to hefty benefits packages, flexible remote-working arrangements, and regular performance-based increments. However, these offerings are still primarily put together based on experience and intuition rather than in-depth analysis.
If you’re wondering how to bring accurate, data-driven decision-making to your HR, start implementing payroll analytics.
Why Focus on Payroll Analytics?
Payroll Analytics is the First Step to Data-Driven HR
Although payroll is an often overlooked HR area, it’s undeniable that the process plays a pivotal role in securing employee commitment to their company. This importance holds true especially for millennial workers, who, according to most metrics, are more economically vulnerable than previous generations. Today, any employer that can walk the walk and ensure the financial well-being of its workforce is a step ahead of the pack. Then, of course, there’s also the fact that payroll makes up anywhere from 50% to 70% of the average company’s overheads to consider as well.
HR initiatives in this area can encompass everything from self-service financial management tools like the kind rolled out by Walmart to instant payment options pioneered by the likes of Uber and Lyft. Either way, a data architecture that can effectively consolidate and deliver payroll data is required to drive your objectives.
In many ways, payroll is purpose-built for advanced analytics. The data produced is largely quantitative in nature, occurs repetitively, and is usually reliable with little room for human error. So, suppose your enterprise has processes to leverage the data and transform it into accurate and timely business intelligence. In that case, you can bring enormous value to various parts of your compensation management.
Build Payroll Analytics Around Your Company’s Use Cases
Aligning Compensation with Corporate Objectives
How do you Make Sure Your Compensation Policies Hit the Mark?
One of the first steps in any analytics process is setting goals. For example, you might prioritize throughput and customer feedback at a call center, so benchmarks for employee remuneration would be selected based on their effectiveness in these areas. Other vital factors for companies to consider when determining fair compensation would be – level of education, years of experience, and attendance.
With the correct data architecture in place, you can collect the necessary information to measure these criteria, feed it into a consolidated repository, and then build dashboards to analyze required metrics. With the resulting insights, you can design a payroll structure that’s personalized to the performance and expectations of each individual.
Ensure Error-Free Payroll Processing
Ensure You’re Getting Those Key Calculations Right
So how do you make sure that payroll is set up for maximum cost-efficiency and optimal engagement?
Many issues can add significantly to overall operational costs, including:
- Duplicate payments/checks to the same employee.
- Miscalculated checks.
- Overstated claims for overtime or other bonuses.
- Payments made to terminated employees.
Manually tracking these errors or possibly fraudulent activities can be difficult, especially when you don’t have a consolidated view of payroll processing across your company. However, you can monitor trends across payment periods to identify areas of concern with analytics in place.
For example, a particular department in your organization could show an inexplicably increase in payments over a certain period compared to the mean. Correlating this data against employee wages in the department would reveal whether hours and compensation line up. Similarly, if one of your functions is performing multiple payroll runs regularly, this could point to inconsistent policies for compensation in the area.
By proactively identifying and solving these concerns, you can cut down on wastage and ensure proper compensation goes to the right people on a timely basis.
Improve Workforce Planning
<a href=’https://www.freepik.com/vectors/people’>People vector created by pch.vector – www.freepik.com</a>
You have to ensure that the distribution and compensation of your people matches up with the dollar value they’re bringing into the organization.
So, if you have two products operating under your brand, both reporting roughly equal production and payroll costs, but one is performing far better in the market, then you need to investigate whether the budget for compensation and benefits is being allocated optimally.
Advanced analytics could also turn up interesting trends that explain the reason behind varying performance levels. For example, several key employees in the underperforming product area could be nearing retirement age, or the staff may be comprised of mostly new hires.
With complete visibility into the costs and revenues of each area, you can identify whether you’re best served to redeploy staff to the more effective product or whether you need to reexamine overall hiring within specific departments.
As a result of these moves, you can bolster disengaged employees that lack the necessary structure to showcase their best talents or design incentive-based payment schemes that reward high-performing
How to Build a Data Architecture for Payroll Analytics
An HR Data Warehouse Can Unlock the Promise of Payroll Analytics
The biggest roadblock to effective payroll analytics is that critical data often gets siloed into specific systems for different departments and functions as organizations grow in size. For example, certain information might reside in an HRIS system, while other relevant data is only available to finance, marketing, production, or sales. Additionally, most of these sources are transactional, so while they may be useful for operational-level reporting, deriving consolidated insights from them requires substantial manual effort.
First, hidden intelligence needs to be extracted from each source. Then it needs to be cleansed and aggregated before consolidation. As a result, queryable data is often outdated by the time it ends up in any centralized reporting system.
There are a few ways around this problem.
First, you could build a customized architecture to integrate data from various sources and feed it into your analytics system. But this would require considerable input from data warehousing experts, ETL specialists, and data analysts. It would also be challenging to iterate this custom-built solution as your organization’s systems and sources for payroll data continue to expand.
Another possibility would be to replace your current siloed architecture with a global payroll reporting system and leverage any analytics provided via the solution. Of course, successfully implementing this type of initiative could take years with the commiserate investment required. At the end of the day, you’re subject to vendor lock-in, and you still have to figure out how to integrate non-HRIS sources into the architecture.
Build The HR Data Warehouse with Astera DW Builder
Finally, you could look to build an HR data warehouse that can take care of all the requirements for your payroll analytics. Astera DW Builder provides a platform to get you from ideation to implementation in just weeks.
One of the most important parts of building an analytics process from scratch is designing data models. When you’re dealing with disparate sources of data, each with its own specific reporting requirements and relationships then you might need to engineer multiple schemas (data marts) to feed into your data warehouse. With an end-to-end data warehousing tool, the process is accelerated substantially. You can replicate schemas from your chosen payroll data sources in minutes, modify them to suit your architecture, and even iterate them using the same approach as your requirements change.
A good DWA tool should also offer users the ability to design the most effective schemas to meet their needs whether that’s a traditional dimensional model or a 3NF architecture.
Another key benefit of a fully functional analytics process is efficient historical recording. Tools like Astera DW Builder offer built-in functionalities that are aligned with data warehousing best practices, so you can set up your data warehouse to track key historical data (employee roles for example) with minimal work on the user’s end.
If we talk about the most time-intensive task in any sort of analysis (collecting and preparing data for decision-makers), then you’ll find automation comes in extremely handy. With code-free data pipelining features, ADWB allows users to automate the extraction, transformation, and loading of data from sources to destinations.
With the legwork done and all your key data consolidated in an HR data warehouse, it’s really just a matter of feeding that data into the BI tool of choice and then building dynamic dashboards that accurately reflect each area of your payroll process.
Looking to Find out More?
Watch the demo to learn how Astera DW Builder can turbocharge your path from simple payroll reporting to an analytics-ready HR data warehouse.