Big Data isn’t the problem.
Despite the hype around “big data,” and the pressure on different business functions to prove out how they’re using big data to improve their business, HR departments don’t have a big data problem. HR departments are actually dealing with a data quality problem and a data access problem.
In fact, if you try to solve your data issues from a “big data” angle, you’d be barking up the wrong tree and using the wrong tools for the job. HR doesn’t have to deal with big data. The reality is that only the largest companies in the world face the pain of parsing through information gathered from millions of employees every single day.
For most companies, the challenges faced by the HR department are simply having the ability to access and use the existing employee data efficiently.
The real data problem HR faces
The problem is that creating HR reports are often labor-intensive, time-consuming, and error-prone. There’s an easy solution.
Data collected by HR is often associated with different tasks, such as hiring, performance reviews, and payroll data, which comes in from various sources.
The data resides in different databases and more often than not, isn’t compatible with each other. As a result, rows and columns can’t be merged and data can’t be blended. A common example our customers deal with is aligning Employee Name columns so data can be properly joined. These never seem to align from system to system and it’s a common source of heartburn for HR pros (e.g. FirstName LastName; LastName, First Name; FirstName (one column); LastName (one column); Employee ID).
You’re stuck with comparing apples to oranges.
There’s no easy way to match up the data to answer even the most basic questions, such as what applicant attributes can best predict future performance.
HR doesn’t have a problem with collecting data. Instead, the issue lies in the ability to collate and combine data to generate actionable insights.
Unique challenges in HR reporting
Typically, HR analysts have to spend countless hours to manually pull data from multiple systems and organize it into spreadsheets.
Then they have to combine the different datasets with macros and VLOOKUPs through a repetitive process to deliver the same reports year-in-year-out.
With the many new regulations that have gone into effect recently, the process has become more complex and cumbersome.
Here are just a few examples:
EEO-1 reports: Detail tracking and reporting of employee hours are required when compiling these reports, which takes up even more time than before.
Affordable Care Act: ACA reporting requires compiling massive amount of data on worked hours, salary, and benefit cost to determine eligibility, affordability, and enrollment.
State and Federal leave policies: Maintaining compliance with federal leave policies, including FMLA, ADA, is a complex process involving the extraction and compilation of a lot of data. In addition, many states have their own laws with regards to paid leave, requiring even more reporting in order to stay compliant.
An easy solution to automate data extraction, cleansing, and blending
Here’s the good news: to make data work for HR, you don’t need to hire an army of data scientists.
Instead, you need a cost-effective way to clean up the data so it can be used for further analysis to generate real, impactful insights about your workforce.
HR professionals can now leverage self-service data preparation tools to simplify and automate these operational tasks.
Data from a variety of disparate sources, such as payroll, recruiting, or talent management systems, can be blended and parsed instantly into Excel spreadsheets for further analysis and reporting.
Procedures can be saved and workflows can be automated so you can eliminate the manual steps associated with these repeatable processes.
The Marbridge Foundation was able to save 2000-3500 man-hours in ACA reporting, thanks to Altair Monarch. The organization was able to extract and combine data to increase the efficiency and accuracy in reporting, reconciliation, and analysis of critical HR data.