Every company is a data company. Whatever market or industry you’re competing in, the success or failure of a business depends on how you apply data from all areas of the organization. Human resources is no exception, but data from this function presents unique challenges. 

According to Oracle’s State of HR Analytics 2021 report, more than a third of respondents pull data from more than three systems to create people analytics reports. A small percentage pulls data from 12 or more systems. In fact, almost half of the respondents to Oracle’s report cited data integration as one of the most difficult parts of people analytics.

Consider all the different kinds of human resources information systems (HRIS) organizations might use:

• Recruitment and applicant tracking systems (ATS)
  
• Workforce management
  
• Performance management
  
• Compensation management
  
• Benefits
  
• Time tracking
  
• Employee engagement
  
• 360 feedback tools
  
• Employee recognition
  
• Career management
  
• Training and learning management systems (LMS)
  
• Skills management
  
• Offboarding
  

And that’s just a partial list. Each of these areas can be running multiple software applications, and each has its own data structures with which to maintain employees’ and candidates’ information. This disparate data gets presented on separate dashboards and data analytics reports with no way to see correlations that involve data from multiple applications. 

Why so many HR systems? 

This proliferation of HR data from multiple systems increases as a business grows. A company might start out tracking HR data in spreadsheets — one sheet for compensation data, one for attrition, one for diversity, for example — but that approach doesn’t scale. Growing companies need tools that are purpose-built to meet specific needs. With cloud-based SaaS, such tools are relatively inexpensive. But even though some tools and systems might overlap in capabilities, their data lives in individual silos, unavailable to other systems no matter how closely related they may be.

The only way to get a fully rounded picture of certain issues is by seeing data from multiple systems, but bringing it together is challenging. 

When it comes to personal information, confidentiality considerations come into play. Details about people’s age, race and gender should be protected. Both privacy best practices and compliance regulations call for limiting access to some tables and even some fields to individuals who have the proper privileges.

That can mean using techniques like data masking or data obfuscation for certain columns. Or, in some cases, an organization’s HR analytics is kept in a separate data warehouse project, cluster or domain from other reporting data, to keep personally identifiable information (PII) confidential.

For HR analytics, organizations should have a documented policy on who can have access to what data, and a process for reviewing and granting access to systems. They need to designate a data protection officer, who has responsibility for privacy issues, and consult with legal experts to be sure they’re complying with all necessary laws. That helps data analysts answer the executives in the C-suite who ask, “Why don’t I have access to this data?” 

Another challenge for some organizations is that they’re still using the same on-premises systems they’ve used for 20 years for tasks such as payroll and accounting. Getting data from a data source into a data warehouse requires a data pipeline, and not many ETL tools provide connectors for replicating data from these ancient systems to modern cloud data warehouses. 

Companies with legacy systems can often benefit by migrating away from older platforms and to cloud-based SaaS applications. They’ll save on server hardware costs and the IT staff to maintain it and instead pay only a monthly fee out of their operating budget. The ability to get better analytics is icing on the cake.

How to get started with HR analytics 

Better HR analytics bring benefits to every business. When data is available to HR managers and executive leadership, they have a truer picture of their organization and can make better decisions. By consolidating data from multiple sources, they can build a more unified view of employee satisfaction, candidate pipelines and compensation strategies.

If your organization isn’t already creating business intelligence based on HR data, the sooner you start, the more value you’ll get. The first step: Start building a data integration pipeline that can pull data from all your HR systems.

Download our guide on how to get started with data integration, so you take the first step to improving the outlook for your employees and your business.