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1) Data comes from many different sources.

Of the respondents to the Oracle HR Analytics 2021 Report, almost half find data integration fairly or very difficult. The two most difficult areas of HR/people analytics are data integration and data clean up.

Many HR professionals collect data from various applicant tracking, learning and development, and performance management systems. More than one-third of respondents pull data from four or more systems, 9% use data from eight or more systems and 4% rely on 12 or more systems. 

Encouragingly, only 20% find data security fairly or very difficult, presumably because the organization has invested the needed resources in cybersecurity.

The value of HR data can be maximized by combining it with non-HR data. For example, employee engagement data on its own is somewhat interesting but employee engagement’s effect on sales or productivity is very interesting. 

A minority of organizations (21%) have developed the skills to integrate non-HR with HR data often or always. However, nearly half of the organizations have barely started on this sort of integration, with 47% saying they seldom or never integrate non-HR and HR data.

 

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2) Compensation is a key driver of HR analytics. 

It is always best to have insights from a dedicated HR analytics professional, who can run the numbers and get the data that you need. However, if you do not have one on staff, ask some of your compensation pros to be a part of the process.

Compensation consultants are professionals whose area of expertise is compensation plan implementation and design, with the goal of creating the proper incentives for employee behavior. Compensation professionals are typically well-versed in analytics thanks to their experience crunching numbers.

[QuoteText]"Compensation experts increasingly need to be working in tandem with other HR experts who can connect the dots with other key areas of the business, such as talent acquisition, performance management, learning and development, and diversity, equity and inclusion."

– The State of HR Analytics 2021[/QuoteText]

3) Analytics is descriptive, rather than prescriptive or predictive. 

More than two-thirds of organizations practice descriptive analytics, using talent-related data that describes what has happened in the past.

 

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On the other hand, prescriptive analytics allow leaders to gain useful insights, while predictive analytics uses “tools such as statistical models and machine learning to make predictions about the future.”

Currently, 43% of respondents make at least moderate use of prescriptive analytics, while 34% make at least moderate use of predictive analytics. “Only 15% of organizations make high or very high use of predictive analytics,” Oracle researchers noted.

 

4) Most organizations face hurdles when it comes to people analytics.

When it comes to HR professionals' abilities to make decisions based on HR analytics data, most see their efforts as lacking. 

[QuoteText]"Only 35% of the respondents reported being able to make positive changes at their organizations based on HR-related data. And just 29% rated their efforts as good or very good."

– The State of HR Analytics 2021[/QuoteText]

This leaves roughly a third who are poor or very poor at making positive changes based on people analytics,” according to the report. Only 36% of respondents agreed or strongly agreed that their people analytics platform delivers actionable insights.

 

5) Clean data is hard to come by

Among the most challenging elements of people analytics are data integration, cleaning and visualization, according to the report. Nearly half rate their data integration as “fairly difficult” or “very difficult,” while 42% said the same about data clean-up and 38% about analytics visualization.

 

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Conclusion

Is HR data a type of big data? The term big data typically refers to data sets so large and complex that they are difficult to process using traditional statistical methods. To answer that question, Oracle asked respondents about five characteristics typically associated with big data: value, validity, volume, velocity and variety. They discovered that more than half of respondents believe that the HR analytics data they use, do not reflect these characteristics to a high or very-high degree.

Therefore, they concluded HR data—at least these days—must be cleansed and refined to yield meaningful insights. Good HR data analysts do not typically need to be “data miners” or “data scientists,” but they do need to have a clear understanding of how to see patterns and draw insights from the data.

Although a minority of organizations, many companies are skilled and successful at implementing HR analytics. If you plan carefully and use the available resources, your organization can be successful as well.

 

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