HR Analytics 101

BIG DATA, PREDICTIVE ANALYTICS, AND THE IMPACT ON HR

Making It Simple: Big Data and Predictive Analytics in HR
Big Data
Four Levels of HR Analytics in Organizations
Predictive Analytics
Don't Be Fooled: The Predictive or Not Test
Predictive Analytics Done Right

Artificial Intelligence and Machine Learning
Analysis Paralysis No Longer
Hiring and Retention Possibilities
Pitfalls
The Human Element
How HR Makes an Impact with Predictive Analytics
Obstacles to Smarter Analytics
Key Takeaways from Chapter 1

Research cited by Forbes estimates that more than half of large companies (60 percent of those sampled) are investing in big data and predictive analytic tools to guide human resources (HR) decisions. Because of this surge in popularity along with pressure to keep ahead of the competition, authors and commentators describe the HR function as currently in a state of transition, moving from concentrating on meeting internal metrics (e.g., number hired, turnover number) to identifying the links between metrics (e.g., hiring the right people to decrease turnover). In this way, HR leaders are optimizing HR processes and decisions to improve not only the employee experience, but also the business. There are two keys to enabling HR leaders to understand these links: big data and predictive analytics. Unfortunately, there are also numerous faux-scientific processes (e.g., data visualization) that purport to draw these links but do nothing of the sort. The other faux-scientific areas that should not be relied upon are thought-leader clichés and assumptions (e.g., employee engagement always drives business outcomes), and the emerging fields of artificial intelligence and machine learning, which are also not well understood and pose substantial risks when misused or misinterpreted (e.g., misidentifying employees as retention risks). We will delve into all of these in this chapter.

In terms of analytics, this new focus on linking people variables together presents an interesting opportunity for HR with a great deal of upside for HR practitioners. These upsides include

• A greater understanding of the employee knowledge, skills, and abilities that drive business outcomes specific to your organization;

• The ability to make people investments that truly deliver results;

• A way to calculate the return on investment (ROI) of investing in your people; and

• The opportunity to take the lead in making the HR process business focused, thus making HR a strategic business partner for the core business.

Data alone are not all that interesting; it is when you combine data and analysis that you make better talent decisions. A great example is an organization that is looking to reduce turnover. The leading assumption at the organization is that people are leaving due to treatment by their immediate supervisors. The classic thought-leader cliché is "people don't leave their company, they leave their boss." Analytics can be used to test this assumption and determine the true cause of high turnover — whether it be the immediate supervisors or something else entirely.

OK, so what exactly is HR analytics? Simply, HR analytics is the analysis of people data. The goal of any people-analytics project is to gather and understand the connections between people data from multiple sources, as well as other hard data (e.g., performance, financial, and business metrics) to inform organizational and HR changes that support the leadership's vision and company initiatives. Many times the analysis requires multiple data sources, involving the actual collection of data (e.g., distribution of a survey) along with previously collected data (e.g., attrition data accumulated over the last year or selection data of all successful job applicants). The implications of HR analytics — across HR as well as the organization — can be far-reaching and can include projects like the following:

• The development of predictive talent profiles to aid in succession planning and inform the selection and development of employees.

• Survey development and the assessment of employee attitudes on multiple outcomes (e.g., performance, turnover, customer/patient satisfaction) across the lifecycle of employee tenure.

• The utilization of targeted organizational assessments in times of organizational change (e.g., change readiness, climate assessment, wellness assessment).

• The prioritization of survey categories or behavioral competencies based on their impact on business outcomes.

MAKING IT SIMPLE: BIG DATA AND PREDICTIVE ANALYTICS IN HR

Big Data

Although often associated with complex analysis, big data is actually a simple concept: it is the collection and accumulation of numerous pieces of information from multiple sources. Because big data is about gathering and connecting data that may not have previously been considered in concert, it allows for the use of data in new ways to uncover connections between previously disparate concepts. For HR, these concepts may include employee behaviors, attitudes, skill or knowledge levels, performance metrics, turnover data, and much more.

The ways the data accumulate can range from manual (such as the deployment of a selection procedure) to entirely automatic (such as machine scanning for résumé selection). However, even manual systems are less manual today, as they are inevitably aided by technological advancements; for example, a job knowledge assessment may be taken, scored, and stored electronically. Although the concept of big data is not new, the surge in the collection, administration, and accumulation of data has grown exponentially with technological advances. The concept of big data sets the groundwork for the next part of the conversation — what do you do with all the information you are now able to collect? This is where HR analytics comes into play. You may be wondering what analytics means within your organization. The truth is that we see organizations at various stages of sophistication with analytics. Each phase, or level,...