HR is responsible for enabling people to perform to their fullest potential in the workforce. We’re responsible for processes, policies, talent development and acquiring the right people for the right job. While there are many ways to achieve these mandates, the growing benefit and use of analytics to assist HR professionals with making informed decisions in these areas is a trend that shouldn’t be ignored.
As our modern world overflows with data, it’s important to uncover the meaning behind that data (a more thorough understanding that can add reliability and remove bias from decision-making) rather than using redundant, bits of information randomly captured through virtual sources, including resume banks, social media, etc. The volume of data found through HR analytics contains valuable information that can prove beneficial to organizations if leveraged effectively.
The Power of Analytics and Disruptive Technologies
Today, organizational leaders seek to evolve their measurement strategies and appreciate the demonstration of potential business value that people can create through results with hard numbers that are quantitative. Although challenging, the human performance and learning data must be measured to quantify expected outcomes and benefits. This is where we require analytics to refine and analyze data to help draw correlations, provide predictive modeling, perform trend analysis, and inform leadership and talent programs with data-based evidence. Ultimately, this data is used to inform investment decisions that strengthen the organizational workforce and people’s capabilities.
"When trying to analyse people-based data there are a few big challenges: the sheer volume of data, the quality of the data (usually in multiple systems), and most importantly, finding people with the skills to synthesize the data and tell the story"
To make headway, it is imperative to begin with quality data. To do this, it is ideal to focus on a single source of “truth” or one system, rather than pulling disparate data from several sources. This helps to ensure the data is “clean” before using it for detailed analytical use or reporting—yet this is often not the reality for most companies.
From a human capital perspective, an emphasis on position management (role-based workforce data) and ensuring data integrity, as it pertains to an individual’s career information, is crucial. Ideally, the data that is put into an applicant tracking system should not be contained in that single system, it should follow an individual as they transition from applicant, to candidate, to employee, and subsequently should follow their performance and development during their tenure with the organization.
Using multiple elements to evaluate human data (your resume, but also your interests, your interactions, your development actions, your talent or performance information) amplifies the complexity in data synthesis. Having a single data-set not only enables analytics, but enhances an employee experience through consumer-like online transactions, by considering one’s interests, certifications, experience, competencies; and correlating the data for purposes of personalized and organizational development—the hope and promise of the intersection of future technology and data mining.
The challenge is the sheer volume of data, the quality of the data, and finding people with the skills to synthesize the data to tell the story. Storytelling expertise, analytical skills, and the ability to comprehend organizational effectiveness—the nexus of these three competencies is not easy to find, but is required to build an effective human capital analytics function.
Hiring the right person, with the required skill set for a specific role, can be a difficult task. We share professional data on social platforms, like LinkedIn, which can be used to improve the process of talent recruiting. I suspect this is where the implementation of machine learning and artificial intelligence (AI) can be valuable; for example, a recruiter posts a requisition for a new role and leverages AI or machine learning to source the professional data for a candidate generated on social media. They may find it easier and faster to identify the right possible candidates. At Western Union, we are still on this journey towards better—for the organization, as well as for the candidates and employees.
A resume is organized to focus on an individual’s credentials and certifications, whereas most organizations seek a more accurate holistic picture of the whole person including their individual attributes, their ability to function within certain contexts, as well as their capabilities and experiences. Disruptive technologies such as AI can provide algorithmic insights into a person’s life and actual interests and fit. It can also help HR professionals and hiring managers connect to a digital version of a candidate while keeping the human aspect of their persona intact.
Finding the Right Solution
As a decision maker, I seek a data solution that can easily integrate and has a user-friendly interface that is intuitive and straightforward. To me, “it’s not about quantity, it’s about quality.” We are constantly overwhelmed with choices; what we need is a solution to meet our needs instead of multiple, viable solutions and options. Often, the question that perplexes us is “does process drive technology or does technology drive process?”A leader must be pragmatic and maintain a fair balance between process and technology so that an organization can invest in developing them simultaneously rather than focusing on technology at the expense of process or vice-versa.
The use of quality data for HR analytics can lead to more data-driven and informed decision making when it comes to talent acquisition and leadership development. A focus on each of these areas can result in a workforce where your people are truly one of your greatest assets and ultimately a more strategic way to acquire and develop talent—resulting in a high performing organization that delivers valuable outcomes for the business.