Jan 01, 2015

Wisdom Comes From Putting Things Together

There is a well-known quote from John A. Morrison about the difference between obtaining knowledge and wisdom – “Knowledge comes by taking things apart: analysis. But wisdom comes by putting things together.” In the era of big data and predictive analytics, the HR organization is often lagging behind other areas such as finance, accounting, marketing, and supply chain operations. The ability to capture and analyze big data has enabled many enterprises to both increase revenues by better understanding and more accurately targeting customer needs and reducing costs through improved business processes.

Recently, big data has captured the attention of HR managers who are looking for ways to gain knowledge by analyzing mountains of structured and unstructured data, and combining these seemingly disparate facts to gain wisdom and answers to questions regarding workforce productivity, impact of training programs, predictors of workforce attrition, and succession planning.

What’s Getting in the Way?

Factors Impeding Efforts to Build an Analytical Organization

Factors Impeding Efforts to Build an Analytical Organization

Source: Talent Management Magazine (i4cp Study)

A 2013 survey by SHS revealed that 77 percent of HR professionals are unable to determine how their company’s workforce potential is affecting the bottom line, and less than half use objective data regarding worker performance to guide business decisions.

Resistance: There is some resistance to the application of big data utilization in human resources. While almost every business operation can be automated, the personal or “human” aspect is difficult to integrate into technology. Sophisticated intelligence solutions are able to predict a person’s next action, but are still unable to understand and predict human emotion. Additionally, a survey by the Chartered Institute of Personnel and Development (CIPD) found that HR professionals are often hesitant to engage with numbers or are passive towards them, and only half of senior HR leaders believe that they have the skills to link their data to key business and financial data.

Lack of Skills: A large majority of those employed in the HR function lack the data analytic skills or statistical background needed to sort through and use the large pool of available data. Historically, HR and analytics were considered separate subject areas, but leveraging knowledge from big data analysis requires both skillsets. A study from Bersin by Deloitte found that only 15 percent of organizations believed that their HR teams have “strong credibility” when it comes to using analytics, compared to 80 percent who believe their finance and operations teams do.

Analytical Ability by Job Function

Analytical Ability by Job Function

Source: AMA

Silos: Even when an HR organization seeks to develop a talent analytics perspective, they are often limited by difficulties in obtaining the systematic, reliable, and defined data that they need. In many companies, structural silos (structural barriers between HR functions and relevant people in performance and operations) are difficult to break down. For example, in many organizations, worker training or learning operates independently of the human resource function, but data from both areas are needed to gain insights into workforce performance and talent gaps.

Talent Analytics

Companies have volumes of employee, HR, and performance data, including demographic information, performance reviews, educational history, job locations, and other factors relevant to workers. Talent analytics allow managers to use this data scientifically to make informed decisions about workforces and plan for the future, so as to anticipate leadership and talent gaps and to develop candidate pipelines.

In the area of talent acquisition, recruiters can use predictive analysis to screen job candidates’ potential to become good employees. Talent analytics can also be used to cross-analyze a candidate’s resume with personal information available on social networks, giving employers a tool for understanding a candidate’s interests and personality.

Big Data Evolution Model

Bersin by Deloitte’s research into the use and capability of big data analysis in HR organizations resulted in the development of the “HR Analytics Maturity Model.” The research showed that organizations go through four stages of evolution in the process of utilizing big data in HR decision making: 1) reactive 2) proactive 3) strategic 4) predictive

HR Analytics Maturity Model

HR Analytics Maturity Model

Source: Bersin by Deloitte

How to Get Ready?

For companies which are seeking to apply big data mining and predictive analysis to their HR function, the transition can be challenging.  To get ready, companies should look to do the following:

  1. Determine goals – is this a one-time project or an ongoing activity?
  2. Build a solid HR analytics team, which includes resources from outside of the HR organization.
  3. Establish a single source of performance data
  4. Invest in HR technology and training in visualization and project management
“By tracking competencies associated with new recruits – as well as their early performance and length of service – HR teams can build ideal hiring profiles, predict hiring quality and then actively recruit for long-term, high-value talent. Valuable, historic data already exists on HR information systems. But most organizations fail to use this data to address challenges in recruiting practices.” ~Sayed Sadjady, Principal at PwC