Talent Management

Re-imagine the Talent Process by Utilizing Data Analytics

One-size-fits-all talent management processes no longer work in a global setting because people are not-one-size-fits-all. Human Resources analytics enable organisations to make better decisions across functions based on evidence.
— By Donna Chan

Evidence-based talent management requires hard data and analytics that create a link between fact and fiction. Fact in a global organization recognizes the enormous differences in people across countries and cultures. Fiction assumes all job candidates and employees have the same perspectives, experiences and goals. Human Resources professionals understood this many years ago but missing was the evidence.

The proof that global groups of people are motivated by different drivers is found in workforce analytics. Companies, like the Royal Bank of Canada (RBC) and other organisations, are using the data analytics to develop evidence-based talent management tools that enable them to make better people decisions across functions.

Making Strategic Decisions Based on Evidence and Data Analytics
Successful talent strategy needs to be based on facts. It has always been true, but the lack of data and analytics in the past made collecting and documenting the evidence a labourious process. Another barrier has been a lack of understanding that a straight across transfer of talent management processes does not work.

Attracting, engaging and managing people in Canada requires different approaches for people in China, Europe, Latin America, Africa and Asia. It seems so obvious, yet businesses must find a way to manage a diverse talent process, and the simplest way seemed to be applying the same decision-making process across the organization.

Technology has fundamentally changed that process from one in which talent decisions are based on uniform past practices, gut reactions and internal politics to one that is based on a body of evidence driven by Human Resources (HR) analytics. Evidence-based HR management is a decision-making process that combines HR analytics, business information, and the decision-maker's knowledge and experience. It is a process that is meant to eliminate auto-pilot and potentially faulty decisions that managers make as they try to cope with the complex globalization of the talent process.

Adding Structure and Science to People's Decision-Making
Evidence-based HR adds structure and critical thinking to decision-making. The process involves turning an issue into a question, searching for evidence, critically assessing the evidence as to relevance and accuracy, aggregating the evidence, incorporating the evidence into decision-making, and evaluating outcomes.

Compare this process to, "We did it this way because it worked in the past" or "I like the guy, so I want to hire him. He reminds me of myself when I was his age."

HR analytics add science to the process, removing much of the bias and reliance on the way things have "always been done." Analytics are used to add structure to hiring decisions, determine the value of employee rewards, assess talent for promotion or leadership development, analyse performance and organizational culture, identify critical roles, and assign jobs. They are used to assess talent management programs and strategies, to determine their effectiveness and to benchmark against leading companies. Scorecards and dashboards drive the information contained in HR analytics to decision-makers at all levels of the organization, improving HR synergy among organizational units and optimizing the investment of resources in the talent management process.

Data is collected from multiple sources. They include employee performance assessments, employee surveys, health and wellness programs, exit interviews, employee events, talent recruitment and hiring results, and numerous other sources. Quality analytics convert the data into actionable information. Predictive analytics and prescriptive analytics are proving to be critical tools for HR management.

Predictive analytics are used to forecast and to model potential results in the future. Prescriptive analytics are the highest level of analytics because they use machine learning to interpret data and make recommendations.

Establishing Links Between People and Business Activities
For example, employee performance can be predicted by analyzing an employee's drive to improve his/her skills and competencies, how well the person uses events to display or utilize skills, cognitive ability, and prior experience.

Deloitte's research has shown that insights on global mobility initiatives' effectiveness and strategic alignment are available in analytics which can also contribute to career planning and opportunities. Analytics are used for workforce planning by analyzing how people come and go in the organization, and events and management practices that impact people coming and going. Organisations can forecast supply and demand of key occupations, and use predictive analytics to make performance predictions.

HR analytics are not simply about making better hires or improving retention. At IBM, the analytics are used to determine links between employee performance, customers, and revenue.

RBC is also on an analytics journey, discovering a couple of years ago there is a connection between the degree to which people believe in the company's competitiveness and value proposition, and the retail branch's performance. The more employees believed in the company, the better the branch performed. The analytics helped RBC determine the best actions to take to improve their communication of the client value proposition to employees.

The Royal Bank of Canada is a leader in the use of HR analytics, also called people analytics, but across Canada there are other large companies implementing people analytics. They include TELUS, TD Bank, Canadian Tire, and Business Development Bank of Canada (devoted exclusively to entrepreneurs).

Utilizing People Analytics Within for All Organisations
Properly developing and producing complex HR analytics requires people with the appropriate high-level of skills in data collection, assessment, and mapping across multiple systems, and in validating metrics.

Once available, the organisation also needs HR professionals who have the required skills to analyse the results for effective decision-making. No matter how many metrics are produced, someone will still have to make final decisions.

Also, people analytics are not reserved for only large, global organisations. The Toronto Region Board of Trade has approximately 45 employees and relies on digital analytics for marketing, communication, economic and labour analysis, and HR management. In an unusual structure, the HR function reports to the head of finance, so the strategic activities are integrated with business planning and outcomes.

Expect to read more in the coming months and years about the use of HR analytics as their sophistication and value are recognized. They are the evidence-based link between people and organisational success.