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10 Must-Know Leadership Analytics Approaches

By Sarah Mogan and Stephanie Neal

Analytics has become a popular buzz word, but accompanying the popularity of the term is the belief that analytics often seems too complex or overwhelming to implement in human resource efforts.

Referring to the branch of analytics applied to HR data as “people” analytics reminds us that people are behind the numbers; that each number represents human talent and the experiences and motivations of actual people. As applied to your leaders, people analytics is about understanding the performance and perceptions of those who lead others and whose impact is multiplied across their teams. Capturing the human aspect within your data allows you to move conversations from talking solely about graphs and tables to discussing better outcomes for your leaders and employees—how to boost engagement, increase satisfaction, and ultimately, inform better decision-making in your organization.

In our experience working with organizations to design and implement analytics studies, we’ve identified a set of go-to methods that are the backbone of any good people analytics effort. These 10 approaches can help you make the most of your people data. For each, we’ve also included examples of the kinds of leadership and business questions they can help to answer.


1. Descriptive analytics

How can you summarize data to identify meaningful patterns?

Descriptive analytics uses simple techniques to reveal aggregate results and meaningful patterns using strong data visualizations (i.e., dashboards, infographics).


2. Benchmarking

How do your leaders and talent practices stack up?

By comparing results internally and/or externally (i.e., industry, region, best-in-class organizations), benchmarking can highlight strengths to reinforce, identify weaknesses to develop, and indicate how outcomes compare to best practices.

Cohort Analysis

3. Group/cohort analysis

What do differences between groups of stakeholders mean for business operation and strategy?

By studying group-level data, cohort analytics compare one group against another (e.g., leadership level, function, location, high-potential status, etc.) allowing for greater insights into your employees or customers.

Key Driver Analysis

4. Key driver analysis

What are the most important factors for improving leadership capability?

By studying relationships among business metrics and behavioral variables, key driver analyses (including regression and causal modeling) reveal the most critical characteristics within an organization to help outline priorities and reveal gaps, thereby helping to better optimize business processes and improve performance.


5. Survival analysis

Which employee groups are more likely to stay or get promoted over time?

By examining organizational movement (such as promotion or turnover), survival analyses help organizations understand how their talent advances and departs, and identify top talent and potential flight risks.

Text analytics

6. Text analytics

How can you visualize the sentiments of employees or leaders?

By analyzing unstructured data (e.g. emails, written comments), text analytics can identify patterns, assess sentiment, and predict trends in what stakeholders are saying. Analyzing text along with numerical data allows you to extract relevant information and transform it into useful business intelligence.

Decision tree

7. Decision tree

Which talent practices are most likely to result in a desired outcome?

By mapping a series of actions and outcomes, decision trees use machine learning to investigate ideal and alternative paths along with their results and consequences to better select a course of action.

Scenario planning and optimization

8. Scenario planning and optimization

How can you determine the best course of action to meet your leadership and business goals?

By analyzing a variety of hypothetical workforce events, scenario analysis (also known as “what if” analysis) models possible outcomes (from maintaining status quo to following a theoretically optimized path) to indicate payoffs and consequences of different courses of action.

Time-series analysis

9. Time-series analysis

How can archival data help predict the future?

Time series analysis can help track changes over time to extract meaningful statistics, relationships, and data characteristics, using past events and ratings to predict future events and trends.

Human capital investment/ROI

10. Human capital investment/ROI

How do you measure the tangible value of your investment?

By connecting human capital and monetary gains, calculating return on investment indicates the economic value of leadership investments.

We will be leveraging many of these approaches in our own research as we dig into and explore trends across the responses provided by 10,000+ leaders for the new Global Leadership Forecast, a partnership between DDI, The Conference Board, and EY. There is still time for your HR Team and Leaders to participate in this unparalleled leadership study, to benefit with your own benchmarking report and to gain early access to insights from the research.

Sarah MoganSarah Mogan is a consultant in the Center for Analytics and Behavioral Research (CABER). She works with client-centered measurement studies, often longitudinal, to study selection methodologies’ impact on business metrics, and employees’ performance, engagement, and satisfaction. When Sarah isn’t at work “playing with numbers” she is chasing tennis balls on the court or at the dog park because Marty hasn’t gotten the hang of fetch yet—but he’s still a very good boy!

Stephanie Neal, Ph.D.Stephanie Neal is a research consultant for the Center for Analytics and Behavioral Research (CABER). She conducts evaluation studies and research on leadership and talent in the workplace, and is a co-author of DDI's Global Leadership Forecast. Outside of work, Stephanie relishes every opportunity to develop her mini-golf game and to help her son with his extra-credit math problems.

Posted: 05 May, 2017,
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