Learn about the most effective data analysis techniques for performance management, and how they can help you measure, evaluate, and improve your employees' performance and development.

Ideally we always want all data in numbers or being quantified. But if we talk about performance management, most of the data are still in qualitative. It can be in the form of employee feedback, their feeling and experience, their random comments, etc. Yes that can all be quantified, but capturing every single data in quantitative will need extra effort from employee themselves, as they need to fill too many datapoints. So we can simplified it by quantifying the qualitative by ourself: we can talk with employee or ask their feedback in a free text, then we map and summarize each keyword. The keyword that appeared the most will be on higher priority, and vice versa.

Good output (analysis/insight) only comes from good input (data). Before moving into any analysis method, it's vital to ensure that we have solid and workable data to begin with: - Set up a well-organized data bank, and start collecting the data early. Ideally, people performance data exist from the organization's day-1. This data bank serves as a historical record to organize people's performance data, with a focus on long-term use. - Data governance. Establish clear policies and flow around the data, e.g., who is responsible for managing the data, how data accuracy is validated, etc. - Build a query-friendly data structure. This allows the data to be used for more intricate analysis when needed.

What are the most effective data analysis techniques for PM?

Data analysis is a crucial skill for performance management (PM), as it helps you measure, evaluate, and improve your employees' performance and development. But with so much data available, how do you choose the most effective techniques to analyze it and draw meaningful insights? In this article, we will explore some of the most common and useful data analysis techniques for PM, and how they can help you achieve your PM goals.

1 Descriptive analysis

Descriptive analysis is the simplest and most basic form of data analysis, as it summarizes and presents your data in a clear and understandable way. It can help you answer questions such as: What is the current state of performance? How many employees are meeting their targets? What are the trends and patterns in the data? Descriptive analysis can use techniques such as tables, charts, graphs, and dashboards to display your data visually and highlight key information. Descriptive analysis is useful for PM because it can help you monitor performance, identify strengths and weaknesses, and communicate results.

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Onkar Kulkarni

"Empowering HR through Data: HR Specialist, Business Partner, and Analyst dedicated to leveraging insights for strategic decisions and business growth"
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KPIs: Nail strategic goals. Benchmarking: Stay ahead in your game. Balanced Scorecard: Track across the board. Predictive Analytics: Future-proof decisions. Data Dashboards: Visualize performance metrics for quick insights. See success at a glance.

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2 Diagnostic analysis

Diagnostic analysis goes beyond descriptive analysis, as it tries to explain why something happened or what caused a certain outcome. It can help you answer questions such as: Why did performance improve or decline? What factors influenced performance? How did different variables affect performance? Diagnostic analysis can use techniques such as correlation, regression, and root cause analysis to explore the relationships and causes behind your data. Diagnostic analysis is useful for PM because it can help you understand performance, find out the reasons for success or failure, and address performance issues.

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3 Predictive analysis

Predictive analysis is a more advanced and complex form of data analysis, as it uses statistical models and algorithms to forecast future outcomes based on past and present data. It can help you answer questions such as: What will happen if performance continues as it is? How likely is performance to improve or decline? What are the best scenarios and worst cases for performance? Predictive analysis can use techniques such as trend analysis, scenario analysis, and machine learning to estimate and project your data. Predictive analysis is useful for PM because it can help you anticipate performance, plan ahead, and prepare for contingencies.

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4 Prescriptive analysis

Prescriptive analysis is the most sophisticated and actionable form of data analysis, as it uses optimization and simulation techniques to recommend the best course of action based on your data and goals. It can help you answer questions such as: What should we do to improve performance? How can we achieve our performance targets? What are the trade-offs and risks of different actions? Prescriptive analysis can use techniques such as decision analysis, cost-benefit analysis, and sensitivity analysis to evaluate and compare your options. Prescriptive analysis is useful for PM because it can help you optimize performance, make informed decisions, and achieve desired outcomes.

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5 Qualitative analysis

Qualitative analysis is a different type of data analysis, as it focuses on non-numerical data such as words, images, and emotions. It can help you answer questions such as: How do employees feel about their performance? What are their motivations and challenges? How do they perceive their feedback and development? Qualitative analysis can use techniques such as interviews, surveys, focus groups, and thematic analysis to collect and interpret your data. Qualitative analysis is useful for PM because it can help you understand performance from the employees' perspective, capture their feedback and opinions, and enhance their engagement and satisfaction.

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Rendhy A.

Strategic Management & Human Capital Practitioner
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Ideally we always want all data in numbers or being quantified. But if we talk about performance management, most of the data are still in qualitative. It can be in the form of employee feedback, their feeling and experience, their random comments, etc. Yes that can all be quantified, but capturing every single data in quantitative will need extra effort from employee themselves, as they need to fill too many datapoints. So we can simplified it by quantifying the qualitative by ourself: we can talk with employee or ask their feedback in a free text, then we map and summarize each keyword. The keyword that appeared the most will be on higher priority, and vice versa.

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6 Mixed methods analysis

Mixed methods analysis is a combination of quantitative and qualitative data analysis, as it uses both numerical and non-numerical data to answer your research questions. It can help you answer questions such as: How do quantitative and qualitative data complement or contradict each other? How can we integrate and synthesize different types of data? How can we triangulate and validate our findings? Mixed methods analysis can use techniques such as convergent, explanatory, or exploratory design to combine and compare your data. Mixed methods analysis is useful for PM because it can help you gain a more comprehensive and holistic view of performance, address the limitations of single methods, and increase the credibility and reliability of your results.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Rahmandika Herputranto

Organizational Development | Performance Management
(edited)
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Good output (analysis/insight) only comes from good input (data). Before moving into any analysis method, it's vital to ensure that we have solid and workable data to begin with: - Set up a well-organized data bank, and start collecting the data early. Ideally, people performance data exist from the organization's day-1. This data bank serves as a historical record to organize people's performance data, with a focus on long-term use. - Data governance. Establish clear policies and flow around the data, e.g., who is responsible for managing the data, how data accuracy is validated, etc. - Build a query-friendly data structure. This allows the data to be used for more intricate analysis when needed.

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What are the most effective data analysis techniques for PM?

1

2

3

4

5

6

7

1 Descriptive analysis

2 Diagnostic analysis

3 Predictive analysis

4 Prescriptive analysis

5 Qualitative analysis

6 Mixed methods analysis

7 Here’s what else to consider

Performance Management

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What are the most effective data analysis techniques for PM?

1

2

3

4

5

6

7

1 Descriptive analysis

2 Diagnostic analysis

3 Predictive analysis

4 Prescriptive analysis

5 Qualitative analysis

6 Mixed methods analysis

7 Here’s what else to consider

Performance Management

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