Summary of Lecture 02: HR Data Preparation & Development of HR metrics

Summary of Lecture 02: HR Data Preparation & Development of HR Metrics

This lecture focuses on the preparation of HR data and the development of HR Metrics essential for effective HR Analytics. The key areas of discussion include the importance of data preparation, the types of questions to ask regarding Data Reliability, and the methodologies for measuring and presenting HR data.

Main Ideas and Concepts

• Importance of Data Preparation:
  • Data must be collected and transformed before analysis.
  • The preparation process ensures data accuracy, reliability, and applicability.
• Questions to Assess Data:
  • Source of Data: Identify where the data comes from (internal vs. external) to ensure reliability.
  • Sample Representation: Ensure the sample accurately reflects the population to make valid predictions.
  • Outliers: Determine if outliers exist and understand their impact on results.
  • Assumptions: Clarify the assumptions behind Data Analysis and the chosen analytical approach.
• Data Measurement:
  • Decide how to measure data: absolute numbers, ratios, percentages, percentiles, or correlations.
  • Presenting data in various formats enhances understanding and analysis.
• Criteria for Good HR Metrics:
  • Metrics should be documented and include clear definitions and formulas.
  • Each metric should indicate the official source of raw data.
  • Metrics must provide information on what is being calculated and why.
• Developing HR Metrics:
  • Identify problems within HR functions (e.g., recruitment, performance, compensation) to focus Data Collection efforts.
  • Categorize metrics according to HR functions to streamline analysis and decision-making.
• Documentation and Clarity:
  • Each metric should be documented, including its purpose, formula, and source, to ensure clarity and usability.

Methodology for Data Preparation and Metric Development

• Data Collection:
  • Assess the reliability and validity of data sources before collecting data.
  • Collect data relevant to specific problems faced by the organization.
• Data Analysis Preparation:
  • Ask critical questions about the data source, sample representation, outliers, and assumptions.
  • Ensure data is complete, accurate, and applicable.
• Metric Development:
  • Identify challenges in HR functions and list variables that can be measured.
  • Create metrics that are categorized by HR functions and document their definitions and formulas.

Speakers or Sources Featured

The lecture appears to be presented by a single speaker, likely an instructor or expert in HR Analytics, though their name is not mentioned in the subtitles. The content is educational and aimed at HR professionals or students learning about HR Analytics.

Notable Quotes

— 02:47 — « No matter how much you trust your Quant or your data, do not stop asking them tough questions. »

— 03:18 — « It does not matter how much you trust your number; if you have a 100% trust also, then also you should ask certain questions. »

— 06:10 — « If your sample characteristics and population characteristics are not matching, then you cannot make the prediction about the population characteristics. »

— 09:00 — « If you include the outlier, you will not understand how it impacts the result; then your analysis may be misleading. »

— 24:06 — « Don't waste your time on those issues for which there is no problem in the organization. »

Category

Educational

Video