Understanding Data and Basic Analytic Tools: Know Your Data, A Pragmatic View of Data

Understanding data and basic analytic tools are important for HR professionals and decision makers who want to make informed decisions based on the results of people analytics.

Data refers to information collected for a specific purpose, such as employee performance data, demographic data, and engagement survey data. Understanding data involves understanding the types of data available, the sources of data, the methods used to collect data, and the limitations of the data.

Basic analytic tools refer to a set of tools and techniques used to analyze data, including:

1. Descriptive Statistics: This involves summarizing the data using measures such as mean, median, mode, and standard deviation.
2. Inferential Statistics: This involves making generalizations about a population based on a sample of data, using techniques such as hypothesis testing and regression analysis.
3. Data Visualization: This involves using visual aids such as charts and graphs to represent data, making it easier to understand and interpret the data.
4. Predictive Analytics: This involves using statistical models to make predictions about future events, such as employee turnover or job performance.

By understanding data and basic analytic tools, HR professionals and decision makers can effectively analyze and interpret the results of people analytics, and make informed decisions based on the insights generated by the data. Additionally, they can effectively communicate the results of the analysis to stakeholders, encouraging them to take action based on the insights and implications of the data.

Know Your Data

“Know Your Data” is a critical aspect of people analytics, as it helps organizations to effectively analyze, interpret, and make decisions based on the results of people analytics. Here are some key components of knowing your data:

1. Data Sources: Identifying the sources of data, including internal sources such as employee performance data, and external sources such as market data, is important for understanding the limitations and strengths of the data.
2. Data Quality: Ensuring the quality of the data, including the accuracy and completeness of the data, is critical for generating reliable and meaningful insights from the data.
3. Data Limitations: Understanding the limitations of the data, including the representativeness of the sample, the reliability of the data, and any biases in the data, is important for interpreting the results of the analysis accurately.
4. Data Integration: Integrating data from multiple sources, including internal and external data sources, is important for generating a more comprehensive view of the data and for generating insights that can inform business decisions.
5. Data Governance: Establishing clear policies and procedures for managing the data, including data privacy and security, is critical for ensuring the accuracy and completeness of the data, and for promoting trust in the results of the analysis.

Knowing your data is important for several reasons:

1. Reliable Insights: Knowing your data helps to ensure the accuracy and reliability of the insights generated from the data. This is critical for making informed decisions based on the data, and for generating business value from people analytics.
2. Data–Driven Decision Making: Understanding the strengths and limitations of the data enables organizations to make informed decisions based on the data, and to integrate data-driven insights into their decision-making process.
3. Stakeholder Buy–In: When organizations understand their data and can effectively communicate the insights generated from the data to stakeholders, they are more likely to gain buy-in from stakeholders and to encourage action based on the insights and implications of the data.
4. Improved Data Quality: Knowing your data helps organizations to identify any issues with data quality, including accuracy and completeness, and to address these issues in order to improve the quality of the data and the insights generated from the data.
5. Better Resource Allocation: By knowing their data and understanding the strengths and limitations of the data, organizations can allocate resources more effectively, and focus their efforts on the areas where they are likely to generate the most impact from their people analytics initiatives.

A Pragmatic View of Data

A pragmatic view of data involves approaching data and analysis from a practical, results-oriented perspective. This involves focusing on the insights and outcomes that can be generated from the data, rather than on the technical aspects of data analysis.

By adopting a pragmatic view of data, organizations can effectively analyze and interpret the results of people analytics, and make informed decisions based on the insights generated by the data. Additionally, they can effectively communicate the results of the analysis to stakeholders, encouraging them to take action based on the insights and implications of the data.

A Pragmatic view of data requires a focus on the following key components:

1. Business Objectives: Aligning data analysis with business objectives, such as improving employee engagement or reducing turnover, is critical for generating insights that can inform business decisions.
2. Data Relevance: Ensuring that the data being analyzed is relevant to the business objectives, and that it can provide valuable insights and outcomes, is critical for generating meaningful results from the data.
3. Data Quality: Ensuring the quality of the data, including the accuracy and completeness of the data, is critical for generating reliable and meaningful insights from the data.
4. Data Interpretation: Interpreting the results of the analysis in a way that is meaningful to stakeholders, and that can inform business decisions, is critical for generating outcomes from the data that can drive business value.
5. Data-Driven Decision Making: Making decisions based on the insights generated from the data, and integrating data-driven insights into the decision-making process, is critical for generating business value from people analytics.

Adopting a pragmatic view of data is important for several reasons:

1. Improved Business Outcomes: By focusing on the insights and outcomes that can be generated from the data, and aligning data analysis with business objectives, organizations can generate valuable insights that can inform business decisions and drive business value.
2. Increased Stakeholder Buy–In: A pragmatic approach to data can help organizations to effectively communicate the results of the analysis to stakeholders, encouraging them to take action based on the insights and implications of the data, and to gain buy-in from stakeholders for their people analytics initiatives.
3. Better Data Quality: By focusing on the relevance and quality of the data, organizations can ensure that the data being analyzed is accurate and reliable, and that the insights generated from the data can be trusted.
4. Data-Driven Decision Making: By making decisions based on the insights generated from the data, and integrating data-driven insights into the decision-making process, organizations can effectively leverage people analytics to drive business value.
5. Improved Data Interpretation: A pragmatic approach to data enables organizations to interpret the results of the analysis in a way that is meaningful to stakeholders, and that can inform business decisions, improving the impact and outcomes of their people analytics initiatives.