C2-M3 Structured Thinking and Contextualizing Data

Structured Thinking in Business Problem Solving

  • Definition of Structured Thinking

    • Process of recognizing current problems, organizing information, identifying gaps, and revealing opportunities.
    • Aims to save time, money, and resources by being thoroughly prepared in analyzing business problems.

  • Importance of Structured Thinking

    • Prevents wasting time on unresolved issues.
    • Ensures clear understanding of analysis tasks and helps avoid rework due to missing crucial data.

  • Understanding the Problem Domain

    • Start with defining the specific area of analysis.
    • Establish requirements and hypotheses prior to in-depth investigation to prepare for any obstacles.

  • Example of Obstacles

    • Predicting future apartment values requires considering hundreds of variables (e.g., square footage).
    • Missing variables can lead to invalid conclusions and increased workload to redo analysis.

  • Scope of Work (SOW)

    • An agreed outline of what needs to be completed in a project.
    • Includes work details, schedules, and reports.
    • In data analysis, elements include data preparation, validation, analysis, initial results, and visuals.

  • Difference between Statement of Work and Scope of Work

    • Statement of Work (SOW): Detailed document for vendor services including objectives, guidelines, deliverables, timeline, and costs.
    • Scope of Work: Focuses on project-specific expectations and boundaries; usually created by data analysts rather than a comprehensive statement of work.

  • Example of a Simple SOW

    • Wedding planning task focusing on invitations:
    • Lists deliverables (e.g., invitations), timeline, and checkpoints to track progress.

Contextualizing Data

  • Importance of Context in Data Analysis

    • Context surrounds the data and influences its meaning.
    • Analyzing numbers without context can lead to misunderstanding and misinterpretation of data.

  • Various Levels of Data Utilization

    • Descriptive Data: Answers basic questions (e.g., how much spent on travel last month).
    • Diagnostic Data: Explains phenomena (e.g., why travel costs increased).
    • Predictive Data: Anticipates future occurrences (e.g., factors for higher efficiency in travel).
    • Prescriptive Data: Offers recommendations for action based on insight derived from analysis.

  • Impact of Personal Bias on Analysis

    • Two analysts may interpret the same dataset differently due to individual biases formed by personal and cultural beliefs.
    • Importance of objectivity in analysis and the potential for misleading conclusions if biases are not recognized.

  • Key Questions for Contextual Understanding

    • Who: Identity of those who created or collected the data.
    • What: What the data represents in real-world terms.
    • Where: Location of data collection.
    • When: Time frame of data collection (older data may not reflect current realities).
    • How: Methodology used for data gathering (surveys vs in-person interviews).
    • Why: Motivation behind data collection, which can signal potential biases.

  • Benefits of Contextualizing Data

    • Turns raw data into useful information by providing clarity and meaning.
    • Essential for the accuracy of analysis across all steps: collecting, organizing, analyzing, and presenting data.

  • Strategies for Contextual Analysis

    • Inquire about context during data collection, ensuring relevance to business processes.
    • Use context in naming conventions, variable relationships, and presentation of results to stakeholders for clarity in analytics.