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.