SayPro Data Analysis Report Template: Data Sources

Purpose:

The Data Sources section of the Data Analysis Report outlines where the data used for the analysis was collected from. It helps provide transparency about the origins of the data, ensuring the credibility, accuracy, and validity of the analysis. This section also explains any assumptions made or limitations inherent in the data.

---

Data Sources Section Structure

1. Title

• Section Title: “Data Sources”
  • A simple and clear heading to define this section.

2. List of Data Sources

• Identify all Data Sources Used:
  • List all the databases, systems, tools, or external data sources that contributed to the analysis.
  • Example:
    • “Sales data extracted from the internal CRM system (Salesforce).”
    • “Customer satisfaction survey results from SurveyMonkey (Q4 2024).”
    • “Financial data from the company’s ERP system (SAP).”
    • “Market research data from third-party provider XYZ Research.”

3. Data Type/Format

• Describe Data Format or Type:
  • Indicate the type and format of the data collected, as this can influence how it was processed or analyzed.
  • Example:
    • “CRM data was in CSV format and consisted of customer interactions, purchase history, and demographic information.”
    • “Survey responses were collected via an online form and provided in Excel format.”
    • “Financial records were extracted from ERP reports and in PDF format.”

4. Date Range or Time Period of Data

• Time Period:
  • Specify the time period the data covers. This provides context on the relevance and timeliness of the information used in the analysis.
  • Example:
    • “Sales data for the period from January 2023 to December 2023.”
    • “Survey responses collected from January 2024 to February 2024.”
    • “Financial data for FY 2023, covering the period from April 2023 to March 2024.”

5. Data Collection Methodology

• Describe How Data Was Collected:
  • Explain the methods used to collect data, such as manual entry, automated extraction, surveys, or external providers.
  • Example:
    • “Sales data was automatically pulled from the Salesforce CRM using integrated API queries.”
    • “Survey responses were gathered via an online questionnaire sent to 500 random customers.”
    • “Financial data was exported from SAP ERP after month-end closings.”

6. Data Quality & Limitations

• Assess Data Quality:
  • Include any notes on data completeness, accuracy, or limitations. For example, mention if the data was incomplete or if any assumptions had to be made due to missing or unreliable data.
  • Example:
    • “Some customer demographic information was missing due to incomplete profile entries in the CRM system.”
    • “Survey responses may have a bias, as they were voluntary and not representative of all customer segments.”
    • “Financial records were subject to reconciliation processes, and a few minor discrepancies were found between departments.”

7. Data Validity and Reliability

• Validate the Credibility of Sources:
  • Provide information on how reliable and valid the data sources are. For instance, state whether the sources are trusted, regularly updated, or if any quality control measures were implemented.
  • Example:
    • “The CRM data is regularly updated and vetted by the IT department for accuracy.”
    • “Survey results are validated by the external vendor (SurveyMonkey) to ensure reliability and anonymity.”
    • “Financial data is cross-checked with the finance team and is part of the official monthly reporting process.”

8. External Data Providers (If Applicable)

• External Data Vendors:
  • If external data providers are used, mention their name, the service they provided, and any relevant details regarding the data.
  • Example:
    • “Market research data was purchased from XYZ Research, which specializes in global retail industry trends.”

---

Example Layout:

Section Title	Data Sources
Data Source 1	CRM Data
	– Description: Extracted from Salesforce CRM.
	– Format: CSV files with customer interaction, sales, and demographic data.
	– Time Period: January 2023 to December 2023.
	– Collection Method: Automated extraction via Salesforce API.
	– Data Quality: Complete, but some demographic data is missing.
Data Source 2	Customer Survey Data
	– Description: Survey responses collected via SurveyMonkey.
	– Format: Excel spreadsheet containing raw survey results.
	– Time Period: January 2024 to February 2024.
	– Collection Method: Online questionnaire distributed to 500 customers.
	– Data Quality: Some response bias due to voluntary participation.
Data Source 3	Financial Data
	– Description: Extracted from the company’s SAP ERP system.
	– Format: PDF reports with detailed financial records.
	– Time Period: FY 2023 (April 2023 – March 2024).
	– Collection Method: Manual export from ERP system after month-end closing.
	– Data Quality: Reconciliation discrepancies found between departments.
External Data Source	Market Research Data
	– Description: Market research data purchased from XYZ Research.
	– Format: Excel and PDF reports.
	– Time Period: 2024 projections and historical data.
	– Collection Method: Third-party research firm.
	– Data Quality: High, as it comes from a reputable external source.

---

Design Tips:

• Clarity and Transparency: Ensure that each data source is well-explained, allowing anyone reading the report to understand where the data came from and how it was collected.
• Consistent Formatting: Use consistent formatting (e.g., bullet points or tables) for easy comparison across data sources.
• Visual Cues: Consider using icons or visual cues (e.g., database icon for CRM data, survey icon for survey data) to make the section more visually engaging and organized.

---

Conclusion:

The Data Sources section is critical for establishing the credibility and reliability of the analysis. By clearly documenting where the data came from, how it was collected, and any limitations or assumptions, you provide transparency to your audience and build trust in the findings.