SayPro Data Cleaning Checklist

SayPro Data Cleaning Checklist: A checklist to guide the cleaning and verification of tender data, ensuring it is complete and accurate before analysis. SayPro Monthly January SCMR-1 SayPro Monthly Data Analysis: Analyse data from previous tenders and bids by SayPro Tenders, Bidding, Quotations, and Proposals Office under SayPro Marketing Royalty SCMR

Objective:

To verify that all data related to tenders, bids, quotations, and proposals is accurate, consistent, and free from errors. This checklist will help employees ensure that all required data points are captured, identify missing or incorrect information, and streamline the process for analysis.

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Data Cleaning Checklist for Tender Data:

1. Data Completeness

• Tender ID: Ensure every tender has a unique identifier.
  • Checklist:
    • Check that no tender record is missing a Tender ID.
    • Ensure each Tender ID is unique and does not repeat across records.
• Bidder Name: Ensure all tender records have an associated bidder.
  • Checklist:
    • Verify that all bids include the bidder’s name.
    • Ensure the name is spelled correctly and formatted consistently across all records (no abbreviations, typos, or inconsistencies).
• Proposal Status: Verify that the status (Submitted, Under Review, Accepted, Rejected) is filled for each tender.
  • Checklist:
    • Confirm that every tender has a clearly defined proposal status.
    • Ensure there are no blank fields in the proposal status column.

2. Data Consistency

• Date Fields (Tender Submission Date, Evaluation Date, etc.): Ensure dates are formatted consistently (MM/DD/YYYY or YYYY-MM-DD).
  • Checklist:
    • Ensure there is no mix of different date formats.
    • Check that no date fields are empty, especially critical dates like submission deadlines and evaluation periods.
• Bid Amounts: Ensure bid amounts and awarded values are numerically consistent.
  • Checklist:
    • Confirm that all bid amounts are numeric and formatted properly (i.e., no letters, special characters, or empty fields).
    • Ensure there are no negative values unless explicitly required (e.g., refunds).
• Currency Consistency: If there are multiple currencies involved, ensure currency codes (USD, EUR, etc.) are consistent.
  • Checklist:
    • Check that all amounts in the bid or tender records include the correct currency code.
    • Ensure no mixing of currencies unless specified.

3. Data Accuracy

• Tender Details: Verify the accuracy of all tender details, such as project descriptions, tender type (open, restricted), and submission deadlines.
  • Checklist:
    • Review each tender record to ensure it accurately reflects the project scope and description.
    • Confirm that the tender type and deadlines are accurate.
• Bidder Details: Ensure bidder information (company name, contact information) is up to date.
  • Checklist:
    • Cross-check the bidder’s name and contact details (email, phone number) with other internal records.
    • Ensure that any repeated company names or bidder entries are consistent across records.
• Bid Status and Outcome: Cross-check the bid status and the actual outcome (award or rejection).
  • Checklist:
    • Ensure that the status accurately reflects the actual outcome.
    • Validate the awarded values and ensure they align with the successful bidder’s submission.

4. Handling Missing or Incomplete Data

• Missing Data Points: Identify any missing critical data, such as proposal submission dates or bid amounts.
  • Checklist:
    • Check for any blank cells in critical columns like Bidder Name, Tender ID, and Bid Amount.
    • Identify and flag any tenders with incomplete data for follow-up.
• Follow-up Actions for Missing Data: For any missing data points, ensure there is a process for data retrieval.
  • Checklist:
    • Document the missing data for further investigation or clarification from relevant teams.
    • Set deadlines for obtaining the missing information and follow up accordingly.

5. Validation and Verification

• Cross-verify Tender Details: Compare the collected data against external sources (e.g., original tenders or proposals).
  • Checklist:
    • Ensure all tender details match the official documentation.
    • Verify that awarded values and other financial details are consistent with the original tender documents.
• Internal Review: Have internal stakeholders review the data for consistency and accuracy.
  • Checklist:
    • Share the data with relevant teams (e.g., Sales, Finance) for validation.
    • Ensure the data passes internal quality control checks before proceeding to the analysis phase.

6. Data Formatting and Structure

• Uniform Formatting: Ensure all data is consistently formatted for easy analysis.
  • Checklist:
    • Confirm that all text entries (e.g., bidder names, tender descriptions) are free of extra spaces or non-standard characters.
    • Standardize formats for dates, financial values, and currency symbols.
• Duplicate Entries: Check for any duplicate tender or bidder entries.
  • Checklist:
    • Identify and remove duplicate rows or tenders to avoid skewed analysis.
    • Ensure that bid and tender records are unique.

7. Final Data Check

• Spot Check: Perform random spot checks on completed entries to ensure data quality.
  • Checklist:
    • Select a random sample of tender records to verify that all data is correct, consistent, and complete.
    • Review a variety of tenders from different categories and timelines to ensure comprehensive validation.
• Final Verification: Before finalizing the dataset, conduct a final review to ensure that no steps were overlooked.
  • Checklist:
    • Ensure that all data has been thoroughly reviewed for completeness, consistency, and accuracy.
    • Confirm that all necessary corrections and updates have been made.

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Data Cleaning Process:

1. Initial Review:
   • Review the tender data for any immediate issues such as missing fields, inconsistent formatting, or incomplete records.
2. Data Standardization:
   • Ensure all data conforms to standard formats, especially for numerical and date entries. Standardize tender names, bidder information, and other text-based data.
3. Identification of Issues:
   • Highlight any incomplete, inaccurate, or duplicate data for further action. This includes missing bid values, incorrect bidder details, or conflicting information.
4. Verification and Corrections:
   • Cross-check data against official records and sources. Validate bidder information and ensure that the bid outcomes (awarded or rejected) match the actual results.
5. Spot Check and Final Review:
   • After corrections, conduct a final review to ensure that all data is accurate and ready for analysis.

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Tools and Resources to Support Data Cleaning:

• Excel/Google Sheets Functions:
  • Data Validation: Use built-in data validation tools to prevent incorrect data entry.
  • Conditional Formatting: Highlight any incomplete or inconsistent data for easy identification.
  • VLOOKUP / INDEX-MATCH: Cross-reference data with other records or external databases to verify accuracy.
  • Remove Duplicates: Automatically identify and remove duplicate entries using Excel or Google Sheets’ built-in functionality.
• Internal Stakeholders: Collaborate with various teams (e.g., Sales, Finance) for data verification and completeness.

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Conclusion:

By following this Data Cleaning Checklist, SayPro can ensure that the tender data collected for analysis is accurate, consistent, and complete. This is vital for making informed decisions based on the data and ensuring the integrity of SayPro Monthly January SCMR-1 Data Analysis. The checklist also aids in identifying issues early, reducing the chances of errors in the final reports used for decision-making under the SayPro Marketing Royalty SCMR framework.