DATA VALIDATION IN EXCEL

Leveling Up My Excel Skills: Data Validation in Action One of my proudest learning moments in Excel was mastering Data Validation. It’s more than just a tool—it’s a game-changer for keeping datasets clean, reliable, and easy to work with. Whether working with a small dataset or handling thousands of records, I’ve seen firsthand how Data Validation can save time, prevent errors, and make decision-making smoother. Let me share two ways I’ve applied it and why this skill means so much to me.

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SYNTAX WITH EXAMPLES The syntax for Data Validation in Excel depends on the type of validation rule you're setting up. Below are the most common rules, their formulas/settings, and examples:

1.??? Whole Numbers Purpose: Restrict entries to whole numbers within a specific range. Example: Allow only numbers between 1 and 100. Steps:

o?? Go to Data > Data Validation.

o?? In the Settings tab:

§? Validation criteria: Allow = Whole Number.

§? Data = Between.

§? Minimum = 1, Maximum = 100. Scenario Example Table: | Student Name | Test Score | |--------------|------------| | Alice Brown | 78 | | Jake Peters | 95 | Test scores outside the range of 1-100 are flagged as invalid, preventing errors in grading.

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2.??? Decimal Numbers Purpose: Restrict entries to decimal values within a specific range. Example: Allow decimals between 0 and 10. Steps:

o?? Allow = Decimal.

o?? Data = Between.

o?? Minimum = 0, Maximum = 10. Scenario Example Table: | Product | Rating | |-----------|--------| | Product A | 9.2 | | Product B | 7.5 | Only ratings between 0 and 10 are accepted, ensuring consistency in feedback collection.

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3.??? Date Purpose: Restrict entries to a specific date range. Example: Allow dates between January 1, 2023, and December 31, 2023. Steps:

o?? Allow = Date.

o?? Data = Between.

o?? Start Date = 1/1/2023, End Date = 12/31/2023. Scenario Example Table: | Event | Event Date | |-------------------|--------------| | Annual Conference | 03/15/2023 | | Product Launch | 10/25/2023 | Dates outside 2023 are restricted to prevent scheduling errors.

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4.??? Text Length Purpose: Restrict entries based on the number of characters in a cell. Example: Allow text entries with up to 10 characters. Steps:

o?? Allow = Text Length.

o?? Data = Less Than or Equal To.

o?? Maximum = 10. Scenario Example Table: | Username | |------------| | User123 | | AlexM2023 | Usernames exceeding 10 characters are invalid, maintaining a consistent format.

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5.??? List Purpose: Allow only specific values from a predefined list. Example: Allow entries of "Yes" or "No." Steps:

o?? Allow = List.

o?? Source = Yes, No.

o?? Alternatively, use a range in the sheet (e.g., $A$1:$A$2). Scenario Example Table: | Approval Status | |-----------------| | Yes | | No | Ensures responses are consistent with predefined options.

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6.??? Custom Formulas Purpose: Use a formula to validate data based on specific conditions. Example 1: Allow entries starting with “ORD-”. Formula: =LEFT(A1,4)="ORD-" Scenario Example Table:

Order ID

ORD-123456

ORD-987654

Prevents invalid Order IDs that don’t follow the required format.

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7.??? Example 2: Allow entries greater than 100 only if the adjacent cell contains "Approved." Formula: =AND(A1>100,B1="Approved") Scenario Example Table:

Amount

Status

250

Approved

90

Rejected

Only valid if the Amount is greater than 100 and the Status is "Approved."

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DATA VALIDATION EXAMPLES IN ACTION

1.??? Keeping Order IDs Accurate The challenge was ensuring all Order IDs followed a specific format: "ORD-" followed by six digits (e.g., ORD-123456). Even one small typo could create processing delays, which needed to be avoided. Steps taken:

o?? Set up a Custom Data Validation rule using this formula: =AND(LEFT(A1,4)="ORD-",ISNUMBER(VALUE(MID(A1,5,6))))

o?? Added an error message to guide users: “Order IDs must start with ‘ORD-’ and have six digits.” Result: Every entry was consistent and error-free, making tracking smoother and eliminating manual cleanup. | Order ID | Customer Name | Amount ($) | |-------------|-----------------|------------| | ORD-102345 | Sarah Johnson | 250.00 | | ORD-214678 | John Smith | 470.00 |

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2.??? Validating Sales Performance The challenge was ensuring every salesperson’s data fell within a realistic range—between 500 and 5,000 units sold—so the reports could be trusted. Steps taken:

o?? Used Whole Number Validation to restrict entries within the range.

o?? Added a clear error message: “Sales must be between 500 and 5,000 units. Please check your entry.” Result: This ensured we avoided inaccurate outliers that could skew analysis. | Salesperson | Region | Units Sold | |---------------|-------------|------------| | Alex Morgan | East Coast | 1200 | | Jamie Carter | West Coast | 4300 | | Taylor Lee | Midwest | 2750 |

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Why This Matters to Me For me, learning Data Validation is more than just mastering an Excel tool—it’s about solving problems, saving time, and building trust in the quality of my work. Clean, reliable data is the foundation of any successful project or analysis, and I take pride in ensuring that foundation is strong. This skill allows me to contribute to projects with confidence, whether I’m fine-tuning a small dataset or managing thousands of records. It’s a reflection of how much I care about precision, organization, and creating work that others can depend on.

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What About You? Have you ever used Data Validation or another Excel feature to solve a challenge? Let’s share ideas and inspire each other to keep learning and growing! ??

#ExcelSkills #DataValidation #LearningJourney #ProfessionalGrowth

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