Data Validation: Data Validation Strategies for Reliable VLOOKUP Partial Matches

1. Introduction to VLOOKUP and Its Importance in Data Validation

VLOOKUP, or Vertical Lookup, is an incredibly powerful function in Excel that allows users to search for specific information in a dataset. Its primary use is to find data in a table or range by row. The function searches for a key value in the first column of a range and returns a value in the same row from a specified column. The simplicity and versatility of VLOOKUP make it a vital tool for anyone dealing with large sets of data.

The importance of VLOOKUP in data validation cannot be overstated. It serves as a critical component in ensuring data integrity, allowing users to cross-reference and verify information quickly and efficiently. For instance, in a database containing employee information, VLOOKUP can be used to validate an employee's department by matching their ID number against a department list. This not only saves time but also significantly reduces the margin for error.

From a data analyst's perspective, VLOOKUP is indispensable for reconciling data from different sources. It enables the comparison of lists and the identification of discrepancies, which is essential for accurate reporting and decision-making. On the other hand, from a business user's point of view, VLOOKUP is a gateway to self-sufficiency in data handling, minimizing reliance on IT departments for data queries.

Here are some in-depth insights into VLOOKUP and its role in data validation:

1. Lookup Value: The starting point of VLOOKUP is the lookup value. This is the piece of data you want to search for in your table array. It's important that this value is unique to avoid incorrect or multiple matches.

2. Table Array: This is the range of cells that contains the data you want to retrieve. It's crucial to ensure that the table array does not include any errors and is sorted correctly if you're not using an exact match.

3. column Index number: After identifying the row where the lookup value is found, VLOOKUP returns the value in the same row from the column index number you specify. This number represents the column of data you want to pull into your result.

4. Range Lookup: This parameter determines whether you seek an exact match (FALSE) or an approximate match (TRUE). For data validation purposes, an exact match is often necessary to maintain data accuracy.

To highlight the utility of VLOOKUP with an example, consider a sales report that needs to validate product prices against a master price list. By using VLOOKUP, the report can quickly identify any discrepancies between the reported sale price and the official list price, ensuring that the data reflects the true financial performance.

VLOOKUP is more than just a function; it's a fundamental skill for anyone who works with data. Its role in data validation is pivotal, providing a reliable method for ensuring data accuracy and consistency. Whether you're a seasoned data professional or a business user, mastering VLOOKUP can significantly enhance your data validation capabilities. It's a tool that, once learned, becomes an indispensable part of your data management toolkit.

2. When and Why Theyre Used?

In the realm of data validation, partial matches are a critical tool, especially when dealing with large datasets where exact matches may not always be possible due to inconsistencies or variations in the data. The use of partial matches allows for a more flexible and forgiving approach to data lookup, which can be particularly useful in situations where data is entered manually, leading to potential discrepancies in spelling, formatting, or completeness.

Partial matches come into play when data analysts or users perform searches that do not require an exact match to return useful results. This is often the case when dealing with human-generated data, such as names, addresses, or descriptions, where variations are common. For instance, searching for "Jon" might also return "Jonathan" or "John" as valid results. In the context of VLOOKUP, a popular spreadsheet function, partial matches can be leveraged to find and retrieve information even when the search key is only partially present in the reference dataset.

Insights from Different Perspectives:

1. user-Friendly data Retrieval:

From a user's perspective, partial matches can greatly enhance the usability of a data system. Users are not always aware of the exact terms or data structure and may only have a fragment of the information they are seeking. By accommodating partial matches, systems can provide results that are still relevant and useful, thereby improving the overall user experience.

2. Data Integrity Concerns:

On the flip side, data professionals might raise concerns about the integrity of results derived from partial matches. There is a risk of retrieving incorrect data if the match is too broad or if multiple entries satisfy the partial match criteria. This necessitates the implementation of robust validation rules to ensure that the results are as accurate as possible.

3. Performance Implications:

From a performance standpoint, partial matches can be computationally intensive, especially in large datasets. This is because the system may need to scan through more data to find potential matches, which can slow down the retrieval process. Optimizing the search algorithm and indexing the data can help mitigate these performance issues.

In-Depth Information:

1. Setting Up partial Matches in vlookup:

To enable partial matches in VLOOKUP, users typically employ wildcard characters such as the asterisk () or the question mark (?). For example, a VLOOKUP formula like `=VLOOKUP("Jon", data_range, column_index, FALSE)` would return the first instance where the data begins with "Jon".

2. Handling Multiple Matches:

When multiple entries satisfy the partial match criteria, it's important to have a strategy for determining which result to return. This might involve additional functions or sorting mechanisms to prioritize certain matches over others.

3. Combining Exact and Partial Matches:

In some cases, a hybrid approach may be necessary, where exact matches are preferred, but partial matches are used as a fallback. This can be achieved by nesting VLOOKUP functions or combining them with IF statements to check for exact matches first.

Example to Highlight an Idea:

Consider a dataset containing a list of book titles. A user wants to find books related to "cooking" but isn't sure of the exact titles. A vlookup with a partial match, such as `=VLOOKUP("cooking", titles_range, column_index, FALSE)`, would allow them to retrieve all titles containing the word "cooking", regardless of what comes before or after it in the title.

Understanding when and why to use partial matches is essential for anyone working with data. It requires a balance between flexibility and accuracy, and the implementation of partial matches must be carefully considered to ensure that the data validation process remains reliable and efficient.

3. Setting Up Your Data for VLOOKUP Success

Ensuring that your data is primed for VLOOKUP is a critical step in the process of data validation, especially when dealing with partial matches. The VLOOKUP function is a powerful tool in Excel that allows you to search for a value in the first column of a table array and return a value in the same row from another column. However, its effectiveness hinges on the quality and structure of your data. To achieve reliable results, your data must be meticulously organized, with the lookup value in the first column and subsequent columns containing the data you wish to retrieve. It's also essential to consider the format of your data; inconsistencies in data types can lead to unexpected errors or mismatches. For instance, numerical values stored as text can cause VLOOKUP to fail, even if they appear identical to the naked eye.

Here are some in-depth strategies to set up your data for VLOOKUP success:

1. Standardize Data Entry: Consistency is key. Ensure that all data entered follows a standard format. For example, if you're using dates, decide whether to use MM/DD/YYYY or DD/MM/YYYY and stick to it.

2. Sort Your Data: While VLOOKUP can work with unsorted data, sorting your table array can improve performance and help prevent errors, especially when dealing with approximate matches.

3. Use Trim and Clean Functions: These functions are invaluable for removing any extra spaces or non-printable characters that may have been inadvertently entered into your data.

4. Employ data Validation rules: Set up data validation rules to restrict the type of data or the values that users can enter into a cell. This can help prevent errors at the source.

5. convert numbers Stored as Text: If your lookup values are numbers, make sure they are not stored as text. You can use the 'Value' function to convert text to numbers.

6. Utilize Helper Columns: Sometimes, you may need to combine multiple columns to create a unique identifier for VLOOKUP. Helper columns can concatenate data from different columns to form a composite key.

7. Check for Duplicates: Duplicate values in your first column can cause VLOOKUP to return incorrect results. Use conditional formatting to highlight duplicates and remove them if necessary.

8. Exact Match vs. Approximate Match: Understand the difference between exact and approximate matches in VLOOKUP. An exact match searches for a value that exactly matches the lookup value, while an approximate match searches for the closest value that is less than or equal to the lookup value.

9. Error Handling: Incorporate IFERROR or IFNA functions to handle errors gracefully. This can provide a default value or a custom message instead of an error code.

10. Test Your Setup: Before relying on VLOOKUP for critical tasks, test your setup with known values to ensure it returns the correct results.

For example, imagine you have a dataset of employee IDs and names, and you want to look up the name associated with a particular ID. If the IDs are not standardized (some with leading zeros, others without), VLOOKUP may not find the correct match. By using a helper column to standardize the format (e.g., using the TEXT function to format all IDs to have leading zeros), you can ensure that VLOOKUP works correctly.

By following these strategies, you can set up your data in a way that maximizes the accuracy and reliability of VLOOKUP, particularly when dealing with partial matches. Remember, the success of VLOOKUP is largely dependent on the preparation and structure of your data. Taking the time to set it up correctly can save you from headaches down the line.

4. Common Pitfalls with Partial Matches and How to Avoid Them

When dealing with data validation, particularly in the context of vlookup partial matches, it's crucial to navigate the process with precision and care. Partial matches can be a double-edged sword; they allow for flexibility in matching terms that are not exactly alike, but this very flexibility can lead to mismatches and errors that compromise data integrity. The key to harnessing the power of partial matches lies in understanding their common pitfalls and implementing strategies to avoid them.

One of the most common pitfalls is the inadvertent matching of unrelated data due to similar substrings. For instance, searching for "Ham" might incorrectly match with "Shampoo" if not properly constrained. This can lead to significant inaccuracies in data analysis and reporting. To prevent such issues, it's essential to:

1. Use Specific Identifiers: Where possible, include unique identifiers in your data set that can be used for matching instead of relying solely on partial text matches. This could mean adding an ID column that corresponds to the text data you're matching.

2. Set Match Criteria: Clearly define the criteria for what constitutes a match. This might involve setting rules for the number of characters that must match or the position of the substring within the text.

3. Implement Wildcards with Caution: Wildcards like `` and `?` can be used in VLOOKUP to perform partial matches, but they should be used judiciously. For example, `VLOOKUP("Ham",...)` will match any cell starting with "Ham". However, this can also return "Hamburger" which may not be desired.

4. Regular Expressions: For more complex partial match scenarios, consider using regular expressions if your software supports it. This allows for pattern matching that can differentiate between "Ham" and "Shampoo".

5. Data Sanitization: Ensure that the data is clean and standardized before attempting partial matches. This includes removing extra spaces, standardizing text cases, and eliminating special characters that might lead to false matches.

6. Test and Validate: Before fully implementing your VLOOKUP strategy, test it on a subset of your data. Validate the results manually to ensure that the partial matches are functioning as intended.

7. Error Handling: Incorporate error handling in your vlookup formula to manage instances where no match is found. This can prevent cascading errors in your data set.

8. user-Defined functions (UDFs): In some cases, the built-in VLOOKUP functionality may not suffice. Creating UDFs can provide the flexibility needed for more complex partial match scenarios.

Example: Imagine you have a list of product codes and you want to match them with a list of transaction records. The product code "12345" might appear in transactions as "12345-00" or "00-12345". A naive partial match could also match "1234567" or "012345". To avoid this, you could use a UDF that strips away non-numeric characters and compares the numerical parts only.

By being aware of these pitfalls and taking proactive steps to mitigate them, you can ensure that your VLOOKUP partial matches are reliable and your data validation efforts are successful. Remember, the goal is to strike a balance between the flexibility of partial matches and the precision required for accurate data analysis.

5. Advanced Techniques for Enhancing VLOOKUP Partial Match Accuracy

When dealing with large datasets, ensuring the accuracy of vlookup partial matches is paramount. Partial matches are often necessary when data is not standardized or when looking to match on a subset of a given string. However, this can lead to mismatches and inaccuracies if not handled correctly. To mitigate these risks, advanced techniques must be employed that go beyond the basic VLOOKUP functionality. These methods involve a combination of formula adjustments, helper columns, and an understanding of the underlying data structure. By considering different perspectives, such as those of a data analyst who requires precision, or an end-user who needs understandable results, we can tailor our approach to enhance the reliability of partial matches.

Here are some advanced techniques to consider:

1. Wildcard Characters: Use wildcard characters like the asterisk () and question mark (?) within your VLOOKUP formula to match any sequence of characters or a single character, respectively. For example, `=VLOOKUP(""&A1&"*", B:C, 2, FALSE)` would return a match where the value in A1 is found anywhere within the lookup column.

2. Regular Expressions (RegEx): If your spreadsheet software supports JavaScript or a similar scripting language, you can use RegEx for more complex pattern matching. This is particularly useful for matching patterns within strings that are not possible with wildcards alone.

3. Helper Columns: Create additional columns in your data that contain standardized or simplified versions of the data you're trying to match. For instance, if you're matching company names but the dataset includes abbreviations, a helper column could contain the full names to improve match accuracy.

4. Fuzzy Lookup Add-Ins: Some spreadsheet programs offer add-ins for fuzzy lookups, which are less exact than VLOOKUP but can be more flexible with partial matches. These tools compare similarity between strings and provide a match based on a threshold you set.

5. Index and Match Combination: Replace VLOOKUP with a combination of index and MATCH functions for more control over the lookup process. This allows for leftward lookups and better handling of partial matches.

6. array formulas: Use array formulas to search for multiple criteria in a single formula. This can be complex but powerful when looking for a combination of partial matches across different columns.

7. Double VLOOKUP: Perform a VLOOKUP within another VLOOKUP to handle cases where there are multiple potential matches, and you need to refine the search further.

8. Text Functions: Utilize text functions such as LEFT, RIGHT, MID, LEN, and SUBSTITUTE to manipulate and match specific parts of the strings.

9. data Validation lists: Implement data validation lists to limit the user's input to predefined options, reducing the chances of partial match errors.

10. Training and Documentation: Provide training for users on how to input data consistently and create documentation on how to use the VLOOKUP system effectively.

By employing these techniques, you can significantly improve the accuracy of VLOOKUP partial matches. Remember, the key is to understand the data you're working with and choose the method that best suits your specific scenario. Let's illustrate with an example:

Suppose you have a list of abbreviated state names, and you want to match them to their full names. You could set up a helper column with the full names and use a combination of the INDEX, MATCH, and wildcard characters to find the correct match. For instance, if you're looking for "NY" and want to match it to "New York," your formula might look like this:

```excel

=INDEX(FullNamesColumn, MATCH(""&AbbreviatedName&"", HelperColumn, 0))

This formula will search the HelperColumn for any instance of the AbbreviatedName surrounded by any number of characters, effectively allowing for a partial match that can handle variations in the data.

6. Incorporating Wildcards in VLOOKUP for Flexible Data Retrieval

In the realm of data management, the ability to retrieve information efficiently and accurately is paramount. One of the most powerful tools at the disposal of data analysts is the VLOOKUP function in spreadsheet software like Microsoft excel. However, the traditional use of VLOOKUP is often limited by its need for exact matches, which can be a significant hindrance when dealing with real-world data that is rarely so clean-cut. This is where wildcards come into play, transforming VLOOKUP from a rigid function into a dynamic and flexible tool capable of handling a variety of partial match scenarios.

Insights from Different Perspectives:

1. From a Data Analyst's Viewpoint:

- Wildcards can be used to match a substring within a cell, allowing analysts to find related records even when the data contains inconsistencies such as extra characters or varying formats.

- Example: Searching for "Co*" in a product list to find all products starting with "Co" regardless of what follows.

2. From a Database Administrator's Perspective:

- Incorporating wildcards in VLOOKUP can reduce the need for complex database queries and can be a quick workaround for non-standardized data entries.

- Example: Using "*services" to retrieve all entries that end with "services", bypassing the need for SQL pattern matching.

3. From a Business User's Standpoint:

- The flexibility offered by wildcards in VLOOKUP can lead to more intuitive data retrieval, aligning the function more closely with natural language processing.

- Example: A manager looking for all expense reports related to "travel" could use "travel" to capture all relevant documents.

In-Depth Information:

1. Types of Wildcards:

- The asterisk (*) represents any number of characters.

- The question mark (?) stands for a single character.

- Tilde (~) is used to escape the wildcard characters for a literal search.

2. Syntax for Incorporating Wildcards:

- The syntax for a VLOOKUP formula using wildcards is `=VLOOKUP(""&[lookup_value]&"", [table_array], [col_index_num], FALSE)`.

- This allows for partial matches on either side of the lookup value.

3. Limitations and Considerations:

- Wildcards cannot be used when the range_lookup argument is TRUE, as this requires an exact match.

- Overuse of wildcards can lead to performance issues, especially in large datasets.

Practical Examples:

- Example 1:

Suppose you have a list of employee names and you want to find all employees whose names start with "J". You could use the formula `=VLOOKUP("J*", [employee_list], [column_number], FALSE)`.

- Example 2:

If you're looking for a specific transaction in a financial ledger that includes a reference number "123", but you're not sure of the surrounding characters, you could use `=VLOOKUP("123", [transaction_ledger], [column_number], FALSE)`.

By incorporating wildcards into VLOOKUP, users can significantly enhance the function's utility, making it a more robust and indispensable feature in the data analyst's toolkit. Whether it's dealing with data entry errors, non-standardized formats, or simply trying to find a needle in a haystack, wildcards offer a level of flexibility that can meet the challenge. It's a simple yet profound enhancement that underscores the importance of adaptability in data retrieval processes.

7. Automating Data Validation Checks with VLOOKUP and Conditional Formatting

In the realm of data management, ensuring the accuracy and consistency of data is paramount. Automating data validation checks with vlookup and Conditional formatting in Excel is a powerful strategy to enhance the reliability of data analysis, especially when dealing with partial matches. This approach not only streamlines the validation process but also significantly reduces the margin for human error, leading to more trustworthy data sets.

From the perspective of a data analyst, automating these checks means less time spent on manual reviews and more time for in-depth analysis. For IT professionals, it translates into more robust systems that can handle data discrepancies with ease. And from a business standpoint, it ensures that decision-makers are basing their strategies on solid, error-free data.

Here's an in-depth look at how to implement this automation:

1. Setting Up VLOOKUP for Partial Matches:

- Use the wildcard characters, such as an asterisk (), to perform a partial match vlookup. For example, `=VLOOKUP(""&A2&"*", B2:C10, 2, FALSE)` will search for any value in column B that contains the text in A2.

- Combine VLOOKUP with other functions like IFERROR to handle non-matches gracefully. For instance, `=IFERROR(VLOOKUP(""&A2&"", B2:C10, 2, FALSE), "No Match")` will return "No Match" if there is no corresponding value.

2. leveraging Conditional formatting:

- Highlight cells with potential errors by setting up rules in Conditional formatting. For example, you can create a rule that changes the cell's background color if the VLOOKUP result is "No Match".

- Use conditional Formatting to visually compare two columns for discrepancies. If column A is supposed to match column B, a rule can be set to highlight differences, aiding in quick identification of mismatches.

3. Creating Data Validation Rules:

- Implement data validation to restrict the type of data entered into a cell. For example, you can set a validation rule that only allows dates in a specific format or range.

- Use data validation in conjunction with VLOOKUP to ensure that entries in a column exist in a master list. This can prevent spelling errors and unstandardized entries.

4. Automating the Process with Macros:

- Record a macro that applies VLOOKUP and Conditional Formatting across multiple cells or sheets. This can be triggered whenever new data is entered or imported.

- Develop custom scripts using VBA to handle more complex validation scenarios, such as cross-referencing multiple data sources.

Example Scenario:

Imagine a sales database where each transaction record must be linked to an existing customer ID. By setting up a VLOOKUP function to search for the customer ID within the master customer list and using Conditional Formatting to highlight any records that do not match, the system can immediately flag entries that may require correction. This not only ensures the integrity of the sales data but also facilitates a smoother workflow for the sales and accounting departments.

Automating data validation checks with VLOOKUP and Conditional Formatting is a strategic move towards maintaining high-quality data. It's a testament to how a little upfront effort in setting up these systems can yield significant long-term benefits in terms of data reliability and operational efficiency. Whether you're a seasoned data professional or just starting out, mastering these tools can make a substantial difference in your data validation endeavors.

8. Troubleshooting VLOOKUP Errors and Partial Match Discrepancies

VLOOKUP is a powerful tool in Excel that allows users to search for specific information in a dataset. However, it's not without its challenges, particularly when dealing with partial matches. Partial match discrepancies can occur when the lookup value does not exactly match the data in the lookup column, leading to errors or incorrect results. Troubleshooting these errors requires a keen eye for detail and an understanding of how VLOOKUP interprets data. It's essential to ensure that the data is clean, consistent, and formatted correctly before attempting a VLOOKUP with partial matches. Additionally, users must be aware of the limitations of VLOOKUP, such as its inability to look to the left of the reference column or search for more than one criterion without additional functions.

Here are some in-depth insights into troubleshooting VLOOKUP errors and partial match discrepancies:

1. Exact vs. Approximate Match: Ensure that the range_lookup argument is set to FALSE for an exact match. If TRUE or omitted, vlookup will use an approximate match, which can lead to unexpected results if the table is not sorted correctly.

2. Data Formatting: Check for inconsistencies in data formatting. Numbers stored as text or vice versa can cause VLOOKUP to fail. Use the `TEXT` or `VALUE` functions to convert data types where necessary.

3. Leading and Trailing Spaces: Extra spaces in either the lookup value or the table array can prevent a match. Use the `TRIM` function to remove unwanted spaces.

4. Hidden Characters: Sometimes, data imported from other sources may contain non-printable characters. The `CLEAN` function can be used to remove these characters.

5. Case Sensitivity: VLOOKUP is not case-sensitive. If case sensitivity is required, consider using the `EXACT` function in combination with an array formula.

6. Using Wildcards: For partial matches, wildcards like the asterisk (*) for multiple characters or the question mark (?) for a single character can be used within the lookup_value.

7. Combining Functions: If you need to look up based on multiple criteria, combine VLOOKUP with other functions like `CHOOSE` or `INDEX` and `MATCH`.

8. Error Handling: Incorporate error handling with `IFERROR` or `IFNA` to provide alternative results or messages when VLOOKUP returns an error.

For example, suppose you have a list of product codes with varying lengths and you want to find the price of a product using a partial code. You could use:

```excel

=VLOOKUP(""&A2&"",B2:C10,2,FALSE)

In this formula, `A2` contains the partial product code, `B2:C10` is the range with full product codes and corresponding prices, and `2` indicates the second column where the price is located. The asterisks act as wildcards to match any sequence of characters before and after the partial code.

By understanding these nuances and applying the right techniques, users can significantly reduce the occurrence of VLOOKUP errors and ensure more reliable results, even when dealing with partial matches. Remember, the key to successful data validation lies in meticulous preparation and strategic use of Excel's functionalities.

9. Best Practices for Maintaining Data Integrity with VLOOKUP Partial Matches

Ensuring data integrity when performing VLOOKUP partial matches is a critical aspect of data management that requires meticulous attention to detail and a deep understanding of the data at hand. Partial matches can be particularly challenging because they involve searching for and matching data that may not be an exact fit, which increases the risk of errors and inconsistencies. To mitigate these risks, it's essential to adopt a set of best practices that promote accuracy and reliability in your VLOOKUP operations. These practices should encompass a comprehensive approach, including the preparation of your data, the formulation of your VLOOKUP function, and the ongoing management of your dataset. By considering different perspectives, such as the data analyst who requires precision, the end-user who seeks clarity, and the database administrator who prioritizes efficiency, we can develop a robust framework for maintaining data integrity.

Here are some best practices to consider:

1. Standardize Data Formats: Before performing VLOOKUP partial matches, ensure that the data is standardized. For example, if you're looking up names, decide on a format (e.g., "First Last" or "Last, First") and apply it consistently across your dataset.

2. Use Helper Columns: Create helper columns that concatenate or extract key parts of the data to facilitate more accurate matching. For instance, if you're matching product codes that share a common prefix, a helper column could isolate that prefix for a more reliable VLOOKUP.

3. Implement Wildcards: When appropriate, use wildcards like the asterisk (*) to match any sequence of characters and the question mark (?) to match any single character. This can be useful when you know only part of the lookup value.

4. Apply Exact Match First: Attempt an exact match vlookup before resorting to partial matches. This can help avoid unintended matches and ensure that the most accurate data is retrieved.

5. Validate with IFERROR: Use the IFERROR function to handle errors gracefully. It allows you to specify an alternative action or result if VLOOKUP results in an error, such as a mismatch.

6. Double-Check with Conditional Formatting: Utilize conditional formatting to highlight unexpected or anomalous results. This visual aid can quickly draw attention to potential issues that may require further investigation.

7. Regularly Update Lookup Tables: Keep your lookup tables up-to-date to prevent mismatches due to outdated information. This is especially important in dynamic environments where data changes frequently.

8. Document Your Methodology: Maintain clear documentation of your VLOOKUP approach, including the logic behind partial matches. This transparency can be invaluable for troubleshooting and future audits.

For example, consider a dataset containing employee records where you need to match employees based on their names, but the names may not be consistently formatted. You could create a helper column that combines the first three letters of the first name with the first three letters of the last name. Then, you could use a VLOOKUP with wildcards to perform a partial match, like so:

```excel

=VLOOKUP(CONCATENATE(LEFT(A2,3), LEFT(B2,3))&"*", EmployeeTable, 2, FALSE)

In this formula, `A2` contains the first name, `B2` contains the last name, and `EmployeeTable` is the range containing the employee records. The wildcard "*" allows for any characters to follow the concatenated string, enabling a partial match.

By integrating these best practices into your workflow, you can significantly enhance the reliability of your VLOOKUP partial matches and maintain the integrity of your data. Remember, the goal is not just to find a match, but to ensure that the match is the correct one.

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