Do You Trust Your Data?
Stephanie Adams, SPHR
"The HR Consultant for HR Pros" | LinkedIn Top Voice | Excel for HR | AI for HR | HR Analytics | Workday Payroll | ADP WFN | Process Optimization Specialist
Welcome to The HR Edge! Every week, we bring you the latest in HR Insights, Tech tips, cutting-edge AI tools, and actionable steps designed to give HR and Payroll professionals a competitive advantage.
It goes without saying that a very large percentage of HR decision-makers don’t trust the data their organizations are producing.
Data doesn’t have to be perfect to yield new insights, but you must exercise caution by understanding where the flaws lie, working around errors, cleaning them up, and backing off when the data simply isn’t good enough.
Following are four levels of accuracy that show the extent to which your data meets the criteria of “healthy data”:
Level 1 – Thorough
Is the Data Complete and Consistent Across Your Systems?
Clean data is free from errors and duplications, complete data covers all necessary details without gaps, and consistency means that the same standards and formats are applied across all platforms and datasets. Ensuring all three can significantly boost the trustworthiness of data, facilitating better decision-making and compliance.
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Level 2 – Transparent
Is the Data Accessible and Understandable?
Transparency in data means that information is readily accessible to those who need to analyze it, and it is presented in a format that is straightforward and easy to understand, regardless of the user's level of technical expertise. This level of openness not only aids in accountability and compliance but also enhances stakeholder engagement by demystifying data outputs.
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Level 3 - Timely
Is the Data Up-to-Date and Readily Available to the People Who Need It?
Data is only as good as its relevance, and timeliness is key to relevance. Up-to-date data ensures that HR professionals are making decisions based on the latest and most accurate information. Streamlining data processes to deliver real-time or near-real-time data can prevent outdated information from leading to poor strategic choices.
Level 4 - Validated
Has the Data Been Validated by Key Users?
Validation involves regular checks by key users to certify that the data accurately reflects the real-world scenarios it is meant to represent. This step is crucial for maintaining data integrity and reliability, preventing costly errors that can derail company objectives.
Trusting HR data shouldn't involve guesswork. By diligently applying these four levels of data accuracy—thorough, transparent, timely, and validated—you can secure a robust foundation for HR decision-making. With trustworthy data, HR can confidently drive business strategies that propel the organization forward.
Does your data pass the test?
Top 5 Excel Formulas for Data Cleaning
My clients are often surprised to discover that the cost of cleaning and preparing their data for analysis exceeds the expenses of the actual analysis and presentation.
Cleaning data is crucial for maintaining the accuracy and usability of information.
Following are the formulas I specifically use for cleansing HR data:
#1 - TRIM()
Purpose: Removes extra spaces from text except for single spaces between words.
Structure: =TRIM(A1:A15)
Common Use: Cleansing employee names, addresses, or any textual data that may have irregular spacing.
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#2 - UPPER(), LOWER(), and PROPER()
Purpose: Converts text data to upper case, lower case, or proper case (first letter in uppercase and the rest in lowercase), respectively.
Structure: =UPPER(A1:A15), =LOWER(A1:A15), =PROPER(A1:A15)
Common Use: Standardizing the format of text data like names and locations.
#3 - CLEAN()
Purpose: Removes non-printable characters from text.
Structure: =CLEAN(A1:A15)
Common Use: Cleaning data imported from other systems or databases that may contain characters incompatible with your spreadsheet or database.
#4 - TEXT()
Purpose: Converts a value to text in a specified number format.
Structure: =TEXT(A1:A15, "00000")
Common Use: Formatting numbers, such as employee IDs or other standardized numeral data, to meet text entry requirements in databases.
#5 - SUBSTITUTE()
Purpose: Replaces existing text with new text in a string.
Structure: =SUBSTITUTE(A1:A15, "old text", "new text")
Common Use: Correcting recurrent spelling errors in data or replacing outdated terms or codes with updated ones.
These formulas can help ensure the consistency, accuracy, and reliability of data sets, leading to better data management and analytics outcomes.
Incorporating these into your data cleaning routines can save time and reduce errors in HR-related tasks.
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