Do you still make decisions on your gut feeling? It’s out of practice in the present scenario. Data-driven corporate decisions have become a reliable key to unlock accurate and precise strategies. Eventually, their success is guaranteed for being data-powered.
So! How do you maintain your data? If it is on Excel, these tips are for you. Simply try and see how they help in scanning and fixing quality issues.
Why Data Quality Matters in Excel?
However, multiple alternatives are available for storing and working with data. Excel is the simplest of all databases. Its easy-to-use interface and interactive features help in reversing flawed decisions effortlessly. You can check accuracy, validate data entries, and ensure consistency for maintaining the integrity of your datasets. Its in-built tools and features effortlessly address data quality challenges.
Core Features for Ensuring Data Quality in Excel
The given information will be helpful to maintain the quality of data entry.
1. Data Validation for Error Control
Data validation is a unique feature that is mainly used to control the type of data to be entered in a cell or range. Simply put, preventing errors and maintaining consistency can be way easier if you know how to set rules for acceptable inputs.
Let’s understand through the date format, which can be specified so that the entry in the predefined range appears in a uniform format.
Use Case: Many retailers use Excel’s data validation to properly streamline the stock values or inventory management system. Simply put, it maps the validity of inventory items through their product IDs and stock quantities, which actually reduces errors by 35%.
Steps to Set Data Validation Rules:
1. Validation is applied on a specific range of cells. So, start by selecting the cell or range where you want to apply.
2. Move to the Data Tools menu and select Data Validation.
3. A dialogue box is there on the screen, where you need to set its validation criteria, considering what to:
- Specify the data type (e.g., whole numbers, decimals, dates).
- Define acceptable conditions (e.g., between, less than, equal to).
- Go to the Input Message tab, which displays instructions for users.
- Select the Error Alert tab to instruct what to do to counter invalid data entry.
- Stop: Eliminates invalid entries entirely.
- Warning: Alarms the user, but allows them to proceed.
- Information: Routes information without restrictions.
- Click OK to formally enable validation rules to run.
2. Setting Standardised Formats
To bring the consistency in the format across the spreadsheet, this setting is used. You can define specific formats for dates, numbers, and text to avoid inconsistent entries.
Example: Let’s say the date is standardised by selecting a specific date format, i.e., MM/DD/YYYY. This entry format automatically removes the chances of misinterpretations during analysis. Thereby, the processing errors are proactively controlled by 20%.
How to Set a Standardised Date Format?
- Click the cells or range where the formatting is to be applied.
- Apply dates-related criteria as validation.
- Customise alert message in case of seeing any incorrect format.
3. Circle Invalid Data
How long will it take to find errors in an Excel sheet if you have to do it manually? For sure, it will be a lengthy process. Its “Circle Invalid Data” feature is there to shorten this timeframe. It’s like an alert that prompts when any validation rules are violated. So, this feature highlights mistakes to integrate quality in the database.
Use Case: You can highlight invalid phone numbers and email IDs in a spreadsheet and align the valid contacts for emailing. It can reduce the bounce rate by 25%.
How to Use Circle Invalid Data:
- Visit the Data Validation menu.
- Mouse over the dropdown button and select Circle Invalid Data.
- This validation will immediately highlight invalid entries with red circles.
4. Removing Validation Rules
Sometimes, you don’t need validation rules to be applied. It can be done without affecting existing data this way.
Steps to Remove Data Validation:
- Select the cells where validation rules are applied.
- Click on Data Validation in the Data Tools group.
- A dialogue box will pop up where you need to click Clear All and then OK.
Advanced Techniques for Data Quality Management
The best practices for data management go with advanced techniques for quality management in databases. Here come a few ones:
1. Using Conditional Formatting
This function is popularly known to highlight anomalies or specific patterns in data. You can use it to flag, for example, duplicate entries, missing values to enrich, or spotlight outliers with desirable colours.
Example: A sales team can colour & highlight specific datasets, let’s say the downsides of sales campaigns, in its monthly performance table using conditional formatting. This highlighting can attract attention to fix underperforming areas.
2. Leveraging PivotTables for Validation
These tables are mainly used to summarise and then validate datasets. This is also a helpful method of discovering inconsistencies, which can be mismatched categories or missing entries.
Use Case: Many logistic companies use it to examine their shipment details. It immediately highlights untapped discrepancies in delivery times, for example. This information guides them to optimise routes and hence, minimise delays.
3. Excel Add-ins and Integrations
Quality is concerned with data cleansing. Excel has add-ins like Power Query to automate cleaning processes. It also allows users to integrate third-party plugins for advanced validation and reporting features.
Example: With these add-ins and integrations, a healthcare company can automatically consolidate and audit patient records from multiple sources. This best practice of data entry and quality improvement reduces errors by 40%.
Recommendations for Best Practices
- Automate Where Possible: Utilise built-in tools and add-ins to auto-validate and clean data.
- Set Clear Guidelines: Consistently define and communicate validation rules to all operators who are handling or working with the same data.
- Regular Audits: Be regular with the revision of datasets and rectify immediately.
- Train Your Team: Make your team familiar with validation features and quality-related best practices.
Conclusion
The aforesaid best practices of data entry will help in using validation features proactively. This is how businesses can see how the accuracy, reliability, and usability of their data are ultimately improving. These practices eventually drive better decision-making and operational efficiency.