Is Your Data Holding You Back Clean It Up.

• Is Your Data Holding You Back? Clean It Up!

Remove Duplicate Rows

• Use the Remove Duplicates feature in Power Query Editor.
• Select the columns that define a unique record.
• Always preview data to confirm which rows will be removed.
• This prevents skewed results and double-counting.
• Consider if all columns are necessary for the check.
• It is a quick way to ensure data integrity.

Correct Data Types

• Ensure numbers are not stored as text.
• Check that dates are recognized correctly by Power BI.
• Use the data type dropdown in the transform tab.
• Incorrect types cause errors in calculations and visuals.
• Look for columns with the ABC icon which indicates text.
• Change decimal numbers to fixed decimal for currency.
• Use the detect data type feature as a starting point.

Handle Missing Values

• Identify blanks or nulls with filters in Power Query.
• Decide to remove rows with critical missing data.
• Replace nulls with a default value like zero or "N/A".
• Use the fill feature to propagate values up or down.
• Create a calculated column to flag missing entries.
• Analyze if missing data follows a specific pattern.
• Avoid having blanks in key measure fields.

Trim and Clean Text

• Apply the Trim transformation to remove extra spaces.
• Use Clean to remove non-printable characters.
• Standardize text case using Upper, Lower, or Proper.
• Replace abbreviations with full names for consistency.
• Remove prefixes or suffixes that are not needed.
• This ensures text fields merge and group correctly.
• Prevent duplicate categories due to formatting.

Split Columns Wisely

• Break apart combined fields like full name or address.
• Use the Split Column by Delimiter feature.
• Choose a common delimiter like a comma, space, or tab.
• Split into a fixed number of columns for predictable data.
• Preview the results to avoid creating too many columns.
• Rename new columns immediately for clarity.
• Avoid splitting dates; use the date tools instead.

Group and Categorize

• Create new columns to group similar text values.
• Use the Replace Values feature for small groupings.
• For larger sets, use a reference table and merge queries.
• This simplifies reports and reduces visual clutter.
• Create bins for numeric data using the grouping feature.
• Categorize dates into fiscal periods or weeks.
• Ensure consistent naming across all categories.

Validate with Conditional Columns

• Add a new column based on rules and conditions.
• Flag outliers or unexpected values for review.
• Create categories like "High", "Medium", "Low".
• Validate data entry against expected ranges.
• Use custom column for more complex logic with M code.
• This adds a layer of data quality control.
• Filter your dataset based on these new flags.

Profile Your Data

• Use Column Profile in Power Query to see data distribution.
• Check the column quality indicators for errors and empties.
• Identify outliers in numeric data quickly.
• See a sample of the top and bottom values.
• This helps you decide which transformations are needed.
• Understand your data before you start building reports.
• Profile view is found on the View tab in Power Query.

Create a Reusable Template

• Save your cleaned query as a Power BI template file (.pbit).
• Document your steps within the Power Query Editor.
• Use parameters for values that might change.
• Build a standard set of cleaning steps for similar data sources.
• This saves time on future projects.
• Ensures consistency across reports and datasets.
• Share your template with your team.

Document Your Process

• Use the Advanced Editor to view the underlying M code.
• Add comments to your steps within the formula bar.
• Rename query steps to be clear and descriptive.
• This makes it easier to troubleshoot later.
• Helps others understand the transformations you applied.
• Documentation is key for maintaining reports over time.
• Good documentation turns a one-time fix into a repeatable process.

Refresh and Verify

• Always do a full data refresh after cleaning.
• Check your report visuals for expected results.
• Test measures and calculations with the new clean data.
• Set up a refresh schedule in the Power BI service.
• Monitor refresh errors, which can indicate new data issues.
• Clean data is not a one-time task but an ongoing practice.
• Trust your data, but always verify your results.