Zoho DataPrep is a self-service data preparation tool that integrates directly within Zoho Analytics. It allows you to cleanse, transform, and enrich your data before analysis, helping you ensure that your reports are built on accurate and well-structured information.

Accessing DataPrep in Zoho Analytics

The DataPrep add-on can be invoked in two ways:

• During import: When importing a file, select the Prepare Data option to apply transformations before the table is created in your workspace.
• After import: For existing tables, open the More menu and choose View Data Quality or Prepare Data to refine data that is already loaded.

Assessing Data Quality

DataPrep provides visual data quality indicators at the column level. A colour-coded bar displays the proportion of:

• Valid values (shown in green)
• Invalid values (shown in red)
• Missing or empty values (shown in grey)

The system also surfaces intelligent suggestions where it detects common data quality issues, making it straightforward to identify and address problems quickly.

Available Data Transformations

The DataPrep add-on within Zoho Analytics supports a range of column-level and dataset-level transformations:

Column Operations

• Replace values
• Trim whitespace
• Truncate text
• Round off numeric values
• Add a prefix or suffix
• Fill empty cells with a default value
• Change text case (upper, lower, title)
• Cluster and merge similar values
• Sort, rename, or delete columns

Dataset Operations

• Deduplicate rows
• Filter records by condition
• Unpivot columns into rows

Understanding the Ruleset

Each transformation you apply is recorded as a rule in the Ruleset pane. Rules are listed in the order they were applied and can be edited, disabled, or removed at any time. When you modify a rule, all dependent rules are recomputed automatically, keeping your data pipeline consistent.

Sampling Strategies

When preparing large datasets, DataPrep offers four sampling approaches to help you work efficiently:

• Initial: Uses the first rows of the dataset.
• Random: Selects a random sample across the full dataset.
• Erroneous: Focuses on rows that contain data quality issues.
• Column-based: Targets rows based on a specific column's values.