Effective data transformation is essential for turning raw data into meaningful insights. SAP Lumira, a leading self-service data visualization tool, offers robust data transformation capabilities that allow users to prepare and refine datasets before analysis. By leveraging these transformation techniques, business analysts and data professionals can enhance data quality, combine disparate sources, and create a foundation for accurate and insightful visualizations.
This article explores the key data transformation techniques available in SAP Lumira, enabling users to optimize their data preparation processes and drive better business outcomes.
Raw data is often inconsistent, incomplete, or unstructured. Data transformation addresses these challenges by:
- Cleaning and normalizing data
- Combining datasets for comprehensive views
- Creating calculated metrics for deeper analysis
- Filtering and sorting data for focus
SAP Lumira’s transformation tools help users perform these tasks with an intuitive, code-free interface, empowering business users without deep technical expertise.
- Renaming Columns: Rename dataset fields for clarity and consistency.
- Changing Data Types: Convert columns to appropriate data types (e.g., string to date, number to currency) to ensure correct interpretation.
- Handling Nulls and Missing Values: Replace nulls with default values or remove incomplete records to maintain data integrity.
¶ 2. Filtering and Sorting
- Row Filtering: Exclude irrelevant data by applying filters based on specific criteria (e.g., date ranges, regions, product categories).
- Sorting Data: Organize rows in ascending or descending order to identify trends or outliers.
¶ 3. Creating Calculated Columns
Calculated columns are custom fields derived from existing data, allowing for advanced analysis:
- Use mathematical expressions, conditional logic, and string functions.
- Example: Create a Profit Margin column using the formula
(Revenue - Cost) / Revenue.
- Conditional columns can classify data, such as assigning “High,” “Medium,” or “Low” categories based on thresholds.
- Joins: Merge two datasets based on common keys (e.g., Customer ID, Product Code) to enrich analysis.
- Unions: Append rows from similar datasets to create consolidated data views.
SAP Lumira supports inner joins, left joins, and full outer joins to meet various integration needs.
¶ 5. Grouping and Aggregation
- Group data by specific dimensions to summarize information.
- Aggregate measures using sum, average, count, min, or max functions.
- Example: Group sales data by region and calculate total revenue per region.
¶ 6. Pivoting and Transposing
- Pivot data to restructure datasets for better visualization.
- Transpose rows and columns to switch perspectives on the data.
Imagine you have sales data with columns for Date, Product, Region, Revenue, and Cost. Using SAP Lumira:
- Rename columns for consistency (e.g., “Rev” to “Revenue”).
- Convert Date strings to actual date format.
- Filter out records before the current fiscal year.
- Create a calculated column “Profit” = Revenue - Cost.
- Join with a customer dataset to add demographic details.
- Group data by Region and Product category to analyze performance.
- Sort results by descending profit to identify top-performing segments.
- Preview Changes: Use the data preview pane to see transformations in real-time.
- Document Steps: Keep track of transformations to ensure reproducibility.
- Use Built-In Functions: Leverage SAP Lumira’s rich function library for calculations.
- Validate Results: Regularly check transformed data against source data to avoid errors.
Mastering data transformation techniques in SAP Lumira is critical for unlocking accurate insights and delivering impactful visualizations. From basic cleansing to complex joins and calculations, SAP Lumira provides a comprehensive toolkit to refine and enrich your data with ease.
By investing time in proper data preparation, you lay a solid foundation for data-driven decision-making and ensure that your analyses are both trustworthy and actionable.