In 2026, data cleaning and preparation (often called Data Wrangling) consume approximately 70-80% of an analyst’s time. With the rise of AI-driven supply chains, “clean” data is the prerequisite for avoiding “garbage-in, garbage-out” scenarios in predictive modeling.
1. Handling Missing Values and Outliers
Missing or extreme data points can skew lead-time predictions and safety stock calculations.
- Missing Values: Techniques include Imputation (filling gaps with mean, median, or mode) or using AI-driven predictive filling based on correlated features (e.g., estimating a missing shipping weight based on the product category).
- Outliers: Analysts must distinguish between errors (e.g., a “negative” inventory count) and genuine anomalies (e.g., a massive demand spike due to a viral social media trend). Methods like the Z-score or Interquartile Range (IQR) are used to detect and either cap or remove these extremes.
2. Data Normalization and Standardization
Ensuring data is on a consistent scale is vital for comparing different nodes in the supply chain.
- Normalization (Min-Max Scaling): Rescaling data to a range (typically 0 to 1). This is essential when training machine learning models that include disparate units, like “units sold” vs. “total weight.”
- Standardization (Z-score Scaling): Centering data around a mean of 0 with a standard deviation of 1.
- Unit Conversion: Converting all measurements to a global standard (e.g., all weights to Kilograms, all currencies to USD) to ensure “apples-to-apples” comparisons.
3. Removing Duplicates and Errors
Duplicate records often occur during the integration of multiple systems (e.g., merging ERP and WMS data).
- De-duplication: Using “Fuzzy Matching” logic to identify records that are nearly identical (e.g., “Supplier Inc.” vs. “Supplier, Incorporated”) and merging them into a single master record.
- Validation Rules: Implementing automated checks to catch logical errors, such as a “delivery date” that occurs before a “ship date.”
4. Data Type Conversion
Data must be in the correct “language” for analytical tools to process it.
- Date/Time Formatting: Converting text strings into standardized ISO 8601 date formats (YYYY-MM-DD) to allow for time-series analysis and “Days of Supply” calculations.
- Numerical vs. Categorical: Ensuring “Postal Codes” are treated as strings (categorical) rather than numbers to prevent the software from accidentally performing mathematical operations on them.
5. Creating Calculated Fields and Derived Metrics
Raw data is rarely sufficient; analysts must create new metrics to provide strategic value.
- Lead Time:
[Delivery Date] - [Order Date]. - Inventory Turnover Ratio:
[Cost of Goods Sold] / [Average Inventory]. - On-Time Performance: A binary calculated field (1 if
Actual Delivery <= Promised Delivery, else 0) used to aggregate supplier reliability. - Seasonality Indices: Calculated by comparing monthly sales against a yearly average to identify recurring demand patterns.
Tools for 2026
- Low-Code Platforms: Alteryx and Trifacta automate these steps via visual workflows.
- Python Libraries:
PandasandNumPyremain the industry standard for programmatic data cleaning. - Cloud Native: Snowflake Data Quality features allow for “cleansing at the source” before data even reaches the analyst.