Data cleaning and validation: The first step involves cleaning and validating the data. Some important validation checks include:

Check for duplicate records: The dataset should be cleaned to remove any duplicate sales transactions. This can be done by identifying duplicate rows based on primary identifiers like order ID, customer ID etc.

Check for missing or invalid values: The dataset should be scanned to identify any fields having missing or invalid values. For example, negative values in quantity field, non-numeric values in price field, invalid codes in product category field etc. Appropriate data imputation or error correction needs to be done.

Outlier treatment: Statistical techniques like Interquartile Range can be used to identify outlier values. For fields like quantity, total sales amount – values falling outside 1.5 IQR from upper and lower quartiles need to be investigated. Appropriate corrections or exclusions need to be made.

Data type validation: The data types of fields should be validated against the expected types. For example, date fields shouldn’t contain non-date values. Appropriate type conversions need to be done wherever required.

Check unique fields: Primary key fields like order ID, customer ID etc should be checked to not contain any duplicate values. Suitable corrections need to be made.

Data integration: The cleaned data from multiple sources like online sales, offline sales, returns etc need to be integrated into a single dataset. This involves –

Identifying common fields across datasets based on descriptions, metadata. For example – product ID, customer ID, date fields would be common across most datasets.

Mapping different name/codes used for same entities in different systems. For example, different product codes used by online vs offline systems.

Resolving conflicts if same ID represents different entities across systems or if multiple IDs map to same real world entity. Domain knowledge would be required.

Harmonizing datatype definitions, formatting and domains across systems for common fields. For example, standardizing date formats.

Identify related/linked records across tables using primary and foreign keys. Append linked records rather than merging wherever possible to avoid data loss.

Handle missing field values which are present in one system but absent in other. Appropriate imputation may be required.

Data transformation and aggregation: This involves transforming the integrated data for analysis. Some key activities include:

Deriving/calculating new attributes and metrics required for analysis from base fields. For example, total sales amount from price and quantity fields.

Transforming categorical fields into numeric for modeling. This involves mapping each category to a unique number. For example, product category text to integer category codes.

Converting date/datetime fields into different formats needed for modeling and reporting. For example, converting to just year, quarter etc.

Aggregating transaction-level data into periodic/composite fields needed. For example, summing quantity sold by product-store-month.

Generating time series data – aggregating sales by month, quarter, year from transaction dates. This will help identify seasonal/trend patterns.

Calculating financial and other metrics like average spending per customer, percentage of high/low spenders etc. This creates analysis-ready attributes.

Discretizing continuous valued fields into logical ranges for analysis purposes. For example, bucketing customers into segments based on their spend.

Data enrichment: Additional contextual data from external sources is integrated to make the sales data more insightful. This includes:

Demographic data about customer residence location to analyze regional purchase patterns and behaviors.

Macroeconomic time series data about GDP, inflation rates, unemployment rates etc. This provides economic context to sales trends over time.

Competitor promotional/scheme information integrated at store-product-month level. This may influence sales of same products.

Holiday/festival calendars and descriptions. Sales tend to increase around holidays due to increased spending.

Store/product attributes data covering details like store size, type of products etc. This provides context for store/product performance analysis.

Web analytics and CRM data integration where available. Insights on digital shopping behaviors, responses to campaigns, churn analysis etc.

Proper documentation is maintained throughout the data preparation process. This includes detailed logs of all steps performed, assumptions made, issues encountered and resolutions. Metadata is collected describing the final schema and domain details of transformed data. Sufficient sample/test cases are also prepared for modelers to validate data quality.

The goal of these detailed transformation steps is to prepare the raw sales data into a clean, standardized and enriched format to enable powerful downstream analytics and drive accurate insights and decisions. Let me know if you need any part of the data preparation process elaborated further.