High-quality data is essential for successfully applying machine learning or AI and building reliable, efficient systems. In retail, where quick decision-making and precision directly impact a company’s profitability, data quality is critical. However, data is rarely perfectly structured, consistent, or error-free. The success of machine learning and AI projects heavily depends on thorough data cleaning.
Data cleaning involves many details. Simple tasks include correcting typos, filling in missing values, handling duplicate rows, and assigning appropriate data types. More complex examples include aggregating data into consistent time intervals, such as summarizing events by the hour. In retail, these tasks come with additional industry-specific challenges, which STACC has extensive experience addressing.
Integrating data from multiple sources
Retail businesses often need to consolidate data from multiple sources, a process that is both complex and time-consuming.
For instance, data may come from different store units or subsidiaries using various POS systems. Pricing and customer data differ significantly between e-commerce, retail, and wholesale. For companies operating internationally, factors such as currencies, regulations, and market conditions further complicate data collection. These diverse sources must be harmonized to ensure effective use within a unified system.
Cleaning product data
Product data cleaning is often critical in retail and presents unique challenges across different data types.
Retail processes rely on a variety of product identifiers, such as barcodes, SKUs, or database IDs. These identifiers may vary across product sizes and colors. When building systems like recommendation engines, it’s essential to decide whether to analyze data at the model level or account for variations across different product versions.
If accurate inventory data is required for business objectives, data processing must align with inventory updates and reconcile data from multiple sources. Older product information structures may not meet modern standards. Additionally, outdated products and “phantom items” like plastic bags or deposit packages need to be removed to provide accurate data for further analysis.
Cleaning customer data
Customer data cleaning often revolves around privacy regulations and unique identification challenges.
It’s crucial to determine which personal data can be processed directly, which needs to be anonymized, and how it should be stored to comply with data protection laws.
Like product data, customer data may encounter identification issues. Different systems often use various identifiers such as email addresses, loyalty card numbers, personal identification codes, or database IDs. A single person may be registered multiple times with different contact details, and it’s common for family members to share a loyalty card. Additionally, legacy customer information may not align with current standards, complicating processing.
Cleaning transaction data
Efficient transaction data processing is vital in retail, as it directly affects analysis outcomes and business decisions.
Depending on the objective, transaction data may need to be transformed to allow calculations at the level of individual purchase lines or entire shopping carts. It’s also essential to account for cross-cart discounts and decide whether to use unit prices or total prices for purchased quantities.
Automation of transaction data processing requires consideration of data availability and latency. For example, transaction data might update in real-time or at midnight, with delays ranging from immediate availability to several days.
Additional data types may also need to be processed. For instance, e-commerce activity data, such as product page visits, might need to be merged with other customer data. Similarly, distinguishing between local and nationwide campaigns can be an essential step in cleaning and processing data.
How can STACC help?
Over the years, STACC has helped numerous retail businesses optimize their data cleaning processes and solve complex, industry-specific challenges. Our solutions not only organize data but also establish a solid foundation for building accurate and reliable AI systems.
If you want to assess whether your company’s data is ready for AI implementation or need guidance on how to get there, reach out to us. We’ll evaluate your data’s condition and create a plan to bring you closer to data-driven innovation.
Author: Andreas Vija
