Data foundations in e-commerce and retail
A logistics manager uses AsscherAi to investigate warehouse inventory discrepancies by querying the inventory management system and the enterprise resource planning system, then identifies the root cause of the discrepancy and takes corrective action to prevent future errors.
A category manager comparing two suppliers for a fast-moving consumer goods product will often find that the purchase order table does not contain all the information needed to make a decision, highlighting the challenges of data foundations in e-commerce and retail.
Challenges in e-commerce and retail
The nature of e-commerce and retail, with its fast-moving assortments and direct customer signal, makes it harder to maintain accurate and consistent data. Assortment decisions, for example, require data on product lifecycles, seasonal trends, and customer preferences, which can be difficult to track and analyze. Returns and their root causes are another area where data can be incomplete or inconsistent, making it challenging to identify patterns and trends. Promotion effectiveness is also hard to measure, as it depends on various factors such as pricing, advertising, and customer behavior.
Most people get wrong the idea that data from e-commerce and retail systems can be easily integrated and analyzed, but in reality, it requires careful planning and execution to ensure that the data is accurate, complete, and consistent. The complexity of e-commerce and retail data, with its multiple sources and formats, makes it prone to errors and inconsistencies, which can lead to incorrect insights and decisions.
Field-level meaning and documentation
Column names in a database table are often not sufficient to understand the meaning and context of the data, which is why field-level documentation is essential. This documentation should explain the data type, format, and any business rules or constraints that apply to the data. For example, a column named "price" may seem straightforward, but it may not be clear whether it includes taxes, discounts, or other adjustments. Field-level documentation can help to avoid misunderstandings and ensure that the data is used correctly.
A shift supervisor checking the maintenance log for a warehouse management system may find that the data is not properly documented, leading to confusion and errors. In such cases, it is essential to review and update the documentation to ensure that it accurately reflects the data and its meaning.
Systems that get left out of scope
When implementing a data analytics system, it is common to focus on the main systems and data sources, such as the enterprise resource planning (ERP) system or the customer relationship management (CRM) system. However, other systems, such as the warehouse management system or the transportation management system, may be left out of scope, leading to partial answers and incomplete insights. These systems often contain critical data that can help to understand the entire supply chain and make better decisions.
For instance, a logistics manager trying to optimize the delivery route for a new product may not have access to the data from the transportation management system, which can lead to suboptimal decisions. By including all relevant systems in the scope of the data analytics system, organizations can gain a more comprehensive understanding of their operations and make better decisions.
To learn more about how AsscherAi can help you to integrate data from multiple systems, visit our website at www.asscherai.com.
Gaps in data that teach a model the wrong lesson
Stockouts, backfills, and other gaps in data can teach a model the wrong lesson, leading to incorrect insights and decisions. For example, if a product is out of stock for a period, the sales data for that period may not accurately reflect the demand for the product. Similarly, backfills, which are used to fill gaps in data, can introduce biases and errors into the model. These gaps and biases can be difficult to detect and correct, especially in large datasets.
A data analyst trying to build a predictive model for sales forecasting may not be aware of the gaps and biases in the data, which can lead to incorrect predictions and decisions. By carefully reviewing and cleaning the data, organizations can minimize the impact of these gaps and biases and build more accurate models.
Limitations of data analytics in e-commerce and retail
While data analytics can provide valuable insights and help organizations to make better decisions, it is not a silver bullet for all problems in e-commerce and retail. There are situations where data analytics may not be the best approach, such as when the data is incomplete, inconsistent, or biased. In such cases, other approaches, such as expert judgment or market research, may be more effective. Additionally, data analytics should not be used as a replacement for human judgment and decision-making, but rather as a tool to support and inform these processes.
If you have questions about how to apply data analytics in your organization, or would like to learn more about our services, please visit our contact us page to get in touch with our team.
Frequently asked questions
What are the common challenges of data foundations in e-commerce and retail?
Common challenges include maintaining accurate and consistent data, tracking product lifecycles, and measuring promotion effectiveness.
Why is field-level documentation essential for data analytics?
Field-level documentation helps to avoid misunderstandings and ensures that data is used correctly by explaining the data type, format, and business rules.
What happens when systems are left out of scope in data analytics implementation?
Leaving systems out of scope can lead to partial answers and incomplete insights, as critical data from other systems is not considered.
How can gaps in data affect the accuracy of predictive models?
Gaps in data can teach a model the wrong lesson, leading to incorrect predictions and decisions, and careful data review and cleaning are necessary to minimize these effects.