What is the Problem? Our e-commerce platform gathers a vast array of data—from user interactions and customer profiles to purchase histories and order details. This data is sourced through diverse mechanisms and integrated with multiple SaaS products. However, our current method of analyzing these data points in isolation leads to several operational challenges. We lack a systematic approach to data consolidation, which affects the reliability of our data, hampers findability, increases processing costs, and diminishes overall efficiency. Each analyst’s unique method of handling data results in inconsistent outputs for stakeholders, emphasizing the need for a standardized data framework to enhance our marketing and advertising analytics.
What is the Solution? Understanding Data Management Fundamentals:
Before diving into the solution, let’s clarify some essential concepts in data management:
Input Data: Refers to data entering a system, such as customer information input during an ecommerce checkout process.
Output Data: Refers to data that a system outputs to users, such as displaying current weather conditions in a weather app.
The scalability challenges of traditional Data Warehouses, particularly with the increasing volume and diversity of data, necessitate a more flexible solution.
Here, the concept of a Data Lakehouse becomes pertinent. It merges the structured storage capability of Data Warehouses with the vast, schema-less nature of Data Lakes. This hybrid architecture supports both structured and unstructured data, facilitating advanced analytics and accommodating machine learning workflows, all while optimizing cost and maintenance efficiency.
Considering we are dealing with structured data and the level data required for our e-commerce analytics problem; the solution is to create a Data Warehouse Architecture.
There are 4 main layers:
Data Ingestion Data Processing Machine Learning Insights
Data Ingestion:
We consolidate data from a variety of sources including Google Analytics, Salesforce, various Google and non-Google cloud services (e.g., AWS S3, Microsoft Azure), and CRM or POS systems.
Using Google Cloud’s BigQuery data transfer service or API and other SaaS connectors, we manage data ingestion. This includes batch processing for large, non-time-sensitive datasets and streaming for real-time data processing.
Data Processing:
Our objective with data processing is that we ensure consistency, quality, and reliability of the ingested data, which involves cleaning, reformatting, and enriching the data.
We can utilize Google Cloud’s Data Fusion for constructing scalable ETL pipelines and Dataflow for stream processing, enabling real-time data transformation and enrichment.
Machine Learning:
Our data scientists and ML teams leverage the processed data to build predictive models and analytical tools within BigQuery, using technologies like AutoML for automated model training, BigQuery ML for building and deploying machine learning models directly within the database, and Vertex AI for custom model training and deployment.
Insights:
The consolidated, cleaned , curated data in BigQuery can be integrated with the SAAS platforms such as Google Analytics or other BI Platforms. The data is also available for analytics consumption in Looker.
With the Data Warehouse Architecture;
Data Analytics team can create comprehensive insights by consolidating marketing and advertising data in BigQuery. Business Stakeholders can get real-time insights into marketing performance. Data Science or Machine Learning teams can create models for business views such as customer segmentation, customer lifetime value , product recommendations, purchase predictions. Based on these models, marketing team can activate multiple platforms such as email marketing or targeted advertising. Data Analytics team can create insights and dashboards that provides higher visibility into customer behavior , so marketing and content team can improve customer experience with initiatives such as personalization and email marketing.
To navigate the complexities of e-commerce data effectively, it is essential to establish a well-defined organizational structure or workflow in your data management practices. This structured approach enhances data quality, improves data findability, and ensures data reliability, which in turn reduces processing costs and elevates the quality of insights and machine learning models derived from the data. The detailed Data Warehouse Architecture for Marketing data presented here is just one example of how to achieve these improvements. There are numerous other methodologies and frameworks available, one of which is the highly recommended Data Lakehouse architecture. We will explore the Data Lakehouse model in detail in a separate article, providing further insights into its benefits and implementation strategies.
Linkedin Article: https://www.linkedin.com/pulse/data-warehouse-architecture-marketing-insights-neal-akyildirim-fnqse/