Capture Context and Improve Predictions with Historical Data
The Power of Historical Context in Data Science
I recently transitioned in my career from a purely data science role to one that leans more into engineering. My current team is embarking on an exciting project to build a robust data warehouse designed to meet the growing needs for business intelligence (BI) and machine learning (ML). I chose this path because I recognized a golden opportunity to leverage the insights I’ve gained in data science to shape a data warehouse focused on future needs.
In my six years of experience in various data science roles, I’ve noticed a recurring issue: data infrastructure often falls short of facilitating effective data science work. Many data warehouses or lakehouses house tables, including the all-important facts and dimensions, that miss essential fields or structural elements crucial for creating high-performing machine learning models. The most significant drawback? These tables typically only capture the current state of an observation, neglecting the history that can offer vital context.
Addressing the Data Challenge with Slowly Changing Dimensions
Now, why is this historical data so crucial? Let’s explore the concept of Slowly Changing Dimensions (SCD), a data modeling technique that tackles this very issue. By the end of this read, you’ll grasp how storing historical data can significantly enhance your model’s performance, along with actionable strategies for implementation tailored to your use cases.
Why History Matters in Data Infrastructure
Suppose you’re trying to predict customer behavior for a local café in town, let’s call it “Brewed Awakenings.” By merely looking at the current orders and customer preferences, you might miss the fact that customer tastes shift with the seasons or in response to local events, like the annual “Cherry Blossom Festival.” Tracking changes in customer choices over time—like how a summer special latte became a seasonal favorite—could provide deeper insights into trends and help tailor marketing strategies effectively.
When your data environment captures this historical context, it not only enriches predictive modeling but also allows businesses to adjust their operations dynamically in response to customer needs.
Strategies to Implement Slowly Changing Dimensions
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Identify Relevant Dimensions: Pinpoint which dimensions in your datasets require tracking over time. Consider things like customer profiles, product variations, or even environmental factors.
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Choose the Right SCD Type: Understand the different types of SCDs (Type 1, Type 2, Type 3) and choose the one that best suits your data’s nature. For example, if you want to keep all historical records, Type 2 might be your best bet.
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Update Your Data Pipeline: Implement this strategy into your existing data processes. Ensure your ETL (Extract, Transform, Load) processes accommodate the integration of historical data seamlessly.
- Test and Iterate: Start small, analyze results, and refine your approach based on what you learn. It’s essential to keep your team engaged in reviewing the impacts of these changes.
Conclusion
Incorporating historical data into your data modeling is not just a technical enhancement; it’s a strategic shift that can unlock new opportunities for insights and predictions. By understanding the past, organizations can take informed steps forward, preventing missed chances and optimizing both service and product offerings.
The AI Buzz Hub team is excited to see where these breakthroughs take us. Want to stay in the loop on all things AI? Subscribe to our newsletter or share this article with your fellow enthusiasts.
