Decoding Data Historization with SCD Support: Simplified with AnalyticsCreator

Data historization, the process of recording data changes over time, is the cornerstone of effective data warehousing. It allows for trend analysis, understanding data progression, and regulatory compliance. However, manual implementation can be complex, requiring extensive coding and being susceptible to errors.  Only when you have mastered this topic, can you utilize the Advanced Analytics functionality and AI. 

Before we dive into the capabilities of AnalyticsCreator’s Historization Wizard, let’s discuss some key considerations:  

• Preparation: Before starting the historization process, ensure that your data is clean and well-structured. This includes removing duplicates, handling missing values, and ensuring data types are consistent across your dataset. 
• Performance Considerations: Large datasets can significantly slow down the historization process. Consider strategies to manage this, such as partitioning your data or using incremental loads. 
• Security and Compliance: When dealing with sensitive data like patient health metrics, it’s crucial to ensure that your historization process complies with relevant data protection regulations. This could include anonymizing patient data or implementing strict access controls on your historical data. 

AnalyticsCreator's Historization Wizard significantly streamlines this process. It provides built-in support for Slowly Changing Dimensions (SCDs), a crucial feature for managing master data, which changes slowly. Let’s dive into a practical example, to demonstrate the capabilities of this feature. 

Consider you're a product manager at a healthcare company, aiming to track how patient health metrics evolve over time. You want to monitor changes in patient vitals, medication adherence, and demographic information. Manually tracking these changes in your "Patients" table would be a daunting task. But with AnalyticsCreator and its SCD support, it becomes straightforward!  

Stepping into the Time Machine: Utilizing the Historization Wizard 

AnalyticsCreator simplifies the process in 5 simple steps: 

4. SCD Magic: This is where the magic happens. Select the suitable SCD type for each column:

• SCD2 (default): Perfect for tracking changes in demographics like location, age group, or chronic conditions. It generates new rows for changes, closing the previous validity period, thereby preserving historical context. 

• SCD1: Updates existing rows in the historical table, reflecting the latest values but sacrificing individual change details. Ideal for columns like “Last_Checkup_Date” or "Current_Medication."

• SCD0: Keeps the original “Patients” table unchanged, less commonly used for historical analysis. 

5. Key Selection: Choose “Patient_ID” as the primary key to uniquely identify each patient over time.

Beyond the Basics 

This example merely touches the surface. AnalyticsCreator equips you with: 

• Customizable Validity Periods: Set unique validity periods for different columns, offering granular control over historical analysis. 
• Advanced Change Detection: Formulate expressions for granular control over change detection logic. 
   

Unlocking Historical Insights 

By harnessing the power of the Historization Wizard and SCD support, you can:  

• Reduce Development Time: Concentrate on strategic tasks, not manual coding. 
• Improve Data Quality: Ensure consistent and accurate historical data. 
• Increase Flexibility: Easily adapt historization to evolving business needs. 
• Make Informed Decisions:  Gain valuable insights from historical trends and changes in patient vitals, medication adherence, and demographics. 
   

Remember, this is just the start of your data historization journey. Harness the power of AnalyticsCreator and SCD support to metamorphose your data warehouse into a time machine, unveiling valuable insights concealed within your historical data!