Temporal Data Management
Most enterprise systems will only show you how your data looks today. With the 3D Platform, you can specify a query date, and all data will be returned as if it were that day. This adds a new dimension to your data that is critical for queries but also critical for AI to analyze how your data is changing over time. The temporal features embedded within the platform specifically address the time dimension, enabling organizations to manage data that evolves over time with precision and consistency.
Experience a New Data Dimension
​​Examples of Temporal Data
Temporal data is essential for various business operations. Here are some key examples:
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Payroll Data: Compensation parameters for staff change over time, such as roles, rates, and rules.
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Property Tax: Tax assessments depend on property values and applicable rates that vary annually.
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Health Benefits: Benefits are tied to enrollment plans and the limits within those plans, which change over time.
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Oil and Gas Data: Temporal data is crucial in tracking measurement parameters like plate orifice sizes that impact volume calculations.
Key Features of Temporal Data
A system without temporal capabilities can only capture the present state of data. In contrast, the 3D Platform’s temporal capabilities allow for historical data to be maintained and updated based on effective dates. For example, corrections to past payroll periods can be applied retroactively without affecting the current data.
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Temporal Resolutions
Temporal data in the 3D Platform is captured at various resolutions—year, month, day, or even second—depending on the system’s requirements. The resolution determines how temporal segments are created and managed. For instance, in a property tax system with yearly resolution, changes within the same year are consolidated into a single segment.
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Conclusion
The 3D Platform’s temporal data management is essential for creating systems that can adapt to changes over time. By distinguishing between temporal and non-temporal data and standardizing temporal methods across all systems, the platform accelerates development and enables seamless data exchange—even for temporal data. This consistent approach ensures that organizations can manage their evolving data needs while maintaining high data integrity across interconnected systems.