Master Data Vector Consistency Checks for Business Process Execution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Maintaining accurate and consistent master data across an organization is challenging due to issues with data quality, classification, and reconciliation, particularly in business processes that rely on multiple interconnected systems, leading to potential inconsistencies and inefficiencies.
Innovation Solution
A computer-implemented method defines a business process master data vector that includes coordinates for business object node fields, with instance vectors indicating the status of master data, allowing for analysis of master data consistency and providing feedback on updates to ensure that business processes meet predetermined consistency thresholds, and calculating similarity metrics between processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If master data is stored in different data systems across an organization, then data accessibility and functionality are improved, but data consistency and quality deteriorate
Solution Approach 1:
The patent segments master data into distinct business objects (customer, product, material, etc.), each represented as a separate vector with standardized coordinates. This segmentation allows data to be distributed across multiple systems while maintaining consistency through a unified vector representation framework that enables comparison and reconciliation across systems.
Solution Approach 2:
The patent introduces master data vectors as an intermediary representation layer between different data systems. These vectors serve as a common language that translates diverse data formats into a standardized structure, enabling consistency checks and reconciliation without requiring direct integration between all systems.
2Adaptability or versatility
If multiple versions of the same master data are maintained across different systems, then system autonomy and flexibility are improved, but data accuracy and organizational efficiency deteriorate
Solution Approach 1:
The patent implements feedback mechanisms through consistency checks that compare master data vectors across systems. When inconsistencies are detected (e.g., a customer already has a mortgage), the system provides feedback to prevent duplicate actions, thereby maintaining data accuracy while allowing systems to operate autonomously.
Solution Approach 2:
The patent performs preliminary consistency checks before executing business processes by evaluating whether required master data coordinates are satisfied. This preliminary action prevents downstream errors and ensures data accuracy is maintained across autonomous systems that may have different versions of the same data.
3Reliability
If master data changes frequently to reflect current business conditions, then data relevance is improved, but data stability and reconciliation difficulty worsen
Solution Approach 1:
The patent represents master data as vectors with coordinates that can be efficiently updated. When data changes, only the affected vector coordinates need to be modified rather than reprocessing entire data sets. This parameter-based approach maintains data relevance while simplifying reconciliation through standardized vector comparison operations.
4Measurement precision
If comprehensive master data validation is performed across all business processes, then data quality is improved, but processing time and computational resources worsen
Solution Approach 1:
The patent performs partial validation by checking only the specific master data coordinates required for each business process rather than validating all master data comprehensively. This selective approach maintains data quality for critical fields while preserving processing speed by avoiding unnecessary validation of unrelated data.
Solution Approach 2:
The patent segments validation into process-specific coordinate checks rather than organization-wide comprehensive validation. Each business process validates only the master data vectors relevant to its execution, improving processing efficiency while maintaining data quality through targeted validation of critical coordinates.
Data Source
AI summary
A business process master data vector for a business process of an organization can include master data coordinates corresponding to a plurality of business object node fields of a plurality of business objects involved in the business process. Values of the master data coordinates can indicate whether a corresponding business object node field is mandatory for successful execution of the business process. Master data instance vectors corresponding to the plurality of business objects can be evaluated such that a master data instance vector includes instance coordinates indicating whether a corresponding business object node field contains a master data value. The business process can be analyzed using the business process master data vector and/or the master data instance vectors. Related systems, processes, and articles of manufacture are also described.