Normalization Engine for CMDB Data Integrity
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Solution Overview
Problem
ITIL-based Configuration Management Databases (CMDBs) face issues with data inconsistencies and duplication due to varying data sources, leading to poor data quality, manageability, and consistency, which hinder effective reconciliation of Configuration Items (CIs).
Innovation Solution
A Normalization Engine (NE) is introduced to ensure data consistency across the enterprise by normalizing CI attributes, collections, and relationships based on predefined rules, allowing for centralized, customizable, and uniform data management, which can be applied before or after data is written to the CMDB, using knowledge bases and rules-based plug-ins for extensibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is populated from multiple sources into the CMDB, then the CMDB can integrate diverse information, but data inconsistencies and duplication occur
Solution Approach 1:
The patent introduces a Normalization Engine as an intermediary component between data sources and the CMDB. This engine applies normalization rules to transform incoming data into a standardized format before insertion, acting as a mediator that reconciles differences from multiple sources while maintaining data consistency in the target database.
Solution Approach 2:
The Normalization Engine changes data parameters by applying transformation rules that standardize attribute values, data formats, and structures from various sources. This parameter transformation ensures that diverse input data conforms to a unified schema, resolving inconsistencies while preserving the ability to integrate multiple sources.
2Quantity of substance
If manual data entry and multiple data sources are used, then comprehensive data coverage is achieved, but data quality and manageability deteriorate
Solution Approach 1:
The Normalization Engine performs preliminary actions by pre-defining normalization rules and applying them automatically to incoming data. This preliminary processing standardizes data before it enters the CMDB, reducing the manual effort required for data management and improving overall data quality without compromising comprehensive coverage.
Solution Approach 2:
The system enables self-service data normalization through automated rule-based transformations. The Normalization Engine autonomously processes incoming data from multiple sources and manual entries, applying consistent normalization rules without requiring manual intervention for each data item, thereby maintaining data quality while achieving comprehensive coverage.
3Reliability
If reconciliation processes are applied to merge CIs, then duplicate CIs are reduced, but data quality inconsistencies remain
Solution Approach 1:
The Normalization Engine performs preliminary data standardization before reconciliation processes occur. By pre-normalizing incoming data according to defined rules, the engine ensures that data quality is improved upstream, reducing the burden on reconciliation processes and ensuring that merged CIs maintain high data quality standards.
Solution Approach 2:
The system implements feedback mechanisms where normalization rules are continuously applied and refined based on data quality outcomes. The Normalization Engine monitors data consistency and adjusts normalization parameters accordingly, creating a feedback loop that progressively improves both data consistency and quality throughout the CMDB.
Data Source
AI summary
Data is often populated into Configuration Management Databases (CMDBs) from different sources. Because the data can come from a variety of sources, it may have inconsistencies—and may even be incomplete. A Normalization Engine (NE) may be able to automatically clean up the incoming data based on certain rules and knowledge. In one embodiment, the NE takes each Configuration Item (CI) or group of CIs that are to be normalized and applies a rule or a set of rules to see if the data may be cleaned up, and, if so, updates the CI or group of CIs accordingly. In particular, one embodiment may allow for the CI's data to be normalized by doing a look up against a Product Catalog and/or an Alias Catalog. In another embodiment, the NE architecture could be fully extensible, allowing for the creation of custom, rules-based plug-ins by users and/or third parties.