Reliable master data
Reduce ambiguity, duplication, and conflicts in item and asset records through clear, enforceable rules.
Master data engineering
Assessment & data standardization
Classification & coding
Structured categories & identifiers
Item specifications
Standard attributes by category
Automated code generation
Unique, reliable identifiers
Core capabilities
Reliable information starts with structure.
Arraye goes beyond code creation: it establishes classification logic, naming rules, and data quality practices that make information consistent and reusable across the organization.
Scalable classification
Build a coherent classification structure that can grow with catalog diversity and changing business needs.
Automated coding
Generate and validate new codes against defined standards instead of relying on ad hoc decisions.
A shared information language
Create a consistent foundation for using master data across business processes and connected systems.
Information engineering journey
From data assessment to lasting standards
Start by understanding existing data, then define standards, normalize structures, automate coding, and prepare information for connected systems.
01
Assess
Review existing data and identify inconsistencies
02
Design standards
Define classification, coding, and master data specifications
03
Normalize & automate
Align existing records and configure code-generation rules
04
Integrate & maintain
Prepare data exchange and sustain the standards
Information integration with other systems
Standardized codes and attributes can be mapped and exchanged with related systems according to organizational architecture and requirements.
Arraye domains
From data analysis to standardized coding.
Explore the core disciplines of information engineering, from data assessment to code generation and system integration.
Module 01
Master Data Assessment & Cleansing
Understand the current state before redesigning the structure
Review item and asset files, tables, and databases to identify duplicate, incomplete, conflicting, and inconsistently recorded data.
Data quality audit
Assess the quality, completeness, and consistency of existing information before coding standardization.
Duplicate record detection
Identify similar items recorded under different names, attributes, or codes to determine where consolidation or correction is needed.
Technical attribute analysis
Review the attributes that distinguish items and determine the essential information required for each category.
Current-state report
Turn fragmented data into a clear view of issues, risks, and remediation priorities.
Standardized recordsTraceable workflowsIntegrated data
Automated code generation
Rule-based identifier generation
Standard identifier
Unique, reliable code
Generation method
Classification rules
Code validation
Unique identifier
Standardized code generation
Ready for connected systems
Accurate field data capture
From raw data to standardized identifiers
Automate coding rules to move new identifier creation away from ad hoc decisions. Validation and consistent rules help keep codes aligned with classification and item master data.
Generate codes from approved rules
Validate uniqueness and format before saving
Align codes with classification and master data
Keep history and prepare data for integration
Operational value
Standards that create operational value.
Build a shared information language for better search, more reliable decisions, and scalable enterprise systems.
Documented standards
Clear, reviewable rules for classification, naming, master data, and code generation.
Fewer data inconsistencies
Consistent structures and standardized attributes make similar items easier to distinguish and retrieve.
Integration-ready data
Standardized identifiers and attributes provide a foundation for consistent data exchange between connected systems.
Arraye — structure behind information
Give your organization’s data a structure.
Request an Arraye introduction to review your current coding structure and explore a practical standardization path.