30+ YEARS EXPERTISE IN CUSTOMIZED SOFTWARE AND SOLUTION ENGINEERING

Data Cleansing & Enrichment

Accurate and up-to-date asset data is crucial in reducing potential risks and improving overall performance of operations. Complying with regulations is also important and can be achieved by organizing data using standard taxonomy, including equipment class and incorporating bills of material.

Zircoo’s highly efficient method of achieving ideal asset data involves a meticulously executed process of thorough cleansing, precise creation, thorough consolidation, and rigorous classification. Our team of skilled content engineers delivers this service in a timely and cost-effective manner, ensuring maximum satisfaction for our clients.

How Zircoo Can Help

Zircoo’s standard practice comprehensively assesses all facets of your organization’s data:

  • Identify mission-critical issues requiring baselining to pinpoint areas needing improvement.
  • Aid in crafting tailored business rules aligning with your organization’s unique requirements.
  • Assist in implementing a data quality process enabling continuous measured enhancements over time, leveraging both technological tools and procedural improvements.
  • Covering areas such as standardization, deduplication, rationalization, enrichment, enhancement, and monitoring, Zircoo’s Data Quality program and solution offer a holistic approach to optimizing data integrity and efficiency.

BENEFITS OF DATA CLEANSING:

  • Remove duplicates for cost savings
  • Clean data for searchability and standards
  • Prioritize strategic data quality for digital transformation
  • Identify decommissioned data and systems
  • Implement master data governance
  • Make better decisions with accurate data

Zircoo's Proprietary 4C’s Data Cleansing Methodology

Zircoo employs its 4Cs framework to uphold data integrity, particularly for Assets and Materials:

  • Classification: We meticulously assign the appropriate class to each object, ensuring accurate categorization.
  • Cleansing: Employing detailed class and associated characteristics at every level, we conduct parsing and standardization of legacy data against the data dictionary, enhancing data consistency and accuracy.
  • Creation (enrichment): We enhance the data by rectifying errors and supplementing missing information, enriching the dataset.
  • Consolidation: We integrate all objects to form a coherent dataset, consolidating data from various sources and eliminating duplicates, ensuring a unified and meaningful dataset.