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Asset Data Life Cycle

Impact of Poor Asset Data Integrity

  • Cannot trust Asset data quality
  • Lack of data uniformity across organization
  • Data not available in a reportable format
  • Cannot perform comprehensive data analysis
  • Inability to distribute information to stakeholders
  • Regulatory Compliance is difficult & cumbersome
  • Unable to get early alerts
  • Decisions are made solely on judgment

Good quality Asset data is hard to get

  • Voluminous data from disparate data sources
  • No common elements to link data sources
  • Improper identification of good & bad data
  • Laborious process of cleaning data
  • Lack of knowledge on usage of data
  • Cultural challenges in gathering data
  • Complex data validation
  • Training of end users
  • Lack of standardization in Taxonomy
  • Lack of standardization for asset hierarchy, component, hierarchy, failure codes, item master, location master etc.

Issues with Asset Data.

  • Consistency
  • Accuracy
  • Integrity
  • Uniformity
  • Completeness
  • Accessibility
  • Matching and Merging

How we can help with Asset Data Quality Issues...

Asset Data Audit & Quality Analysis

  • Data Sources Analysis (Application, Data Entities, Data Elements)
  • Data Quality Assessment (Availability, Accuracy, Usability, Sufficiency, Integrity)
  • Data Security, Accessibility & Visibility Analysis

Asset Data Development

  • Classification
  • Standardization
  • Naming convention
  • Normalization
  • Combining disparate data sources
  • Analyzing/building/reviewing integrity across disparate sources

Asset Master Data Development

  • Asset Master
  • Asset Hierarchy Catalog with Specifications
  • Asset Component Catalog
  • Location Hierarchy Master
  • Failure, Cause, Remedy Master
  • PM Code Master
  • Spare Part Item Master Cataloging

Asset Data Cleansing & Validation

  • Review & validate existing data based on the quality checks & business rules
  • Translate “Text/Notes” into relevant failure codes
  • Make changes necessary to the data
  • Build scripts to automate data scrubbing wherever possible
  • Check and fix abnormalities
  • Perform validation
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