Data Quality for Data Warehouse

Accurate analysis and reporting depends on quality data

Ever walk into a high-level meeting to review company performance only to learn that no two reports reflect the same revenue figures? A common scenario in global companies, such reports, ostensibly compiled from a common set of data, reflect entirely different numbers. Because no one ever can know which, if any, figures are accurate, important decisions are postponed and crucial initiatives are delayed. And that costs you money.

The underlying problem is inconsistent data housed in a patchwork of operational systems and enterprise applications. These data sources can have scattered or misplaced values, outdated and duplicate records, and inconsistent data standards and formats across customers, products, transactions, financials and more. Businesses require a solution that integrates data quality processes into all their enterprise applications easily to maintain the accuracy and value of the business-critical operational information that impacts all down-stream applications and strategic decision-making. 

Delve into the depths of your data

The Trillium Software System® eradicates the root causes of data problems before they infect your data warehousing and business intelligence efforts by improving the quality of enterprise data to make it “peak-condition.”

  1. Profile and discover data anomalies, structure, and overall suitability before any data migration effort begins.
  2. Match records and identify relationships across multiple customer, product, supplier, financial and other data domains.
  3. Score the perceived quality of data items and the relevance of business rules to gauge the overall health of data that feeds data quality metrics and key performance indicators
  4. Apply reusable data quality rules and processes to master enterprise data across multiple sources, applications and systems in batch, in real time, or by utilizing web services.
  5. Correct misplaced, misspelled and misfielded data to capture valuable information that would otherwise be lost
  6. Derive more accurate operational models, projections and analyses using precise and consistently reliable data