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Data Profiling & Data Quality

Discover and profile data before data integration to reduce or eliminate costly data problems. Enhance IT productivity and enhance better business decision outcomes.

Data Profiling

Data Profiling helps businesses to understand the content, structure, pattern of source data. It significantly reduces development cost by discovering the underlying data, and accelerates delivery time by lowering or reducing the data quality risks. It provides an algorithmic sample of source data to visualize :

  • Relationships
  • Data Anomalies
  • Incorrect Data
  • Missing Data
  • Misrepresented Data (Nulls)

Data Profiling helps to determine if existing data could be utilized for other purposes, outline challenges and risks for integrating with other application, and see if metadata reflects the actual value. Data Profiling is a predecessor for Data Quality, Master Data Management, Data Integration, Data Warehousing and Business Intelligence.

Cube Intelligence is strongly positioned to help you in your data profiling missions to profile :

  • Data Columns
  • Data Dependency
  • Data Redundancy

Data Quality

Cube Intelligence Data Quality solutions deliver authoritative and trustworthy data to all stakeholders, projects and application – on premise or in the cloud. We profile, cleanse, enrich, and match missing data for business needs such as Name and Address cleansing, data matching and mapping, data consolidation and data integration.

Data Quality is checked across five major components :

  • Consistency
  • Completeness
  • Data Format
  • Accuracy
  • Duplication
  • Relationship Integrity

Based on the definition of the core functional requirements of the data quality in Gartner Magic Quadrant for DQ 2010, Cube Intelligence includes the following steps for delivering data quality initiatives :

  • Profiling
  • Parsing and standardization
  • Generalized “cleansing”
  • Matching
  • Monitoring
  • Enrichment

“In a recent Gartner study, 36% of participants estimated that they are losing more than $1 million annually because of data quality issues. It is estimated that more than 25% of the data in mid to large organizations is flawed, inaccurate, duplicated and incomplete. Almost every organization in the world that has substantial amount of data, inevitably inherits poor data. Poor data quality not only affects sales and marketing, it also impacts internal business processes related to manufacturing, financing and distribution.”

Led the initiative to design the Master Data Management models for the Masshealth Third Party Liability (TPL) group. Expertise in data warehousing, data integration, data matching and data mining expedited the development of reporting for key healthcare coverage data.

Kathleen M – TPL Program Director UMass CHCF   
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