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Data Health Assessment (DHA) with MDO Data…

Data Quality Management

MDO DQM employs a unique AI-backed approach through its inbuilt tool, Data Intelligence Workbench (DIW) for data quality assurance.

What are your data quality challenges?

Inheriting bad-quality data from legacy systems.

No systematic way to detect and fix data quality issues.

Absence of system and tool to incorporate data quality into processes.

How MDO Data Quality Management helps you in your data quality journey?

DQM provides a workbench to validate,cleanse and enrich data with minimum human intervention using these four steps:
Profiling and Scoring

Establish data structures, outline evaluation criteria, evaluate data integrity, and analyze performance (contrasting benchmarks with current results).

Data Remediation

Remediate the original data; run the rules again; handle exceptions and make corrections accordingly; manage governance and workflows for further improvement.

Data Transformation

Utilize automated data transformation guided by rules; Enhance data by incorporating external content; Integrate with data from external applications.

Learning and Monitoring

Monitor DQ scores compared to standards, Improve learning models within MDO, Monitor data quality Key Performance Indicators (KPIs).

What will you get?