Global Leader in Pharmaceutical
and Life Sciences

Data Quality and Data Governance Analytics

Business Requirements:

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Perform data quality analysis on master and logistics data to measure accuracy, completeness, and consistency.

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Reduce/eliminate manual effort for report generation and provide write back functionality from report to database application.

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Illustrate goals, trends, progress, an root-cause analysis.

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Build the enterprise data lake to consolidate the data from SAP, Salesforce, Veeva and Trackwise data sources.

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Identify data quality assurance process across plant and attribute groups over time.

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Provide scorecard view for various plant to analyze Temp Condition, DG profiles, Alternative UDM, Loading Grp, and Material.

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Business wanted to highlight deviations and flag outliers for Operational Controls.

Scope of Services:


Designed data quality visualizations and dashboards for summary view, plant view,
defect code analysis, LDO analysis, DQI Details-Logistics, and weekly trend, Spotfire is
used as the software to design & deploy the dashboard.


Ingested data from various data source like Infinity, Regulatory Data, and Enterprise
Material into AWS Cloud (Enterprise Data Lake) using Python.


Utilized python for Dynamic data analysis, filtering, deep data analysis and dynamic
color coding for trend & outlier.


 Using R Capabilities perform defect code and trend analysis.


Develop Custom DQIs (Data Quality Indicators) as per businesses need.

Solution Highlights:


Data Quality Dashboard enables business users to continuously monitor data quality,
view its current state, track trends, and use insight to make informed business decision.


 Eliminated need for extensive verification of planning output.


Implemented report automation to reduce the manual labor hours and eliminated
process waste which led to cost savings.


 Write back capabilities were implemented in Spotfire.


Effective Tracking of Issue

Centralize Data Storage

Improve Efficiency of Plant

Technology Environment: