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QlikView To Microsoft Power BI Migration

Simplifying Technology Strengthening Business > AI Technology > QlikView To Microsoft Power BI Migration
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QlikView To Microsoft Power BI Migration

Seamless migration from QlikView to Power BI to enhance analytics capabilities, scalability, and operational efficiency in the energy & utilities sector.

Industry: Energy & Utilities

Functional Area: Data Analytics, Reporting, and Business Intelligence

Project Info

  • Client:

    Digital Health Platform | UAE

  • Services:

    Credit Restoration

  • Date:

    February 12, 2024

  • Category:

    Finance

  • Team:

    Jonathan Hunt

Business Objective/Challenges:

  • Enhance actionable insights by modernizing the existing QlikView environment.
  • Overcome integration limitations with diverse data sources and systems.
  • Address scalability challenges to manage large volumes of energy and utility data.
  • Improve user experience with a more intuitive interface and advanced analytics capabilities.
  • Reduce operational costs associated with legacy infrastructure and licensing.

Solution/Approach

  • Designed a robust Power BI architecture emphasizing seamless data integration, strong governance, and scalability.
  • Executed a zero-risk migration by mapping existing QlikView data models to Power BI datasets, including necessary data transformations and cleansing.
  • Implemented performance optimization techniques such as partitioning, incremental refresh, and integration with Azure Analysis Services.
  • Transitioned to a cloud-based reporting environment to enhance accessibility and reduce infrastructure costs.

Technologies

  • Power BI
  • Postgressql

System Architecture:

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Business Outcome/Benefits/Results:

  • Democratized data access across the organization with interactive, user-friendly dashboards.
  • Reduced infrastructure costs and eliminated dedicated server maintenance through cloudbased deployment.
  • Achieved comprehensive data integration from multiple sources, enabling more informed
    decision-making.
  • Improved scalability and performance, supporting growing datasets and enhanced user
    concurrency
  • Enabled cross-device reporting, ensuring stakeholders have access to real-time insights
    anytime, anywhere.
  • Laid the foundation for advanced data modelling and AI-driven analytics.

Team size:

  • 3 Members

Duration of Project in Months:

  • 1 Month