Client is a leading retail pricing intelligence software provider, renowned for their cutting-edge solutions that enable retailers to make well-informed pricing decisions in a dynamic market. Sparity’s collaboration with the client led to enhanced reporting capabilities, a revamped UI for reduced load times, and seamless MySQL integration, resulting in high client satisfaction.

ClientRetail ISVServicesPower BI, My SQLYear 2023

Key Challenges

  • ‚ÄĮThe existing reports were spread across numerous pages and datasets, making it challenging for users to navigate and extract insights efficiently.
  • Managing data efficiently was a challenge, impacting the ability to provide real-time updates and retrieve data efficiently.
  • Existing bugs and performance bottlenecks were affecting the system’s efficiency, causing delays and hindering user experience.

Technologies

MySQL
powerbi
Python

Solution

  • Python visuals were converted into Power BI, enabling advanced analytics for precise pricing insights and trend forecasting, improving the reporting capabilities significantly.
  • Over 120 pages of existing reports were revamped, consolidating disparate datasets. This streamlined UI/UX design allowed for intuitive navigation and improved user satisfaction.
  • MySQL was implemented as the primary database, ensuring real-time updates and efficient data retrieval, addressing the data management issues.
  • An exhaustive review was conducted to identify and rectify existing bugs and performance bottlenecks, resulting in a substantial improvement in system efficiency.
  • DAX (Data Analysis Expressions) was implemented for Power BI performance optimization, reducing load times and enhancing report responsiveness.

Benefits

  • Conversion to Power BI and a comprehensive report overhaul delivered more actionable insights to clients.
  • Streamlined UI/UX design and consolidated reports increased user satisfaction by 45%.
  • Implementation of MySQL ensured timely and accurate data updates.
  • Bug fixes, performance enhancements, and DAX optimization improved system efficiency and reduced load times.