Overview Conviva's solution for data teams focuses on enhancing data accessibility and reducing operational costs. By utilizing sessionized data, it eliminates the need to store or process irrelevant raw events, leading to a 50% reduction in storage, query, and logging expenses. This approach ensures that teams work from a consistent data set, facilitating seamless integration of real-time insights into operational dashboards across various departments. Additionally, ingesting quality of experience data into data warehouses allows for bespoke analysis when combined with other datasets. Overall, Conviva's platform empowers data teams to free up resources for more strategic initiatives by minimizing data warehouse storage and query costs.

Features and Capabilities

• Sessionized Data Processing: Transforms raw event data into sessionized formats, reducing the need to store or process irrelevant information and cutting storage, query, and logging costs by 50%.
• Consistent Data Sets: Provides a unified and consistent data set, enabling seamless integration of real-time insights into operational dashboards across teams.
• Cross-Team Collaboration: Facilitates collaboration by ingesting quality of experience data feeds into data warehouses, allowing for bespoke analysis when combined with other datasets.
• Resource Optimization: Reduces data warehouse storage and query costs by leveraging sessionized, stateful metrics, freeing up resources for more strategic initiatives.
• Real-Time Insights Integration: Enables the incorporation of real-time insights into operational dashboards, enhancing decision-making processes across various teams.
• Scalable Data Management: Supports large-scale data environments by efficiently managing and processing data, ensuring scalability as organizational data needs grow.
• Enhanced Data Accuracy: Ensures data accuracy and consistency, providing reliable information for analysis and reporting purposes.
• Customizable Analytics: Offers the ability to perform bespoke analysis by combining quality of experience data with other datasets, tailoring insights to specific business needs.
• Operational Efficiency: Streamlines data operations, reducing the time and effort required for data processing and analysis tasks.
• Cost Reduction: Achieves significant cost savings in data storage and processing by eliminating unnecessary raw data handling.