Development of an Internal Analytics Dashboard for Data-Driven Decision Making

The organization faces difficulties in consolidating and visualizing internal data sources, resulting in limited real-time insights and delayed decision-making. Existing tools do not support comprehensive analytics, leading to potential inefficiencies and missed opportunities.

A mid-to-large-sized organization seeking to enhance internal analytics capabilities to improve decision-making processes and operational efficiency.

• Design and implement a centralized internal analytics dashboard that consolidates data from multiple sources.
• Enable real-time data visualization and customizable reporting features to support swift decision-making.
• Improve data accuracy and accessibility, reducing manual data handling efforts.
• Achieve measurable improvements in operational efficiency and decision making within 6 months of deployment.

• Secure data integration modules supporting multiple sources such as databases, APIs, and internal applications.
• Customizable dashboards with drag-and-drop widgets for various key performance indicators.
• Real-time data update and alerting mechanisms to notify stakeholders of critical changes.
• User role-based access controls to ensure data security and compliance.
• Automated report generation with export options for stakeholder distribution.
• Mobile-responsive interface for access on various devices.

• Database systems (SQL, NoSQL)
• Internal API connections for continuous data feeds
• Existing business applications (ERP, CRM)
• Authentication and identity management systems

• System must support at least 99.9% uptime
• Data processing latency should be less than 2 seconds for real-time updates
• User authentication and data encryption to ensure data security and privacy
• Scalable architecture to support increasing data volume and user load over time

The implementation of the internal analytics dashboard is expected to significantly enhance data accessibility, leading to faster, data-informed decisions. This is projected to improve operational efficiency by approximately 20% within the first 6 months, reduce manual reporting efforts by 30%, and strengthen overall data governance and security.