Enterprise Data Core - Full data life cycle

Telecom

ANZ

Modern Data Stack | Google BI Stack | Telecom | Data Lakehouse Architecture | Data Availability

Business Challenge :

Managing and analysing large volumes of data in a timely and accurate manner is challenging, especially with disparate reporting platforms and operational silos of information. The heavy dependence on spreadsheets and lack of data accessibility across the organization can further impede decision making processes, leading to missed opportunities and decreased business agility.

Proposed Solution:

  • Build an Enterprise Data Core as a fabric for all analytics needs using the lake house strategy. This solution would provide a unified view of data, eliminate the need for multiple definitions and sources of truth, and improve data accessibility across the organization.

  • Leveraging the latest features of the modern data analytics stack and adopting an open architecture with industry standard tool sets that include a mix of open source and COTS platforms would ensure scalability, flexibility, and automation of analytics workflows and ensures data accessibility and sharing.

  • Finally, ensuring data accessibility and sharing by implementing access control mechanisms would foster collaboration and unlock the true potential of data for the organization.

KPIs Measured:

Customer:

  • Product Mix
  • Renewals
  • Cross & Upsell
  • ARPU
  • AMPU
  • Channel
  • Effectiveness
  • Revenue
  • Dunning

Operational:

  • NOC 3P Integration
  • Network & Carrier Cost Management

Strategic:

  • Customer Lifecycle Value
  • Decay Curve Analysis
  • Cohort Classification
  • Customer Health Score

Key Features/Functionalities

  • Single source of truth across all systems with data lake-house as the primary source for insights.
  • Decoupled approach to handle data load for scalability and risk mitigation.
  • Foundational AI & ML ready architecture for advanced analytics techniques.
  • Fully integrated orchestration from data landing zone up to consumption layer for seamless data flow.
  • A1P approach to ensure data availability, integrity, performance, and protection.
  • Enhanced data observability with embedded data lineage tracking and data catalog.
  • Embedded data quality and governance mechanism for high-quality data conforming to organizational standards.
  • Data enrichment flows to enhance master data, both manual and automated.
  • Closed loop feedback approach to re-ingest data points for continuous data improvement.
  • Plug and play for analytics automation workflows and ease of consumption. 

Architectural Diagram

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