CVMBoK

A Guide for Leaders and Professionals in Customer Value Management

Collaboration with Data Engineering team

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Purpose of functional area #

The purpose of the Data Engineering team as understood here is ensure necessary data is collected, integrated and available for decision making, process automation, and measurement for the CVM function.

Inputs: How it impacts CVM #

  • Provides the foundational data infrastructure & customer data
  • Determines the effectiveness of CVM activities by making high quality data available for data-driven decisions
  • Enables automation and high productivity
  • Affects stability of automated CVM processes
  • Affects credibility of CVM reporting

CVM work related to function #

  • Owns the data requirements for customer data needed by CVM function
  • Uses data provided for work such as:
    • analytics,
    • reporting,
    • personalization/recommendations,
    • campaign management,
    • customer insights,
    • impact tracking
  • Provides feedback on data quality and completeness to D.E. team
  • Owns and initiates major data engineering projects related to refactoring of customer data

Outputs: How CVM impacts this area #

  • CVM initiatives dictate the requirements for data engineering work related to Customer, Campaign and Customer Value Management domains.
  • Changes to how Campaigns and CVM initiatives are designed & implemented break previously created data pipelines resulting in refactoring workload for Data Engineering team
  • CVM automation creates SLA and availability demands for data engineering pipelines

Key handshakes to consider #

The aim in this collaboration is to ensure that Data Engineering team is aware of CVM role as the owner of requirements, and that CVM team is a good internal customer for the Data Engineering team. Together these teams are:

  • Designing data warehouses/data models that effectively support CVM analytical needs.
  • Implementing ETL processes that streamline data preparation for CVM applications.
  • Ensuring real-time data feeds are available for timely CVM decision-making.
  • Ensuring that monitoring and tests are in place to facilitate operational awareness

Impact of collaboration quality on CVM team results #

HIGH – lack of alignment with Data Engineering team results in a severe impact on the performance of CVM.