CVMBoK

Data engineering (DE) teams play a crucial role in enabling CVM by ensuring that customer data is collected, integrated, and readily accessible for decision-making, process automation, and performance measurement. This collaboration directly impacts the effectiveness and efficiency of CVM activities.

DE provides the foundational infrastructure for handling customer data, which is essential for CVM’s operations. It ensures the availability of high-quality data, enabling CVM teams to make informed decisions and automate processes efficiently. Reliable data supports analytics, reporting, campaign management, personalization, and customer insights. It also impacts the stability of automated CVM processes and the credibility of reporting.

For example, access to real-time customer usage data allows CVM teams to launch targeted campaigns and track their performance effectively. Accurate data ensures that CVM teams can deliver personalized offers, predict churn, and evaluate the success of initiatives, enhancing the overall productivity of the CVM team.

CVM teams define data requirements and collaborate with the DE teams to ensure that these needs are met. This includes working on projects related to restructuring customer data and providing feedback on data quality and system requirements. The DE teams use this input to design systems that support key CVM activities, such as:

• Analytics and reporting
• Personalization and recommendations
• Campaign management
• Customer insights and impact tracking

CVM teams influence the workload of DE teams by initiating requirements for data pipelines and automation. CVM initiatives often dictate the need for timely and accurate data feeds. Automation of CVM processes adds additional demands, including service-level agreements for data pipeline availability and monitoring. Changes in CVM implementation may lead to the refactoring of data pipelines, emphasizing the importance of a collaborative approach.

To ensure successful collaboration, CVM and DE teams must focus on the following:

• Designing data warehouses and models tailored to CVM’s analytical needs
• Implementing efficient ETL processes to streamline data preparation
• Ensuring real-time data feeds for timely decision-making
• Establishing monitoring and testing mechanisms to maintain operational continuity

CVM teams should act as effective internal customers by clearly communicating requirements, prioritizing needs, and providing timelines. Regular alignment meetings and shared KPIs help both teams stay focused on shared objectives.

The quality of collaboration between CVM and DE teams directly affects CVM’s ability to execute impactful initiatives. Misalignment can lead to disruptions in data availability, inaccuracies in reporting, and inefficiencies in campaign execution. Clear communication, mutual understanding, and proactive alignment are critical to maintaining a productive partnership.