Data Management Dilemma: Choosing Between In-House and Outsourced WFM Solutions
Throughout my WFM career, I've encountered scenarios where WFM data was handled by either the internal WFM team or external teams like a centralized analytics group. Each option presents distinct advantages and disadvantages:
Internal WFM team managing the data:
Advantages:
- Speed and Flexibility: As contact center operations rapidly change, owning the process in-house allows for agile adjustments of data definitions and metric calculations aligned with operational shifts.
- Upskilling Opportunities: The WFM team can broaden its knowledge in data analytics and engineering, enabling potential expansion into other business areas.
Disadvantages:
- Scalability Challenges: The WFM team lacks the expertise of a dedicated Data Engineering team, making it harder to scale datasets optimally as the business and complexities grow.
- Technical Gap: Attrition of technically skilled WFM members can result in talent shortages and difficulty in uptraining remaining staff.
External team managing the data:
Advantages:
- Core Focus on WFM Services: Data management outsourcing allows the WFM team to concentrate on value-added services like RTA, scheduling, and forecasting.
- Scalable Solutions: Collaborating with dedicated Data Engineering and Analytics partners ensures scalable data solutions aligned with business expansion.
Disadvantages:
- Prioritization Issues: WFM data isn't typically deemed mission-critical, potentially leading to deprioritization if Data Engineering or Analytics teams have other urgent commitments.
- Loss of Control: Outsourcing WFM data management involves challenges in ensuring accurate understanding of changes, proper evolution of data, and clear documentation for accessing new data.
This decision needn't be binary, and with evolving technology, contact center data will continually expand. It's crucial to retain the flexibility to explore both options. The optimal approach involves close collaboration between the WFM and Data Engineering teams. The WFM team can refine data definitions, adjust pipelines, and establish data marts. Simultaneously, the Data Engineering team can establish a robust data warehousing foundation for WFM self-service while remaining engaged partners for future solutions as WFM data requirements evolve.