Implementing Data Fusion
Define Objectives & Scope : Align with business goals: customer insights, incremental revenue, market expansion, etc.
Example: “Unify customer data from 5 sources to enable real-time personalisation.”
Assess Data Sources & Quality
- Audit existing CRM, ERP, and APIs for completeness and accuracy.
- Cleanse data: remove duplicates, fill gaps, and standardise formats.
Consolidate Data
- Combine raw streams for processing.
- Integrate segments using rules or machine learning within a clear data fusion model.
- Apply WolfzHowl’s category expertise for actionable insights
Deploy Integration Infrastructure
- Use proprietary AI-driven platforms and ETL pipelines.
- Map entities (customers, products) across systems to create unified profiles.
Resolve conflicts algorithmically.
Develop and Activate Plan
- Build action plans aligned with business goals and insights.
- Activations may include personalised offers, upsell/cross-sell, or journey mapping powered by data fusion solutions.
Enable Cross-Team Adoption
- Provide training and dashboards for accessibility.
- Establish governance frameworks to ensure long-term data fusion development success.