Implementing Data Fusion
- Define Objectives & Scope
- Align with business goals: Are you improving 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 data (CRM, ERP, transaction, third-party APIs) for completeness, accuracy, and redundancy.
- Cleanse data by removing duplicates, filling gaps, and standardising formats (e.g., dates, currencies).
- Consolidate Data
- Combine raw data streams for processing.
- Integrate processed data (e.g., customer segments) using rules or machine learning.
- Apply WolfzHowl’s brand/category-specific understanding to derive strategic insights.
- Deploy Integration Infrastructure
- Use WolfzHow’s proprietary AI-driven platform and custom ETL pipelines to:
- Ingest data via protocols like MQTT, OPC UA, or REST APIs.
- Map entities (e.g., customers, products) across systems to create unified profiles.
- Resolve conflicts algorithmically (e.g., prioritising the most recent or reliable source).
- Develop and Activate Plan
- Create action plan based on business goals and data insights
- Activations can include personalised offers, cross and up sell opportunities, engagement journeys etc.
- Enable Cross-Team Adoption
- Provide training and intuitive dashboards to ensure teams can access and act on fused data.
- Establish governance frameworks to maintain data quality and compliance over time.