Dashboards & Visualization — The 5 Phases (+ Optional AI Phase)
Built New or Scaled to Enterprise
1. Data Landscape Assessment & Strategic Alignment
We assess your current reporting environment—whether starting from scratch or scaling existing dashboards—to understand business needs, data gaps, and opportunities.
Steps:
- Review existing dashboards, reports, and KPIs
- Identify inconsistencies, redundancies, and pain points
- Interview stakeholders to understand decision needs
- Map reporting requirements to business outcomes
- Prioritize dashboards for build, redesign, or scale
2. Modern Data Platform Architecture & Cloud Design
We design the semantic models, data structures, and platform components required to support new dashboards or expand existing ones with consistency and governance.
Steps:
- Define semantic model structure and relationships
- Establish naming conventions and metric definitions
- Design scalable, governed data layers for BI
- Align architecture with Power BI best practices
- Ensure cloud readiness and performance standards
3. Data Ingestion, Modeling & Warehouse Modernization
We prepare and optimize the underlying data—building new models or enhancing existing ones—to ensure accuracy, performance, and readiness for visualization.
Steps:
- Build or refine data pipelines feeding BI
- Clean, transform, and validate source data
- Create or enhance star schemas and dimensional models
- Optimize for performance and refresh efficiency
- Implement data quality and validation checks
4. Enterprise Analytics, BI Modernization & Operationalization
We develop modern dashboards and visual experiences—either net‑new or redesigned at scale—using best‑practice Power BI patterns.
Steps:
- Design intuitive, consistent dashboard layouts
- Build visuals aligned to business questions
- Apply UX/UI standards for clarity and usability
- Implement drill‑downs, navigation, and interactivity
- Validate metrics with business stakeholders
5. Optimization, Governance Maturity & Continuous Improvement
We refine performance, validate metrics, standardize visual patterns, and support adoption—ensuring dashboards remain trusted and scalable.
Steps:
- Optimize DAX, visuals, and refresh performance
- Standardize templates, themes, and visual patterns
- Strengthen governance for metrics and access
- Provide training and documentation for users
- Establish continuous improvement and enhancement cycles
6. (Optional) AI‑Driven Insight Opportunities & Readiness
We identify where AI can enhance your reporting—surfacing opportunities for automated insights, natural‑language querying, and predictive visualizations.
Steps:
- Assess data readiness for AI‑enhanced reporting
- Identify high‑value AI use cases within dashboards
- Evaluate opportunities for natural‑language querying
- Explore predictive and automated insight capabilities
- Recommend pilot opportunities and next‑step roadmap
