Solution Capability · 5.6
Improve data quality, accessibility, integration, modeling, and governance so reporting, automation, applications, and AI can rely on better information.
Data may be duplicated, inconsistent, inaccessible, poorly documented, delayed, or trapped in systems that were not designed for analytics, automation, or AI.
CIO, CTO, data and analytics leaders
Application and integration owners
Operations and finance leaders
AI and automation teams
Organizations modernizing legacy reporting or data platforms
Identify priority business decisions and use cases.
Assess sources, quality, ownership, lineage, access, models, pipelines, reporting, and governance.
Design the target data architecture and migration sequence.
Improve integration, quality, models, metadata, and access.
Validate reporting, automation, and AI use cases.
Establish ownership, monitoring, and continuous improvement.
Sales and pipeline process improvement.
Service and case workflows.
Data quality and reporting.
Integration with Microsoft, ERP, finance and portals.
Forms, approvals and document workflows.
AI-assisted knowledge, service, and operations.
Azure data services, Microsoft Fabric where appropriate and confirmed, Power BI.
SQL and databases, ERP, CRM, Salesforce.
Dynamics 365, Business Central, files, APIs, data platforms, Microsoft 365, and AI services.
Security & Governance
Modernization addresses data classification, access, minimization, quality, lineage, retention, migration validation, environment separation, monitoring, and approved use. Product and platform claims are confirmed during architecture.
A readiness workshop is the fastest way to find out if this is the right starting point.
