Establishing an accurate enterprise data audit
Most failed AI projects trace back to fragmented data rather than bad software. Large organizations run on dozens of disconnected tools, legacy databases, and division-specific spreadsheets across different departments. Deploying AI on scattered data produces unreliable reports and sub-optimal outputs, leaving business leaders hesitant to trust the results.
An AI data readiness assessment maps your entire data landscape against your management structure, ensuring your data directly supports executive decision-making:
Eliminating Unfiltered Assumptions
Auditing actual data quality, access controls, and storage practices across all division databases rather than relying on self-reported estimates.
Mapping Data to Decisions
Connecting scattered data sources directly to the reporting dashboards, key metrics, and decision-making workflows leadership uses to run the business.
Testing AI Roadmap Feasibility
Testing existing databases and document repositories against the specific technical requirements of your planned AI initiatives.
Accelerating AI Data Strategy
Identifying immediate infrastructure fixes to unlock quick-win use cases while planning long-term platform upgrades.
A structured 4-week evaluation
Weeks 1–2: Discovery & Audit
Conducting targeted interviews across technical, business, and operations teams. Auditing sample data sources across structured databases, legacy platforms, spreadsheets, and unstructured document stores.
Weeks 2–3: Gap Analysis
Mapping existing data capabilities against your planned AI initiatives. Classifying projects into three buckets: ready to launch, requiring targeted remediation, or requiring foundational architecture changes.
Week 4: Executive Workshop & Roadmap
Presenting findings to executive leadership, sequencing recommendations across immediate quick fixes and multi-quarter structural initiatives.
Clear technical and operational direction
Enterprise Data Landscape Map
Full visibility into all division data sources, quality levels, ownership models, and integration points across the organization.
Decision-Making & Reporting Framework
A clear map connecting underlying data sources and reporting dashboards directly to key leadership decisions and managerial workflows.
AI Initiative Gap Analysis
Direct mapping of each planned AI use case against required data readiness standards.
Prioritized Action Plan
Sequenced recommendations separating immediate operational fixes from broader architectural projects.
Target Architecture Options
Clear guidance on whether your organization should evaluate platforms like SAP HANA, Databricks, Snowflake, or open-source solutions next.
Next steps for infrastructure
Once your assessment establishes clear baseline requirements, Ekipa helps move findings into target platform engineering.
Design your infrastructure
Move into detailed platform selection and system design via Data Architecture Design.
Deploy engineering teams
Build scalable data pipelines and modern platforms through Data Platform Implementation.