Data Readiness
  1. Assess data sources, ownership, and accessibility
  2. Evaluate data quality, completeness, and reliability
  3. Identify gaps in data pipelines and integrations
  4. Determine readiness for analytics, automation, and AI models
  5. Define data remediation and infrastructure requirements
A close-up, hyper-realistic image of a data analyst working on a laptop, surrounded by data visualizations on the screen. The background is softly blurred, showcasing a modern office environment with light colors. The subject is focused on the screen, with graphs and charts displayed prominently, symbolizing data readiness. The lighting is bright and inviting, emphasizing a professional atmosphere.
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