Accessing reliable business data often requires support from analysts, creating delays between questions and answers. OWOX Data Marts helps bridge that gap by enabling data teams to build a trusted data foundation that business users can explore independently. Once the underlying data structure is created, teams can deliver live, customisable reports that work seamlessly in spreadsheets and AI tools such as ChatGPT, Claude, and Gemini.
This approach allows non-technical users to analyse information and generate insights without needing direct access to complex databases. By maintaining a consistent source of truth, OWOX Data Marts helps reduce errors and improves confidence in reporting. The platform is designed to support collaboration between data teams and business stakeholders, making analytics more accessible across an organisation. OWOX Data Marts provides a streamlined way to turn trusted data into actionable insights at scale.
Image Credit: OWOX Data
What's Driving This Trend
- Self-service Analytics
- Business users gain faster access to trusted insights as platforms shift reporting control away from analyst bottlenecks and into familiar work environments.
- AI-ready Data Marts
- Structured data foundations become more valuable as generative AI tools rely on consistent, verified business information for accurate analysis.
- Spreadsheet-native Reporting
- Live, customizable reports inside everyday spreadsheet tools create new potential for scalable analytics adoption across non-technical teams.
Who This Affects Most
- Business Intelligence
- Centralized sources of truth are reshaping reporting platforms by improving data reliability while supporting broader organizational decision-making.
- Enterprise Software
- Collaboration between technical and business teams is becoming a key differentiator for software that turns complex data systems into accessible workflows.
- Data Management
- Governed data infrastructure enables companies to reduce reporting errors and extend analytical capabilities without exposing users to database complexity.
