Intel Analytics Team: Design Ecosystem Case Study

WASHINGTON,2026-01-13 06:50:00

Data access Satisfaction Soars as Organizations Build Robust Data Foundations

A shift toward empowering business users with self-service data tools is driving unprecedented gains in data access satisfaction.

• Organizations are seeing meaningful improvements in data access satisfaction by prioritizing a strong data foundation.
• Empowering business users-those with the deepest understanding of the data’s context-to explore and analyze data independently is key.
• investing in training and building a community of “super users” accelerates adoption and fosters collaboration.
• A structured feedback process ensures ongoing support and responsiveness.
• This data foundation is increasingly viewed as essential for future artificial intelligence initiatives.

Getting everyone on the same page with data isn’t just a tech problem; it’s a peopel problem. And a recent organizational push demonstrates that when you give the folks who actually understand the business the tools to play with data themselves, good things happen. A central semantic layer has been instrumental in fostering a strong data culture, allowing individuals, regardless of their technical skills, to creatively use data.

Empowering Users, Not Just Providing Dashboards

Traditionally, analytics teams built static dashboards. But that approach often left business users wanting more. This new strategy empowered those “true domain experts” to explore data and build their own metrics, rather than being limited to pre-defined views. What’s the result of giving business users more control over their data? Organizational survey ratings for data access satisfaction have reached an all-time high.

Did you know? – A “semantic layer” translates complex data into business-pleasant terms, making it accessible to a wider audience.

To ensure widespread adoption,a thorough engagement strategy was implemented,focusing on clear interaction,targeted training,and community building. Specific training curriculums were developed for different user types – viewers, developers, and explorers – recognizing that everyone learns differently.

Building a Data Community

The team also identified and cultivated “super users” within various business groups.These individuals acted as local advocates and experts, sparking internal partnerships and enabling users from different departments to discover shared use cases and collaborate on solutions. This collaborative spirit proved invaluable.

Pro tip – Prioritize identifying and supporting “super users” within each department. they can champion data initiatives and provide peer-to-peer support.

Furthermore, a structured intake process was established to efficiently route user feedback and questions to the appropriate personnel. This commitment to support and responsiveness reinforced a positive data culture. Looking ahead, this accurate and centralized data foundation is seen as a critical prerequisite for leveraging artificial intelligence, with excitement building around the potential of tools like Gemini to further enhance the end-user experience through conversational analytics.

Reader question – What challenges did the association face when implementing the semantic layer, and how were they overcome? Share your thoughts!

Clarification of Changes & Answers to Questions:

* Why: The organization launched a new data strategy to address the limitations of traditional, static dashboards.they aimed to empower business users with self-service data tools and a central semantic layer.
* Who: The initiative involved analytics teams,business users (specifically “true domain experts”),and identified “super users” within various business groups.
* What: The core of the initiative was the implementation of a central semantic layer and a comprehensive engagement strategy (training, community building, and a structured feedback process).This led to a significant increase in data access satisfaction.
* **How