The shift to the cloud was supposed to change everything: faster access to data, lower overhead, endless scale. But for many organizations, cloud migration hasn’t automatically translated to better business outcomes. Why? Because having data in the cloud isn’t the same as having data that’s usable, trustworthy, and actionable.
According to TDWI’s 2025 research, even as more organizations adopt cloud data platforms, self-service analytics remains the top priority for analytics leaders year after year, yet challenges still remain: Siloed systems, low data literacy, poor governance, and a mismatch between tools and users.
What’s holding back self-service analytics?
Self-service analytics isn’t just about putting tools in people’s hands. It’s about giving every employee the data, skills, and context they need to take effective action. Yet only 20% of organizations report that BI is truly democratized across departments.
- Many business users still lack access to the data they need.
- Data literacy is low, and training is uneven or one-size-fits-all.
- Data quality and trust issues discourage exploration.
- Metadata is incomplete or missing, making data hard to interpret.
- Unstructured and semi-structured data is still out of reach.
How do you build a foundation for governed self-service analytics?
Start by rethinking your architecture. The key is balancing flexibility for business users with the oversight IT needs. A strong data foundation for self-service analytics should include:
Centralized governance with decentralized access. Tools like Unity Catalog from Databricks provide centralized governance, metadata management, and lineage across cloud environments.
Federated access across data sources. Instead of moving all your data, platforms like Databricks enable federated governance and querying of data wherever it lives.
Low-code access through trusted interfaces. Alteryx connects to governed data layers like Unity Catalog and pushes analytics logic down to the data platform—keeping data in place, while giving users the power to explore.
With this stack, business users can access and analyze data through familiar tools, while IT retains control over data access, auditability, and performance.
Real-world examples in retail and finance
Consider the global retail brand that receives thousands of customer reviews in multiple languages. Using Alteryx and Databricks, they built a governed workflow that:
- Translates reviews into English
- Applies sentiment analysis and topic modeling
- Summarizes insights for each product and region
- Forecasts sales based on customer sentiment
Because all data prep, analysis, and model scoring happens inside the Databricks environment with Unity Catalog governance, both IT and business teams can trust the outputs. Dashboards can be built in Databricks or external tools like Tableau and Power BI.
In the office of finance, monthly close traditionally took days of manual spreadsheet work. By consolidating spreadsheets into a governed lakehouse and using Alteryx to build repeatable workflows, what once took weeks now takes hours. The best part? Business users control the workflows, but data stays governed and auditable.
This approach also sets the stage for enterprise-scale AI. With platforms like Alteryx and Databricks, organizations can:
- Feed GenAI models with governed, enriched data from the data lakehouse
- Enable marketing teams to experiment with natural language prompts, while restricting what models can access
- Build audit trails and lineage for every AI-assisted insight.
What’s next?
The cloud was just the first step. If you want your data to work for your business, it’s time to:
- Connect your cloud data platforms to governed access layers
- Empower business users with low-code tools that respect governance
- Tailor data literacy efforts by role and responsibility
- Align analytics governance with data governance (not as an afterthought)
Self-service without guardrails creates chaos. Governance without access stalls innovation. The good news is that organizations can have both.
Editor’s Note: The content of this blog post was based on information from a webinar presented by Alteryx, TDWI, Databricks, and Capitalize Analytics.