Business people have wanted self-service BI for years, but it's rarely achieved and often misunderstood. Before you can select the right tool, it’s critical to examine the people, process, and politics involved.
In this session, we'll review what self-service means to various BI user communities and what each of these communities needs in terms of data, governance, integration, and analytical capabilities. We'll explore three BI environments—traditional BI portals with guided data discovery, analytical sandboxes, and data science hubs—and how they support casual information consumers, business "power" users, and data scientists. We will also review how data engineering, data integration, and data preparation fit together, and who is involved in these processes.
You Will Learn
- How to determine analytical use cases and match people’s analytical styles
- The best choices for data architectures
- Where data governance and metadata management applies
- Enabling organizational approaches
- Best and pragmatic practices
- How to select best-fit technology
- Data and information architects
- Data governance professionals (data governance managers, directors, architects and leads)
- Data professionals (architects/ analysts/ administrators/ modelers/ etc.)