Self-service BI: use cases, features & platforms to consider

Augmented analytics capabilities entail the incorporation of artificial intelligence, machine learning, and natural language processing to automate many basic and advanced data management and analytics initiatives, from data quality management to data visualization. AI-powered BI tools help surface insights from unstructured data, prioritize relevant information, and forecast potential outcomes when creating what-if scenarios.

An integral element of a self-service BI solution is the ease of use manifested in a graphical drag-and-drop user interface with intuitive navigation, interactive charts, and graphs, as well as support for natural language querying and generation for conversational interaction. These capabilities are equally important for standard and advanced users, such as data scientists, to enable the creation of custom data transformations and scripts, complex data visualizations, and data pipelines.

A self-service BI solution should offer customizable report and dashboard templates that can be configured according to the employee’s role, for example, filtering and selecting the metrics of interest and establishing refresh intervals for automatic data updates. Self-service BI users should also be able to set up report or dashboard send-out time and mode (via emails, secure viewer areas, URLs, embedding, mobile reporting, etc.).

Semantic data catalogs help self-service BI consumers autonomously find relevant information with familiar business terms (customer, prospect, sales, etc.). Having constantly updated and well-governed semantic data catalogs helps ensure all business users across the company get a consistent view of corporate information and can collaborate efficiently.

Self-service BI software should enable its users to not only access and handle data but also share the discovered insights with colleagues via email, Slack, Microsoft Teams, Google Sheets, and other platforms. Self-service BI software supports this capability with content sharing and embedding (scheduled and ad-hoc), role-based content editing, alerts, and notifications on report updates or changes.

As specialists from different departments get access to sensitive corporate data, it’s essential for a self-service BI tool to protect data privacy. That’s where features like granular user access levels, multi-factor authentication, and data masking and encryption can help. These capabilities also help the self-service BI solution comply with the strictest data protection laws and regulations.

The self-service BI platform should support robust data governance policies that define how corporate data is consolidated, aggregated, accessed, and used across the company. For example, implementing data marts enables controlled data access management and enhanced data consistency. These data repositories aimed at particular user groups, such as marketing, customer service, and sales teams, provide all the information a member of the specific team could require without any excess.

For a BI platform to maintain high performance and accommodate the growing number of users simultaneously, it should come with a flexible architecture, be fault-tolerant, and allocate resources reasonably. One of the solutions could be setting up a self-service BI tool in a cloud environment. It’s a valuable asset for rapidly growing businesses, allowing them to swiftly increase or decrease storage or computational resource consumption.