Self-service analytics: transforming business decision-making

What is self-service analytics?

Imagine a tool that allows every employee, regardless of data skills, to dive into analysis, discover opportunities, and derive actionable insights. This is the essence of self-service analytics. In this new paradigm, accessing and exploiting data is no longer the privilege of data analysts but is becoming child's play for everyone.

The Power of Autonomy

Self-service analytics tools differ from traditional BI solutions because they are easy to use. They offer drag-and-drop interfaces, enhanced analytics capabilities through automation and AI, and flexible data connectivity. So, whether you want to build a dashboard, interpret a report, or share insights, these tools make analysis as accessible to the layman as to the seasoned analyst.

What are the benefits?

Self-service analytics bridges the gap caused by the lack of trained analysts, ensuring that the benefits of data analytics are available to more people. For example, adopting truly autonomous BI solutions offers multiple benefits:

• Better decision making: they improve accuracy, agility, and decision-making efficiency by delegating analytical tasks directly to business users who have a deep understanding of the data.
• A data-driven culture: they allow more people to read, manipulate, share, and evaluate their data without relying on experts to create or explain reports.
• A reduction in dependence on experts: they offer users access to standalone dashboards and reports, eliminating the need for systematic assistance in creating reports or looking for answers in their data.

How do I deploy it in my company?

The successful deployment of self-service analytics within an organization is not just about selecting the right tools; it is a true strategic adventure, aligning technology with the skills and needs of each user. Here's how to navigate to success:

1. Understanding users
   ‍The first step to effective deployment starts with a deep understanding of who will be using these tools. Employees generally fall into three main categories: consumers (non-technical business users), explorers (users with intermediate experience with analytics tools), and experts (data analysts and developers). Everyone has different needs and expectations when it comes to data and KPIs.
2. ‍Selecting the right tools
   Once users have been identified, it is important to choose tools that offer the right balance between ease of use and advanced analytical capabilities. Features like natural language query for consumers, interactive data visualizations for explorers, and sandbox environments for experts can cover the entire spectrum of needs. The easiest way is sometimes to create a “tailor-made” tool for your organization, which includes all the needs of teams in essential functionalities, as we recommend at 10:11.‍
3. Training and support
   Even the most intuitive tools require a certain level of familiarization. Offering customized training and ongoing support can help remove barriers to entry and encourage wider adoption within the organization.‍
4. Data governance and security
   Opening access to data to a greater number of users raises questions of security and data governance. Establishing role-based access controls, permissions, and accurate access rights is imperative to ensure that sensitive data remains protected while being accessible to those who need it.‍
5. Collaboration between IT and Business
   For self-service analytics to deliver its full value, close collaboration between IT teams and business users is crucial. This ensures that the tools are well aligned with strategic goals and effectively meet the needs of end users.‍
6. Ongoing Assessment and Adjustment
   Finally, standalone analytics is not a “set and forget.” It is essential to regularly assess the use and effectiveness of the tools deployed and to be ready to adjust the situation. Gathering user feedback and analyzing usage metrics can provide valuable insights to continuously refine the autonomous analytics strategy.

By following these steps, organizations can not only successfully deploy self-service analytics but also maximize its impact, transforming each employee into a key player in the data-driven culture.

Towards an Autonomous Decision-Making Future

Self-service analytics is essential for any business that wants to democratize access to data and insights for all. If your goal is to promote data-based decisions and add value to your product or organization, exploring how autonomous analytics can help is a key step in your journey.