[REPLAY] Product Talk: Using AI to enhance the data marketplace search experience

Watch the replay
Glossary

Self-Service Business Intelligence

Self-service business intelligence (BI) solutions enable a wider audience to successfully benefit from business intelligence tools. Explore what self-service BI is, its advantages, and essential capabilities.

What is Self-Service BI?

Self-service BI seamlessly combines business intelligence tools with the democratized use of data. It enables all employees to explore and analyze organizational data without relying on the IT department or specialist business analysts. This simplified access to data and analysis fosters the development of a data-driven culture in the company and enhances decision-making.

Why Choose a Self-Service BI Solution?

Limitations of traditional BI tools:

Traditional BI tools are typically designed to be used by data or IT experts (such as data analysts, data scientists, data stewards, data engineers, or BI specialists). These experts create reports and dashboards, conduct advanced analysis, and add data sources, before providing these insights to decision-makers who then make strategic choices based on them.

This model prevents business experts from accessing data themselves to gain faster insights into their activities. They have to go through IT, and ask them to analyze data in specific ways. This relies on having and communicating a proper understanding of their needs and then waiting for the appropriate reports to be created, which can be time-consuming and counterproductive.

Moreover, the complexity of traditional BI tool interfaces may lead to frustration among employees who find them difficult to use effectively. This in turn leads to a lack of interest in using data as a valuable asset for the organization.
85% of decision-makers consider “data usage as an important development driver” for their organization. Facilitating data usage is crucial to enabling this, leading to the introduction of the self-service concept in BI tools.

Benefits of Self-Service BI

Unlike traditional BI tools, self-service opens up data to everyone, even non-experts, allowing them to use, explore, and exploit information effortlessly. Organizations can make decisions more rapidly as there’s no longer a need to involve IT services or data experts to create reports. Additionally, enhanced data accessibility fosters collaboration and information exchange, further contributing to the development of a data-driven culture.

Key Features for Self-Service BI

To fully leverage a self-service BI solution, it should contain essential features that include:

  • Data Connectivity: The ability to connect data, ensuring interoperability regardless of source, format, or tool used.
  • Data Correction and Modification: Simplification of data cleaning (removing duplicates, inaccurate, or outdated information) and reformatting.
  • Data Enrichment: Cross-referencing internal (corporate data) and external information (such as customer data, partner data, supplier or public data).
  • Data Visualization Creation: The ability to design and create dashboards, charts, maps, and data stories to make data accessible and understandable for all.
  • Sharing via Portals or APIs: Making it easy to distribute data to colleagues, clients, partners, and the public, facilitating data reuse and value creation.

Opendatasoft promotes internal and external data sharing by enabling the creation of data portals that make it easy to find and reuse all of an organization’s data assets, without requiring technical skills.

 

Ebook - Data Portal: the essential solution to maximize impact for data leaders
Learn more
What is a data catalog? Data access
What is a data catalog?

Organizations now generate an enormous range of data assets across their operations and departments. Harnessing this data successfully starts with understanding what data is available and where it is located through centralized data catalogs. This blog explains what they are and how they can benefit businesses.

Building a successful data business – lessons from McKinsey Data services
Building a successful data business – lessons from McKinsey

How do organizations tap into the revenue benefits of creating external data products and services at scale? Based on a new report from consultants McKinsey, we explore the foundations required to industrialize the delivery of successful external data products.

What is the difference between a data product and a data asset? Data Trends
What is the difference between a data product and a data asset?

Data products and data assets both aim to make data usable and valuable. What are the differences between the two and how do you incorporate them into your data strategy?

Start creating the best data experiences