Self-Serve Data Acces

Learn more here about how Satori simplifies Self-Serve Data Access.

Until recently, the paradigm for data access was mainly that of data engineers or DBAs that authorize data consumers to access data. Self-Service Data Access is a new paradigm that strives to provide tools to a wide range of users to access, manipulate and provision its data assets. This paradigm change intends to drive data democratization, allow greater agility, and generally enhance user usage of data assets throughout an organization or company.

The Common Reasons of Self-Service Data Access Adoption

  • Increased pace for business activities: Business processes have become much more time-dependent, and users cannot afford long periods to get data insights. Therefore, there has been a need to empower users to be able to fulfill their data requirements.
  • A more significant number of data sources: Companies and organizations have increasingly collected data from various sources. This large and diverse data enables new insights from existing data thanks to aggregation and enrichment derived from new sources, ultimately leading to operational advantages. It has been of paramount importance to empower users to use these new insights to exploit these new insights.
  • Widespread adoption of data-driven culture: Organizations are leading data democratization initiatives that aim to provide users with new sets of skills to be able to use data for decision-making, regardless of their position and role. This is based on the concept that open data access can reduce the error derived from human bias in the context of decision-making at all levels. A more significant number of users can now create their requirements for data assets and operate the tools required to fetch and visualize them.

In response to the new user’s need for self-service in the context of data, providers, and vendors have been progressively adding tools that support self-service for data visualization, analytics, business intelligence, and data aggregation.

For these systems and tools to work effectively, several conditions need to be fulfilled.

  • Data repositories for self-service: Data access with the appropriate restrictions and permissions needs to be ensured for all users who require them. To provide an ordered way for users to access the data, data engineers can create data marts or data repositories for specific users. These users can use these repositories and self-service data tools for visualization, analytics, and exploration.
  • Metadata repositories and data catalogs: Exploring digital assets is of paramount importance to be able to use self-service data tools. Metadata repositories and data catalogs enable users to freely explore the catalog of available data assets in the context of data discovery initiatives that ultimately seek to derive new insights from existing data.
  • Access Control Management: Data repositories should be clearly defined access control policies that limit user access depending on the role and need. This way, secure applications and insights can be constructed with self-service tools without compromising security.


The use of analytics sandboxes can be especially beneficial to address these self-service data access needs. It enables the isolation of sensitive data within a cloud environment to control the distribution of data assets and analytics being run on it.

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