HUMAN-CENTRIC SELF-SERVICE ANALYTICS
Easily connect to any SQL-database, and CHAT WITH YOUR DATA™ auto-generates a visual semantic layer, i.e. Knowledge Graph, with relationships between entities for you. In a few minutes, instead of days, you can start chatting with your data and get instant answers with a simple, AI-powered search.
Advanced data modelling language for semantics, allows you to define semantics, use ontologies, add synonyms, relationships, hierarchies and much more, enabling the most accurate and natural Q&A experience.
Frequently Asked Questions
A semantic layer is a business-oriented abstraction layer that sits between the underlying database and the end-users. Essentially, it acts as a translation layer that simplifies complex data into terms and relationships that are easily understood by business users.
This makes it possible for non-technical users to query the database and generate reports without having to understand database languages like SQL, or the intricacies of data structure and storage.
In a typical database setup, data is stored in tables and requires specific queries to access and manipulate it. These queries are usually written in database languages like SQL, which may be complex and difficult for non-technical users to grasp. A semantic layer simplifies this by presenting the data in terms that are familiar to business users.
For example, a database may have tables for 'Products', 'Customers', and 'Sales'. A semantic layer would translate these into more accessible terms like 'Items Sold', 'Client Information', and 'Revenue', and allow users to interact with the data using these terms.
User-Friendly: Enables people with limited technical skills to interact with data effectively.
Standardization: Provides a consistent set of terms and definitions, ensuring that everyone in the organization is on the same page.
Security: Can be configured to limit data access at various levels, providing an additional layer of data security.
Efficiency: Speeds up the data retrieval process as users don’t have to create complex queries from scratch.
Data Integrity: Reduces the risk of errors by limiting direct interaction with the database, thereby maintaining data integrity.
Flexibility: Allows technical staff to make changes to the database without affecting the end-user experience, as the semantic layer will still present data in the same user-friendly format.
A semantic layer is primarily used in Business Intelligence (BI) and analytics platforms but can be useful in any context where data is being accessed by non-technical users for reporting and decision-making. It is an essential component for companies that aim to be data-driven but have a varied user base with different levels of technical expertise.
In summary, a semantic layer plays a crucial role in making data accessible and useful to all members of an organization, thereby enabling smarter business decisions.