How a semantic layer connects business intelligence and artificial intelligence

A semantic layer is a business-oriented representation of data that enables easier access for end users through the use of standard, business-friendly vocabulary.

Main Highlights:

A critical stage in an organization’s data maturity is progressing beyond simple historical analysis to making accurate forecasts about the future. Historically, business analysts concentrated their efforts on historical research, whereas data science teams sought to uncover intriguing insights about the future.

Today, with the emergence of the semantic layer, these two compartmentalized worlds are reuniting. Businesses that combine these two disciplines may give augmented analytics, which enables everyone to have a deeper understanding of the past and forecast the future.

Analytical techniques

Businesses use analytics to have a better understanding of and improvement in their business operations and consumer satisfaction. Before we proceed, let us describe the four types of analysis frequently encountered in an organization, each with a progressively higher degree of sophistication.

As the table above illustrates, business users often focus on historical analysis, whereas data scientists forecast the future. It’s self-evident that business users who can predict the future make better judgments. Additionally, it is self-evident that data scientists create more accurate models to compare their predictions to what occurred. In other words, both historical and predictive analysis is necessary for both teams, but they seldom intersect.

semantic layer

What does the term “semantic layer” mean?

A semantic layer is a business-oriented representation of data that enables end-users to access it more quickly through standard, business-friendly words. A semantic layer converts complicated data linkages into simple-to-understand business terms, allowing the company to have a uniform, consolidated view of its data. A semantic layer has the following advantages:


One of the most frequently voiced complaints from the business is that IT takes much too long to create or send reports. Users desire control over their future, and subject-matter specialists (not information technology) are most equipped to utilize data to enhance the firm. A well-designed semantic layer abstracts the complexities of data’s physical shape and placement while converting it to intelligible business abstractions. By making data easy to use, a semantic layer liberates business users and data scientists from relying on IT and data expertise.

Protection and governance

Today, businesses are subject to stringent and sometimes governmental responsibilities to keep track of “who” viewed “which” data and “when.” A contemporary semantic layer enables users to appear to underlying data systems as themselves from any consumer tool. Simultaneously, a semantic layer maintains data consistency independent of the consumption method and ensures everyone follows the same (governance) norms.


Analytics agility commonly referred to as “time to insight,” refers to the period between when data is received and when it may be used to make choices. Data access for BI tools that need data imports, extraction, or cube creation can take anywhere from minutes for little data to days/weeks for massive data. A contemporary semantic layer takes advantage of data virtualization to ensure that any new data that enters your data warehouse is immediately queryable by your business intelligence tool, regardless of its size.

Performance and scale-

Cubes and data extracts were designed to address the performance concerns associated with analytics and data platforms. This technique results in data duplication, increased complexity, reduced agility, and introduced delay. A contemporary semantic layer optimizes performance independent of the underlying data model, whether it is a snowflake, a star, or solely OLTP.

By automating the creation and management of aggregates or materialized views inside the underlying data platform, a semantic layer learns from user query patterns and optimizes the data platform’s speed and cost without requiring data migration.

The semantic layer 

Using a semantic layer, you may bridge the divide between business intelligence users and data science teams. This enables your teams to collaborate and operate transparently with the same information and goals.

Using a business model, a semantic layer abstracts away the complexity of underlying raw data, enabling any data consumer to access quantitative measurements, properties, features, forecasts, business hierarchies, and sophisticated computations through an intuitive, easy-to-understand interface.

A semantic layer solution renders this user-friendly interface in the “language” of their tools (SQL, MDX, DAX, JDBC, ODBC, REST, or Python), converting queries to the cloud platform’s dialect. With a shared set of business terms, both teams may interact with the same data, adhere to the same governance standards, and provide the same outputs, regardless of the technology used.

With both teams working on the same semantic layer solution, data scientists and business analysts may discuss (or publish) their created features and predictions. In contrast, business analysts offer input to data science teams on the quality of their forecasts and model drift.

Once data becomes widely available, teams may work not just within their four walls but also with data from secondary and third-party sources, democratizing data and analytics. Closing the divide between business intelligence and data science teams is critical for attaining a high degree of data analytics maturity and scaling all sorts of analytics.


When business and data science teams connect via a semantic layer, they may add predicted insights to their historical data. By bridging the divide between business intelligence and data science teams, businesses have more insight into the output of data science programs across the enterprise. They can harness their data for predictive and prescriptive analytics.

Augmented intelligence (a.k.a. augmented analytics or decision intelligence) integrates AI-generated insights into standard business intelligence procedures to improve data-driven decision-making.

When most individuals consider augmented intelligence, they see specific capabilities in AI-assisted business intelligence solutions. For instance, some business intelligence (BI) products use natural language query (NLQ) or outlier analysis to assist users in posing better inquiries or locating the needle in the haystack. These are helpful features, but they are tool-specific and may behave differently among tools.

By contrast, they combine business intelligence with data science results in adding AI-enhanced data to the semantic layer, delivering consistent insights throughout the consumer spectrum regardless of the tool utilized. In essence, a semantic layer magnifies the impact of the data science team by disseminating their work to a broader audience and enabling that audience to provide input on the quality and value of their forecasts – a win-win situation.

Unleashing the potential of enhanced intelligence

Businesses may be transformed into data-driven companies through the use of augmented intelligence. This begins with establishing the necessary procedures and tools for democratizing data and empowering users to use it through self-service analytics.

Every business wishes to empower every employee to make data-driven decisions. A semantic layer may serve as the vehicle for delivering enhanced intelligence to a larger audience by releasing the outcomes of data science projects through current business intelligence channels. Your company may profit from more than simply historical analysis by feeding the results of data science models back into the semantic layer.

Decision-makers can use predictive analytics in conjunction with historical data. They can also reliably “dig down” into the nuances of a forecast using the same regulated data. Consequently, your business may promote more self-service and data science knowledge, as well as increase the return on data science expenditures.

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