The future of semantic layer – trends to watch out for in 2021 and beyond

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Richard Makara
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Have you ever heard of the term "semantic layer"? It's a crucial element in the field of data analytics and business intelligence that helps organizations make sense of their data. But what does the future hold for this technology? With advancements in artificial intelligence and machine learning, the semantic layer is set to undergo some exciting transformations in the coming years. In this article, we'll explore the top trends to watch out for in 2021 and beyond, and what they mean for the future of data management.

What is a semantic layer

A semantic layer is a virtual layer that sits between the raw data and the end user, providing a simplified view of the data and making it easier to use and understand.

It acts as a translator, converting complex data structures and terminology into terms that are more familiar and meaningful to the end user.

The semantic layer is made up of metadata, or data about the data, including definitions, relationships, and business rules.

It allows users to query data using common terms and concepts, without needing to know the specific technical details of how the data is stored or organized.

Overall, a semantic layer helps to bridge the gap between the technical complexities of data management and the practical needs of business users.

Benefits of using a semantic layer

When it comes to business intelligence, the benefits of using a semantic layer cannot be overemphasized.

First, a semantic layer acts as a business-friendly layer, as it removes the need for users to have technical knowledge about databases and query languages. This means that business users can access data and easily generate reports without relying on IT departments, reducing the burden on IT teams and enhancing productivity.

Second, a semantic layer can help to improve data consistency across different applications and databases. By providing a standardized metadata layer, the semantic layer ensures that data is interpreted consistently by different users and applications, reducing the risk of errors and inconsistencies.

Third, a semantic layer can also enhance data security by providing controlled layer access to different users and groups within an organization.

Lastly, a semantic layer can improve data performance by optimizing queries and reducing the impact on source databases, making it possible to extract insights from large and complex data sets in real-time.

Overall, the benefits of using a semantic layer are numerous and they’re essential to enabling organizations to effectively leverage data and derive valuable insights that drive business growth.

Top trends to watch out for in 2021 and beyond:

Increased use of artificial intelligence and machine learning

  1. AI and machine learning algorithms will continue to play a significant role in enhancing the semantic layer's capabilities.
  2. These technologies can help automate the identification and tagging of data, making it easier to analyze and access insights.
  3. AI-powered tools can also assist in data quality and integrity checks, ensuring that the semantic layer's data remains accurate and up-to-date.
  4. Machine learning algorithms can learn from past queries and usage patterns, optimizing future data retrieval and analysis.
  5. AI-based predictive analytics can assist in anticipating user needs and surface relevant insights proactively.
  6. Overall, increasing reliance on AI and machine learning will help organizations extract even more value from their semantic layer investments and streamline data-driven decision-making.

Focus on data governance and security

As the importance of data grows, so does the need for proper management and security measures to ensure its integrity. In response, a focus on data governance and security has emerged as an essential trend to watch out for in the future of semantic layer technology. Here are some key points to keep in mind:

  1. Data Governance: This refers to the establishment of policies, procedures, and standards for data management. Companies are focusing on the governance of semantic layers to ensure consistent usage and standardization across the organization.
  2. Data Privacy: With data being increasingly at risk of hacking or theft, there has been a heightened focus on privacy and security. This includes proactive measures to safeguard data while it's in transit and when it's at rest.
  3. Compliance: With the rise of data protection and privacy regulations such as GDPR and CCPA, organizations are focusing on compliance within their semantic layer technology to demonstrate that they are aligned with regulatory requirements.
  4. Monitoring and Auditing: To maintain governance, security, and compliance standards, organizations are implementing monitoring and auditing processes within their semantic layer technology to ensure best practice is being followed.

In summary, ensuring proper data governance and security within semantic layer technology is vital for organizations to maintain data integrity, prevent data breaches, and demonstrate compliance with regulations.

Integration with natural language processing

One of the trends to watch out for in the future of semantic layer is the integration with natural language processing (NLP). NLP is a subset of artificial intelligence that deals with the interaction between computers and humans, specifically concerning human language. In simpler terms, it allows machines to interpret and understand human language to a certain extent.

The integration of NLP with the semantic layer will enable users to query data using natural language, making data analysis much easier and efficient. This means that users can ask questions in their natural language, and the semantic layer will understand and convert the queries into machine-readable language, which can then be analyzed and visualized.

Furthermore, NLP can help in data cleaning and preparation, which is a necessary step in any data analysis process. By using NLP algorithms, the semantic layer can identify patterns and relationships in the data and suggest appropriate cleaning techniques based on the natural language queries.

Overall, it can be concluded that the integration of NLP with the semantic layer will enhance user experience and enable more effective data analysis through the use of natural language queries.

Expansion of semantic layer capabilities to include unstructured data

The expansion of semantic layer capabilities to include unstructured data is a trend to watch out for in 2021 and beyond. Unstructured data includes text, images and videos that are not organized in a predefined way. Currently, semantic layers only work with structured data like databases. However, as the amount of unstructured data increases, the need for semantic layers to integrate with it also increases.

This trend brings with it several benefits. For instance, it allows companies to get a more comprehensive understanding of their data. With unstructured data, there are often insights that a company may miss by only analyzing structured data. Semantic layers also make it easier to analyze unstructured data by making it more accessible.

Expanding semantic layer capabilities to include unstructured data requires the use of advanced technologies like natural language processing, machine learning, and artificial intelligence. With these technologies, it is possible to extract insights from unstructured data. Companies that integrate these capabilities into their semantic layer will be better equipped to compete in the data-driven world.

Overall, expanding semantic layer capabilities to include unstructured data is a trend that is set to transform the way companies analyze data. By including unstructured data, companies can get a more comprehensive understanding of their data, and better insights that can be leveraged to improve their operations.

Use of cloud-based technologies for semantic layer deployment

One of the top trends for the future of semantic layer is the use of cloud-based technologies for its deployment. This means that instead of hosting the semantic layer on-premises, it will be hosted in the cloud. The main benefit of using cloud-based technologies is scalability. It allows organizations to easily scale their resources up or down depending on their needs.

Another benefit of cloud-based deployment is convenience. It eliminates the need for organizations to maintain their own hardware and software for hosting the semantic layer. Instead, cloud providers take care of all the maintenance, upgrades, and security for the hosting infrastructure.

Finally, cloud-based deployment ensures continuous availability and redundancy. Cloud providers have multiple data centers located in various geographic locations, which means that even if one data center goes down, your data and semantic layer will still be available.

In summary, using cloud-based technologies for semantic layer deployment offers scalability, convenience, and continuous availability. As more organizations adopt cloud-based technologies, this trend is expected to continue to grow in 2021 and beyond.


The semantic layer in business intelligence (BI) tools is crucial for enabling non-technical users to access and understand data. In 2021 and beyond, trends such as natural language processing, augmented analytics, low-code and no-code solutions, cloud-based technologies, and a focus on data governance are shaping the future of the semantic layer. By embracing these trends and investing in advanced technology, organizations can improve their decision-making capabilities and drive growth.

However, it is essential to also prioritize data quality and security to ensure accurate and trustworthy insights.


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