The ultimate guide to the semantic layer
Businesses that can grow their use of analytics, AI, and data will come out on top in the current spectacular wave of digital transformation sweeping across every industry. Insights are useless if they are delivered in languages that nobody understands or are kept segregated and out of the reach of the majority of business users, regardless of how sophisticated the technology you employ to unearth them may be.
This comprehension led to the creation of what is now known as the semantic layer (SL). Read this article if you need clarification on what it's all about.
What is the semantic layer?
It is a layer of abstraction between your application and the data source. This layer creates meaning for your users, so they can easily understand what they are looking at and interact with it. And it uses machine learning technologies like Natural Language Processing (NLP) and Deep Learning to help you build apps faster by automating tedious tasks such as data cleansing or natural language understanding.
SL allows organizations to manage data complexity. The success of an enterprise data warehouse depends on the quality, governance, and accessibility of its documents—and, thus, its ability to provide business value. It enables organizations to achieve this by allowing them to define their taxonomies for each document type.
This makes it easier for users within an organization or across multiple teams working together in disparate silos (for example, finance versus marketing) to find relevant information about any given topic without needing access directly through another department’s system or application.
Challenges of building SL
You’ll need to address several challenges as you build out your SL. These include:
- Data governance
- Data silos
- Data quality and accuracy (e.g., the accuracy of images, the validity of user-generated content)
- Security and privacy concerns
How can you build SL?
Using a semantic layer framework
A framework is a set of tools and services that help you build your application but isn't specific to one technology or programming language.
Using an SL management system (SLM)
An SLM provides various services, such as monitoring and maintaining data stored in HDFS (Hadoop Distributed File System). It also manages access control privileges based on user roles so that only authorized users have access rights over specific data at their respective granularity levels.
Table level or column level are within the underlying database tables themselves, otherwise known as row-level security versus column level security respectively which differs depending upon whether you're using Apache Derby OR Oracle SQL.
Benefits of SL
SL can benefit your organization in a variety of ways. It makes it easier to do data integration, which means you can access and use data from multiple applications, reuse the same information across different applications, and share that information with others.
One of the main benefits of SL is that it reduces the need for manual data entry, which can be time-consuming and error-prone. When you have a semantic layer in place, users can enter their data directly into your application without manually entering it one by one. This makes it easier for them to get their work done more quickly, reducing delays caused by waiting on human input.
Another benefit is that when people share their information with other systems or departments within an organization. It becomes easier for them to share accurate information because all parties are using the same language (i.e., vocabulary).
With the proper approach, SL can easily make your data more accessible and valuable. It can help you manage data complexity, improve search performance and deliver better user experiences. It doesn't just make your reports easier to read but also reduces the amount of time you spend on data entry and manipulation.
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