When it comes to Business Intelligence (BI) and Business Analytics (BA), you may have come across software which uses a semantic layer in order to run business reports.
A semantic layer is a layer of abstraction that provides a consistent way of interpreting data. The idea is to get all the definitions and business logic in one place and then manage and change them centrally. The basic purpose of the semantic layer is to make data more useful to the business and simplify querying for the users.
Why do you need a Semantic layer?
Think of all the data that comes into your data lake each day. How do you feel about that data? For a business user who needs to analyze all that data, it’s hard to figure out how to start with the data. Without a semantic model, it is difficult for a user to identify the appropriate customer key, customer ID, or date position. Different fields can mean different things to different people. Each team or user will then interpret those fields in different ways and get a different view of the same data.
Most of the BI tools allow users to define their own semantic models– dimensions, measures, and the hierarchies etc. One option is to let the business users create their own semantic models in the tools that they use. However, achieving a single source of truth is difficult in this case. It is necessary to have a common representation of data so that different teams can access their data using common business terms.
Once you create a universal semantic layer, the same model is available to all business users regardless of the BI tool they use. They can work on Excel, Tableau, PowerBI, Looker or any other tool they like, and access the same semantic model. This helps create a consistent view of data for users across the enterprise.
How do you build a semantic layer?
A semantic layer is constructed by someone who understands both how data stores work, and how reporting needs to be done in the business. This person then creates a layer by naming the raw data fields into business-friendly terms, and hiding fields that aren’t needed.
This person can also create filters that might commonly be used, so that end users can run reports looking for the specific information they need, without having to know anything about the way the data storage is constructed.
So, for example, the person building the semantic layer might organize some data in terms of the year. They can then create a predefined filter for “This year”. When run, this would provide the data needed for the current year.
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About Me:-
I am Om Prakash Singh – Data Analytics Consultant , Looker Consultant , Solution Architect .
I am Highly analytical and process-oriented Data Analyst with in-depth knowledge of database types; research methodologies; and big data capture, manipulation and visualization. Furnish insights, analytics and business intelligence used to advance opportunity identification.
You’ve got data and lots of it. If you’re like most enterprises, you’re struggling to transform massive information into actionable insights for better decision-making and increased business results.
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