Semantic Layer- Understanding What it is & Why Business Enterprises Need One

A semantic layer is a commercial illustration of corporate data which assists the end users access the data separately by using familiar business terms and maps complex data into known business terms like product, customer, or profits to provide an integrated and combined view of data across the business enterprise.

The concept of semantic layer is not completely new, but what’s the catch? Keep in mind that the semantic layer is an abstraction & while having several abstractions for the similar concept is often accurate and valuable, usually it goes against the ‘don’t repeat yourself’ concept.

For staying great, great concepts should grow. The ideas of a business intelligence semantic model introduced nearly 25 years ago have been mostly static. Believe in the value of modern business intelligence platform is in addressing identified weaknesses and innovating on what initially made the semantic layer amazing- it is a power multiplier for the data consumers. 

Comprehensive understanding of calculations and original data structure took a backseat to generations of helpful operational reports, dashboards & other analysis created through people that know the business domains & not calculations.

Exploring the Massive Alterations Which Happened & Influenced Data & Its Semantic Layer Have Experienced In the Last Decade:

• Quantity: Huge volumes of data are being generated every day and in 2020, it is expected that 40 zettabytes of data will be produced. The value of aggregates is through the culmination of both decreased maintenance and capability to more speedily analyze these huge quantities of data. Data is not going to waste in anyway, it will just grow constantly. Be ready to handle the massive volume coming towards you.

• Speed: The previous semantic layer approaches the data that had a static development stage and are extremely slow to keep pace with the ambush of data today and in the future. By working with the biggest business enterprises in the world, the latest adaptive cache that can automatically plan from source to amass and can perform additional updates. Eventually, business enterprises get access to the latest data accumulated without IT department being involved or keeping the company waiting. It is all about your speed to respond them back.

• Diversity: The latest semantic layer can make the semi-structured data appear to be structured; however it is relational at the back of its abstractions, which means that business users can keep making use of your standard visualization tools which have no idea of new or machine-generated original formats without any requirement for complicated, difficult to maintain and no-use-to- add repetitive data movement. No requirement to retrain the end-users on a new data visualization UX.

• Authenticity: Uncertainty can approach you in several different forms and business users do not have faith in the data they make use of to take informed & right decisions. The abstractions deliver proven & tested structures & calculations which consumers can believe in.

Key Benefits of Semantic Layer for Business Enterprises:

• Usability: One of the challenges that business enterprises face is that it takes way very long time for the IT department to generate or change reports for them. A perfectly created semantic layer with responsive tooling enables the users to know how adjusting their question will result in different results, while simultaneously providing them independency of the IT department & making them confident that their results will be right.

• Authority & Safety: Currently, business enterprises has strong and often regulatory, needs that they track and identify the ‘who’ saw ‘which’ data and ‘when’. The total of your real allocation product came from a joint venture with your incredible customers that enables them to identify who, what, and when in a strongly secured data lake. Before manifestations of the semantic layer was deficient to track the line of data from row level to each aggregate handled through the software.

• Potential: More mature and complex analytics coupled with modern business intelligence vendors introducing the latest abilities equal to abstraction necessary. These new generation of analytical and machine learned analytics are both not support through business intelligence through tools and very complicated to expect business users to know the execution rather than just the preferred outcome.

• Accuracy: The semantic layer outshines to create difficult SQL and at times multiple SQL statements in reply to an extremely simplified group of user gestures. The semantic layer should know how to manage the database loops, aggregate table navigation, difficult objects, complex sets and connect shortcuts. Through applying rules to illustrate the database difficulty and uncertainty the generation of the SQL assures that if multiple users request the same data then, they will get the same results and this is the key feature of the semantic layer, enabling a business enterprise to set a single version of the reality.

• Scale & Performance:  Both multi and tabular models have certain strengths and weaknesses at present and one is not evidently better than the other.

Being a leader in your business intelligence groups, whether you are on the business or tech side you must have an excellent intellect of when you require semantic layer since one size does not fit all.