The top five challenges a semantic layer can solve
In recent past the term semantic layer is frequently pop-up in data-driven AI talks and articles. It has been invented to mold relational databases and their SQL dialects into an approachable Interface for business users. Now the concept of measure and dimensions as an abstraction of SQL has become the preferred language for business users.
In this article I am trying to identify few key business challenges which can potentially be solved by semantic layer.
There are common problems that crop up without a semantic layer facilitating decision-making in an organization involved in data-driven business.
We can group these problems into five areas:
1. Vendor lock-in for BI tools: Many large organizations are strongly bounded with one or two tools which restrict them to select must appropriate tool designed for specific use-case. Data semantic layer can play an important role to deal with this vendor lock in problem.
2. Lack of Data Access: Gartner reports that 87% of organizations have low BI and analytics maturity. Business organizations might have abundant data, but your data consumers struggle to make sense of it — and it’s hampering the speed at which they c make accurate decisions. A semantic layer eases this pain by powering your data model with crucial context to aid decision-making.
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3. Integration with different Platforms/Tools: Business today moves quickly, and waiting for a centralized data team to produce reports and dashboards for different departmental use cases is considered to be critical issue in data-driven business processing. Use of semantic layer will ensure seamless integration with different platforms.
4. Inconsistent BI reports : Of course, having multiple BI tools across the organization results in differing results for similar queries. Each BI tool com with its own modeling layer, and all of them support custom calculations, so it’s easy enough to create wildly diverge reports off of the same data.
5. Reliability of DATA: Experian reports that 6 in 10 companies believe that high-quality data increases business efficiency, 4 believe it raises consumer trust, 43% conclude it enhances customer satisfaction, 42% believe it drive more informed decision-making, and 41% report that good data cuts costs. However, always the key question is "IS this Data Manipulated?".
use of Semantic layer will help to resolve these issues of data reliability.
#Semantic layer #data semantic #atscale
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1 个月Very informative