Understanding the Role of the Semantic Layer in Enterprise Data Discovery

Posted by Kirk Newell on Mar 29, 2018 4:08:00 PM
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Organizations today view their data assets as key business drivers for competitive advantage. However, the cost of running analytic solutions is drastically increasing, while speed-to-deployment remains a major challenge.

architecture-1549090_1920.jpgWhether you are in IT and focused upon digital transformation projects, seeking to reduce costs, speed development cycles, or a business user under pressure to deliver valuable insights now in this new on-demand, data-driven world, enterprises are seeking a comprehensive data analytics solution.

The good news is that the complex process of bringing together different data sources throughout an organization is now being automated to create a single “semantic layer” of an organization’s data.

Semantic technologies have always been seen as a great way to potentially integrate data since it greatly simplifies the sophisticated modeling needed to connect information from multiple sources including text, but up until now it was not possible to scale these solutions. They tended to stop at the departmental level at best before they ran out of steam. Querying them was just too slow to make them useful in practice.


The traditional way of preparing data is a business person figures out their question and then often works with a team of IT people who find, clean, integrate, and then extract the data needed to answer that question. This waterfall approach is slow and usually requires many iterations and baton passes to get right. It also requires the business side of the house to formulate and narrowly specify their questions in advance (often without even knowing what data is available) so that additional or altered questions go to the back of the queue.

Cambridge Semantics’ Anzo Smart Data Lake 4.0® offers an end-to-end, enterprise-scale open platform that creates a single semantic layer of an organization’s structured and unstructured data. The resulting fully governed data fabric is fully capable of managing all enterprise data, while also enabling users to conduct code-free, rich interactive discovery and analytics at speeds more than 100x faster than competing approaches.

With the open standards approach that Cambridge Semantics incorporates, the connected data is so well-described at a business level using semantic layers that it can quickly be combined and reused in any manner. This allows the enterprise to become far more agile with quick iterations and fast pivots to evolve and tackle follow-on or new questions immediately. The semantic layer enabled by ASDL 4.0 provides both a venue and a means of translation of all of an enterprise's data to the language of the consuming user, department or organization from how it’s initially generated and stored in any number of proprietary formats from multiple different siloed sources and even documents.

The semantic layer is easily accessible to all users in the network who can use it to quickly extract the answer to their particular problem. You don’t need to go through several people and long iterative processes to manipulate data into the form that you need to answer a question as it’s all stored, connected and queried in one convenient location.

Organizations use ASDL 4.0 and the semantic layer to more quickly achieve insights that ultimately promote faster and more informed decision-making based on a broader and deeper understanding of all the information available.

Only with a rich and interactive semantic layer can the data and analytics stack deliver true on-demand access to data, answers, and insights, weaving data together from across the enterprise into an information fabric.

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Tags: Semantics, Hadoop, Data Lake, Smart Data Lake