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Why your BI strategy needs a universal semantic data layer

Thor Olavsrud | Nov. 11, 2017
A universal semantic data layer is a single business representation of all corporate data aimed at helping users access data using common business terms via the BI and analytics tools of their choice.

To make the most of your corporate data, your analysts should have universal access to data that can be understood by their tool of choice. But the siloed nature of data repositories, coupled with semantic data layers tailored to specific BI tools, have long scuttled that goal. Enter the universal semantic data layer, which, when applied to a data lake, can give your BI strategy a universal boost.

What is a universal semantic data layer?

A universal semantic data layer is a single business representation of all corporate data. It aims to help end users access all corporate data using common business terms via the business intelligence (BI) and analytics tools of their choice.

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The concept of a semantic layer underpinning BI platforms has been around for some time. It was patented by Business Objects in 1991, and the patent was successfully challenged by MicroStrategy in 2003. But these semantic layers have always been purpose-built for specific BI tools, used by specific teams within the enterprise.

In the past decade, the advent of the data lake — a single repository of all enterprise data stored in its native format — gave rise to the promise that enterprises would be able to access all their data with whatever BI or analytics tools they chose, without having to move the data.

But that promise hasn't been realized, says Dave Mariani, co-founder and CEO of startup AtScale and former vice president of development, user data and analytics at Yahoo. Mariani says the missing piece is the universal semantic data layer.

The advantages of a universal semantic data layer

"Data lakes are just collection areas for files," Mariani says. "Without semantics on top of those, it's impossible to get any value out of it. I think of it as an abstraction layer. We're abstracting how the data's stored and where it's stored. We're taking what is essentially raw data and we're giving it semantic meaning for the business."

Consider the concept of "net sales," writes Matthew Baird, co-founder and CTO of AtScale.

"Is it net of invoice line-item costs and/or net of rebates? A small use case may contain tens of these calculations, while a departmental model may contain hundreds," Baird writes. "Without some level of abstraction, business is beholden to IT to generate and run reports or risk making big, costly, and worst of all, hidden mistakes. Can you afford to have each of your employees independently trying to replicate this logic correctly in their spreadsheets and reports? Will you be able to catch the subtle yet impactful errors?"


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