Trend #3: Semantic layer
Posted: Tue Feb 11, 2025 5:01 am
Last but not least on our list of top enterprise data management trends, is the semantic layer.
A semantic layer maps data to familiar business terms such as product, customer, or revenue, and offers a unified, consolidated view of your data across the organization. It is important because it eliminates data brawls and speeds time-to-insight by aligning stakeholders to shared, consistent definitions of data.
In fact, the promise is so great – and the demand so high, that dbt Labs is making a substantial investment to add a single open semantic layer of dbt code. According to dbt Labs CEO and founder Tristan Handy, “This will solve, once and for all, one of the biggest problems in our space: the ‘single-source-of-truth’ problem.”
At data.world, we’ve believed in the power of the semantic layer since day one – it’s why our data catalog was the first in the space to be built on a knowledge graph. Knowledge graphs are greece whatsapp number data modern data infrastructure that connect data and metadata to all users in an organization, providing a single semantically organized view of your data. They are unique in that business terminology is represented as concepts and relationships that are both understandable by people and machines in the exact same way.
data.world customer Luke Slotwinski, VP of Data and Analytics at Prologis, appeared on Catalog & Cocktails to discuss how his team leverages the semantic layer to answer the why behind data challenges.
What do all three trends have in common?
The tie that binds all three trends together is data mesh. (What, you thought you’d read an article on enterprise data management that doesn’t mention it?)
“Rather than checking every single air waybill for errors manually, the European Green Fast Lane program advises us which of them have errors, and marks the location of the errors,” said Spoor. When freight forwarders share shipment data and pre-identified errors earlier, cargo handlers can anticipate and prepare for the required checks.
Warehouses are now being modified to handle more trucks per hour, and according to the European Green Fast Lane website, “the amount of concrete information we now have is leading to real process change” and “facilitating conversations about how things are working in the real world, leading to clarity, cooperation, and understanding.”
Knowingly or not, many organizations are already practicing – or pursuing – data mesh principles. That’s because, despite its detractors, data mesh offers a new approach to enterprise data management that instills hope in data and analytics teams with the promise of brighter days ahead.
Want more mesh? Join us on April 7th for the data.world spring summit where data.world co-founder and CPO Jon Loyens, Monte Carlo co-founder and CEO Barr Moses, Gary George from Indeed, and a special guest from Snowflake will participate in a panel discussion on what comes next for data mesh.
A semantic layer maps data to familiar business terms such as product, customer, or revenue, and offers a unified, consolidated view of your data across the organization. It is important because it eliminates data brawls and speeds time-to-insight by aligning stakeholders to shared, consistent definitions of data.
In fact, the promise is so great – and the demand so high, that dbt Labs is making a substantial investment to add a single open semantic layer of dbt code. According to dbt Labs CEO and founder Tristan Handy, “This will solve, once and for all, one of the biggest problems in our space: the ‘single-source-of-truth’ problem.”
At data.world, we’ve believed in the power of the semantic layer since day one – it’s why our data catalog was the first in the space to be built on a knowledge graph. Knowledge graphs are greece whatsapp number data modern data infrastructure that connect data and metadata to all users in an organization, providing a single semantically organized view of your data. They are unique in that business terminology is represented as concepts and relationships that are both understandable by people and machines in the exact same way.
data.world customer Luke Slotwinski, VP of Data and Analytics at Prologis, appeared on Catalog & Cocktails to discuss how his team leverages the semantic layer to answer the why behind data challenges.
What do all three trends have in common?
The tie that binds all three trends together is data mesh. (What, you thought you’d read an article on enterprise data management that doesn’t mention it?)
“Rather than checking every single air waybill for errors manually, the European Green Fast Lane program advises us which of them have errors, and marks the location of the errors,” said Spoor. When freight forwarders share shipment data and pre-identified errors earlier, cargo handlers can anticipate and prepare for the required checks.
Warehouses are now being modified to handle more trucks per hour, and according to the European Green Fast Lane website, “the amount of concrete information we now have is leading to real process change” and “facilitating conversations about how things are working in the real world, leading to clarity, cooperation, and understanding.”
Knowingly or not, many organizations are already practicing – or pursuing – data mesh principles. That’s because, despite its detractors, data mesh offers a new approach to enterprise data management that instills hope in data and analytics teams with the promise of brighter days ahead.
Want more mesh? Join us on April 7th for the data.world spring summit where data.world co-founder and CPO Jon Loyens, Monte Carlo co-founder and CEO Barr Moses, Gary George from Indeed, and a special guest from Snowflake will participate in a panel discussion on what comes next for data mesh.