An important step in the data maturity of an organization is moving beyond simple historical analysis to generating accurate predictions about the future. In the past, business analysts focused on historical analysis while data science teams attempted to surface interesting insights about the future. Today, with the advent of the semantic layer, these two siloed worlds are coming together. Enterprises that merge these two disciplines can deliver augmented analytics, helping everyone in the organization better understand the past and predict the future.
Copyright: venturebeat.com – “How a semantic layer bridges BI and AI”
Types of analytics
Organizations leverage analytics to help them understand and improve their business operations and customer satisfaction. Before we go further, let’s define the four flavors of analysis we typically see in an organization, each with increasing levels of sophistication.
As illustrated by the table above, business users typically focus on historical analysis while data scientists are working to predict the future. It’s obvious that business users make better decisions if they can anticipate the future. It’s also obvious that data scientists build better models if they can compare their predictions to what actually happened. In other words, historical analysis and predictive analysis are relevant to both teams, but rarely do the two meet.
What is a semantic layer?
A semantic layer is a business representation of data that makes it easier for end users to access data using common, business-friendly terms. A semantic layer maps complex data relationships into easy-to-understand business terms to deliver a unified, consolidated view of data across the organization. A semantic layer provides the following benefits:
Usability
One of the biggest complaints from the business is that it takes way too long for IT to build or deliver reports for them. Users want to control their destiny, and subject-matter experts (not IT) are best suited to applying data to improve the business. A well-designed semantic layer hides the complexity of data’s physical form and location while translating data into understandable business constructs. A semantic layer frees business users and data scientists from the dependency on IT and data experts by making data easy to use.[…]
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Read more: www.venturebeat.com
An important step in the data maturity of an organization is moving beyond simple historical analysis to generating accurate predictions about the future. In the past, business analysts focused on historical analysis while data science teams attempted to surface interesting insights about the future. Today, with the advent of the semantic layer, these two siloed worlds are coming together. Enterprises that merge these two disciplines can deliver augmented analytics, helping everyone in the organization better understand the past and predict the future.
Copyright: venturebeat.com – “How a semantic layer bridges BI and AI”
Types of analytics
Organizations leverage analytics to help them understand and improve their business operations and customer satisfaction. Before we go further, let’s define the four flavors of analysis we typically see in an organization, each with increasing levels of sophistication.
As illustrated by the table above, business users typically focus on historical analysis while data scientists are working to predict the future. It’s obvious that business users make better decisions if they can anticipate the future. It’s also obvious that data scientists build better models if they can compare their predictions to what actually happened. In other words, historical analysis and predictive analysis are relevant to both teams, but rarely do the two meet.
What is a semantic layer?
A semantic layer is a business representation of data that makes it easier for end users to access data using common, business-friendly terms. A semantic layer maps complex data relationships into easy-to-understand business terms to deliver a unified, consolidated view of data across the organization. A semantic layer provides the following benefits:
Usability
One of the biggest complaints from the business is that it takes way too long for IT to build or deliver reports for them. Users want to control their destiny, and subject-matter experts (not IT) are best suited to applying data to improve the business. A well-designed semantic layer hides the complexity of data’s physical form and location while translating data into understandable business constructs. A semantic layer frees business users and data scientists from the dependency on IT and data experts by making data easy to use.[…]
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Read more: www.venturebeat.com
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