AtScale AI-Link Connects Business Intelligence and Enterprise AI with Semantic Layer to Scale Augmented Analytics and Data Science
AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams, today announced the availability of AtScale AI-LinkTM. AI-Link provides a python interface to AtScale, rich with business context and metrics, to connect data science and augmented analytics programs with enterprise business intelligence (BI). The AtScale semantic layer delivers the governance, consistency and compliance needed to scale enterprise BI and artificial intelligence (AI) while accelerating live connections to public and private cloud data.
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“AtScale has been a core part of the analytics stack at Wells Fargo globally for several years, providing us unparalleled flexibility, performance and consistency across data platforms”
AtScale’s semantic layer insulates data consumers from the complexity of raw data, with business-oriented data models connected to live cloud data platforms including Snowflake, AWS, Microsoft Azure, Google Cloud and Databricks. Hundreds of forward-thinking data teams use AtScale to let BI teams consume live cloud data with the tools of their choice, including Tableau, Power BI and Excel. With AI-Link, data scientists can use Python to access the same governed source of enterprise metrics.
Exposing the AtScale semantic layer to data scientists with AI-Link delivers mission critical benefits, including:
- Reducing the complexity, time, and expense associated with data wrangling and preparation for machine learning (ML) models.
- Ensure data science teams are tapping into qualitative and quantitative values defined and vetted by the business.
- Simplifying the process of integrating new data sources, including those from 3rd party data providers such as Amazon’s Data Exchange or Snowflake’s Data Marketplace.
- Accelerate feature engineering by giving data science teams access to governed business metrics and first class categorical and hierarchical constructs.
- Promoting model reusability.
- Enabling the automated writeback of data science model predictions and features to the semantic layer, for consumption by BI users and other data scientists.
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“Giving line-of-business users and executives the ability to access, analyze and act on machine learning predictions and augmented analytics is the real value of enterprise AI,” said Christopher Lynch, Executive Chairman and CEO of AtScale. “We’re seeing more and more organizations embrace the convergence of artificial intelligence and business intelligence as a fundamental component of their digital transformation.”
According to Gartner, “Augmented capabilities are bridging the gap between existing analytics and business intelligence (ABI) platforms for analysts and business users, and data science and machine learning (DSML) tools for citizen and expert data scientists.” AtScale AI-Link lets data scientists publish model results within a semantic layer, creating a single source for consuming both actual and modeled data. This structure supports organizations in scaling data science and augmented analytics without constraining the agility of data scientists or business analysts. Model results can reach a wider audience faster, supporting organizational decision making and accelerating time to insight.
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