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.
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.
“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.” [1] 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.
AtScale AI-Link is immediately available as an add-on to AtScale’s core semantic layer offering.
Supporting Quotes
“AtScale’s virtual semantic layer unifies multiple data sources to take out the patchwork that plagues the early stages of AI development and enables us to shift from experimental to production grade AI quickly,” said Jay Schuren, SVP of Customer Success and Enablement at DataRobot. “Our collaboration allows a wider audience of users to be successful and leverage their data in a governed and trustworthy way.”
“When it comes to creating value from data, accuracy, consistency and speed are some of the most critical factors,” said Jennifer Wheeler, Director, Analytics at Cardinal Health. “Data Science initiatives require metric consistency across all data consumers – from analysts to executives – and are why a semantic layer is critical to the success of enterprise analytics adoption.”
“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,” said Basil Darwish, Managing Director, Fintech and Enterprise Technology, Wells Fargo. “Extending our use cases into augmented analytics and data science provides a comprehensive analytics strategy to unify our data community.”
“As the leader in AI conferences, ODSC attracts Data Scientists and Analysts from many of the largest enterprises,” said Sheamus McGovern, Founder ODSC. “One of the common concerns is the lack of seamless integration between descriptive and prescriptive analytic workflows; the market is starved for transparency and sharing of analysis between these two camps to accelerate decisions and actions.”
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