Hello Sense and Respond, Bye Bye Command and Control

SHALINI RAGHAVAN | August 31, 2016 | 115 views

It was a few years ago, my colleague Matt Beck, General Manager for FICO’s marketing solution, and I were discussing the challenges of doing business in an omnichannel world. Our ability to interact with customers across multiple communication channels is just one factor contributing to the fact that there is now more data than ever before.

Spotlight

Front

Front is reinventing the inbox so people can accomplish more together. With new workflows, efficient collaboration, and all their communication channels in one place, more than 3,500 businesses rely on their Front inbox to be more productive as a team. Founded in 2013, Front has raised $79 million from Sequoia, DFJ, and others to unlock new ways to work that make people more efficient, fulfilled, and ultimately happier at work. Front is headquartered in San Francisco with an office in Paris. To learn more, visit frontapp.com.

OTHER ARTICLES
BIG DATA MANAGEMENT

Enhance Your Customer Experience with Data-Centric AI

Article | July 6, 2022

Data-centric AI is a unique approach to machine learning that depends on the data scientist to design the complete pipeline from data purification and intake through model training. There is no need for a detailed understanding of AI algorithms in this method; instead, it is all about the data. The principle behind data-centric AI is simple: rather than training an algorithm first and then cleaning up the dirty dataset, begin with clean data and train an algorithm on that dataset. Why Is It Necessary to Centralize Datasets? A consolidated data platform can be utilized to produce a single source of truth, therefore simplifying and assuring accuracy. When a team concentrates on continual improvement, wasted time and resources are reduced. You can improve optimization by centralizing data. This is due to the increased opportunity for your team to enhance procedures and make better judgments. The capacity to exploit a single platform that promotes constant improvement in processes, products and operationalization models is provided by centralizing data. Data-Centric AI for Personalized Customer Experience Data-centric AI connects your data and analytics. It's used to detect common habits and preferences, tailor marketing campaigns, provide better suggestions, and much more. Data-Centric AI is being used to evaluate various types of data in order to assist organizations in making quicker, more efficient choices. It can be used to analyze client behavior and trends across several channels in order to provide personalized experiences. It enables applications and websites to adjust the information that individuals view according to their preferences, as well as advertisers to target specific consumers with tailored offers. What Will the Future of Data-Centric AI Look Like? Data-centric AI strives to provide a systematic approach to a wide range of domains, including product design and user experience. Data-centric AI is a systematic technique and technology that enables engineers and other data scientists to employ machine learning models in their own data studies. Moreover, the goal of data-centric AI is to build best practices that make data analysis approaches less expensive and easier for businesses to implement effortlessly.

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BUSINESS STRATEGY

Why Adaptive AI Can Overtake Traditional AI

Article | July 22, 2022

With the ever-changing technology world, company demands and results are no longer the norm. Businesses in a variety of sectors are using artificial intelligence (AI) technologies to solve complicated business challenges, build intelligent and self-sustaining solutions, and, ultimately, remain competitive at all times. To that aim, ongoing attempts are being made to reinvent AI systems in order to do more with less. Adaptive AI is a significant step in that direction. It has the potential to outperform standard machine learning (ML) models in the near future because of its ability to enable organizations to get greater results while spending less time, effort, and resources. The capacity of adaptive AI to enable enterprises to achieve greater outcomes while investing less time, effort, and assets is why it can overtake traditional AI models. Why Adaptive AI Overtakes Traditional AI Robust, Efficient and Agile Robustness, efficiency, and agility are the three basic pillars of Adaptive AI. The ability to achieve great algorithmic accuracy is referred to as robustness. The capacity to achieve reduced resource utilization is referred to as efficiency (for example, computer, memory, and power). Agility manages the ability to change operational circumstances in response to changing demands. Together, these three Adaptive AI principles provide the groundwork for super-capable AI inference for edge devices. Data-Informed Predictions A single pipeline is used by the adaptive learning approach. With this method, you can use a continually advanced learning approach that maintains the framework up-to-date and encourages it to achieve high levels of performance. The Adaptive Learning method examines and learns new changes made to the information and produces values, as well as their associated attributes. Moreover, it benefits from events that can modify market behavior in real-time and, as a result, maintains its accuracy consistently. Adaptive AI recognizes information from the operational environment and uses it to produce data-informed predictions. Closing Lines Adaptive AI will be utilized to meet changing AI computing requirements. Operational effectiveness depends on algorithmic performance and available computer resources. Edge AI frameworks that can change their computing demands effectively reduce compute and memory requirements. Adaptive AI is robust in CSPs' dynamic software environments, where inputs and outputs alter with each framework revamp. It can assist with network operations, marketing, customer service, IoT, security, and customer experience.

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BIG DATA MANAGEMENT

Data-Centric Approach for AI Development

Article | July 15, 2022

As AI has grown in popularity over the past decade, practitioners have concentrated on gathering as much data as possible, classifying it, preparing it for usage, and then iterating on model architectures and hyper-parameters to attain our desired objectives. While dealing with all of this data has long been known as laborious and time-consuming, it has typically been seen as an upfront, one-time step we take before entering into the essential modeling phase of machine learning. Data quality concerns, label noise, model drift, and other biases are all addressed in the same way: by collecting and labeling more data, followed by additional model iterations. The foregoing technique has worked successfully for firms with unlimited resources or strategic challenges. It doesn't work well for machine learning's long-tail issues, particularly those with fewer users and little training data. The discovery that the prevailing method of deep learning doesn't "scale down" to industry challenges has given birth to a new "trend" in the area termed "Data-Centric AI." Implementing a Data-Centric Approach for AI Development Leverage MLOps Practices Data-centric AI prioritizes data over models. Model selection, hyper-parameter tuning, experiment tracking, deployment, and monitoring take time. Data-centric approaches emphasize automating and simplifying ML lifecycle operations. Standardizing and automating model-building requires MLOps. MLOps automates machine learning lifecycle management pipelines. An organizational structure improves communication and cooperation. Involve Domain Expertise Data-centric AI development requires domain-specific datasets. Data scientists can overlook intricacies in various sectors, business processes, or even the same domain. Domain experts can give ground truth for the AI use case and verify whether the dataset truly portrays the situation. Complete and Accurate Data Data gaps cause misleading results. It's crucial to have a training dataset that correctly depicts the underlying real-world phenomenon. Data augmentation or creating synthetic data might be helpful if gathering comprehensive and representative data is costly or challenging for your use case.

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BIG DATA MANAGEMENT

A Modern Application Must Have: Multi-cloud Database

Article | July 6, 2022

To function well, modern apps require enormous amounts of diverse data from sensors, processes, interactions, etc. However, these apps cannot understand the unstructured big data and extract commercial value for effective operations unless this data is maintained properly. In today's age of cloud computing, apps gather and analyze data from various sources, but the data isn't always kept in the same database or format. While increasing overall complexity, several formats make it more difficult for apps to retain and use various data. Multi-model databases, a cutting-edge management system, provide a sophisticated approach to handling varied and unstructured data. A multi-model database allows various data models to natively utilize a single, integrated backend, as opposed to combining different database models. Why Has Multi-Model Database Become a Necessity for Modern Applications? Modern applications can store diverse data in a single repository owing to the flexible approach to database management, which improves agility and reduces data redundancy. Improve Reliability Each database might be a single point of failure for a larger system or application. Multi-model databases reduce failure points, enhancing data dependability and recovery time. Such recovery minimizes expenses and maintains customer engagement and application experience. Simplify Data Management Fragmented database systems benefit contemporary applications but complicate development and operations. Multi-model databases provide a single backend that maintains data integrity and fault tolerance, eliminating the need for different database systems, software licenses, developers, and administrators. Improve Fault Tolerance Modern apps must be fault-tolerant and respond promptly to failures promptly. Multi-model databases enable this by integrating several systems into a single backend. The integration provides system-wide failure tolerance. Closing Lines As applications get more complicated, so do their database requirements. However, connecting many databases and preserving consistency between data gathered from various sources is a time-consuming and expensive undertaking. Fortunately, multi-model databases provide an excellent option for generating the data models you want on a single backend.

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Spotlight

Front

Front is reinventing the inbox so people can accomplish more together. With new workflows, efficient collaboration, and all their communication channels in one place, more than 3,500 businesses rely on their Front inbox to be more productive as a team. Founded in 2013, Front has raised $79 million from Sequoia, DFJ, and others to unlock new ways to work that make people more efficient, fulfilled, and ultimately happier at work. Front is headquartered in San Francisco with an office in Paris. To learn more, visit frontapp.com.

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BUSINESS INTELLIGENCE,BIG DATA MANAGEMENT,DATA ARCHITECTURE

Mode Analytics Recognized as a Leader in Snowflake’s Modern Marketing Data Stack Report

Mode Analytics | September 30, 2022

Mode Analytics today announced that it has been recognized as a Business Intelligence Leader in the inaugural Modern Marketing Data Stack Report: Your Technology Guide to Unifying, Analyzing, and Activating the Data that Powers Amazing Customer Experiences, executed and launched by Snowflake, the Data Cloud company. Snowflake’s data-backed report identifies the best of breed solutions used by Snowflake customers to show how marketers can leverage the Snowflake Data Cloud with accompanying partner solutions to best identify, serve, and convert valuable prospects into loyal customers. By analyzing usage patterns from a pool of nearly 6,000 customers, Snowflake identified six technology categories that organizations consider when building their marketing data stacks. These categories include: Analytics Integration & Modeling Identity & Enrichment Activation & Measurement Business Intelligence Data Science & Machine Learning Focusing on companies that are active members of the Snowflake Partner Network (or ones with a comparable agreement in place with Snowflake), as well as Snowflake Marketplace Providers, the report explores each of these categories that comprise the Modern Marketing Data Stack, highlighting technology partners and their solutions as “leaders” or “ones to watch” within each category. The report also details how current Snowflake customers leverage a number of these partner technologies to enable data-driven marketing strategies and informed business decisions. Snowflake’s report provides a concrete overview of the partner solution providers and data providers marketers choose to create their data stacks. “Marketing professionals continue to expand their investment in analytics to improve their organization’s digital marketing activities. “Mode has emerged as a leader in the Modern Marketing Data Stack, with joint customers leveraging their technology to interpret insights that lead to informed business decisions.” Denise Persson, Chief Marketing Officer at Snowflake Mode was identified in Snowflake’s report as a Leader in the Business Intelligence category for its particular success with Visual Explorer, Mode’s flexible visualization system that helps analysts explore data faster and provides easy-to-interpret insights to business stakeholders. Additionally, Mode and Snowflake have partnered in the past couple of years tocreate a modern data analytics stack, mobilizing the world’s data with the Snowflake Data Cloud to help joint customers quickly execute queries and perform analysis. “Mode combines the best elements of business analytics and data science into a single platform, unlocking new ways for marketers to accelerate data-driven outcomes,” said Gaurav Rewari, CEO, Mode Analytics. “Our partnership with Snowflake makes it possible for marketing and other departments across an organization to truly centralize and interact directly with their data. With Snowflake’s single, integrated data platform, built to fully leverage the speed and flexibility of the cloud, organizations can mobilize their data in near-real time.” About Mode Analytics Mode’s advanced analytics platform is designed by data experts for data experts. It allows data scientists and analysts to visualize, analyze, and share data using a powerful end-to-end workflow that covers everything from early data exploration stages to presentation-ready shareable products. Unlike traditional business intelligence tools that produce static dashboards and reports, Mode brings the best of BI and data science together in a single platform, empowering everyone at your organization to use data to make high quality, high velocity decisions. Mode also supports the analytics community with free learning resources such as SQL School, open source SQL queries, and free tools for anyone analyzing public data.

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BUSINESS INTELLIGENCE,BIG DATA MANAGEMENT

VAST Data and Dremio Break Down Data Silos And Accelerate Queries At Any Scale

VAST Data | September 30, 2022

VAST Data, the data platform company for the AI-powered world, today announced a strategic partnership with Dremio, the easy and open data lakehouse platform, to enable enterprises to get from data to insights faster with a hybrid, multi-cloud architecture for scalable analytics. Regardless of physical location – on-premises or in the public cloud – Dremio customers can now analyze their data anywhere by leveraging VAST’s massively parallel architecture for concurrent and near real-time data access at any scale. VAST and Dremio are at the forefront of a market shift away from siloed data warehouses and legacy data platforms such as Hadoop. As businesses struggle with the exponential growth of data volumes and data sources, they need a highly-scalable solution for storing that data, and providing broad and concurrent access for a wide range of technical and non-technical data consumers. Paired with Dremio, VAST's Universal Storage enables organizations to escape the restrictive, walled garden environment of Hadoop and the Hadoop File System. It provides customers with an open data lakehouse platform that powers the data management, data governance, and enterprise analytics capabilities typically found in a data warehouse, powered by an all-flash data store that is purpose-built to manage large volumes of structured, semi-structured, and unstructured data. In the spirit of public cloud object storage offerings like Amazon Simple Storage Service (S3), VAST unifies an organization’s data for analytics on a common, single-tiered and linearly scalable data platform - while also enabling customers to step into an all-flash S3 experience without the flash expense that’s common with conventional systems. Dremio provides an open data lakehouse platform that executes lightning-fast SQL queries using a common semantic layer across data sources, and a simple user interface. As a result, organizations can build capabilities that are superior to even public cloud offerings with cloud-native infrastructure that provide choice and flexibility on how and where data is managed. “Partnering with VAST ensures Dremio users are equipped with the lakehouse data capacity and scalable high performance necessary to run their business intelligence workloads and data analytics applications. “As data volumes continue to grow, VAST’s disaggregated architecture enables users to easily scale the performance and capacity that businesses demand, and that our open data lakehouse platform delivers.” Roger Frey, vice president of alliances at Dremio Faster time to data access Dremio’s open lakehouse platform enables organizations to query data directly in the data lake – and on S3 architecture – without having to copy or move data. By querying data in place, Dremio eliminates the need for complex and brittle ETL pipelines and data copies. Dremio reduces the time required to fulfill data access requests from weeks or months to just hours, and makes data teams more productive. Dremio also centralizes security and governance, and its no-copy architecture reduces network and storage costs. Dremio’s simplified data architecture complements VAST’s all-flash Universal Storage storage platform, which reduces latency and delivers a high-performance infrastructure for analytics at any scale. VAST’s breakthrough Universal Storage data platform reduces the amount of storage capacity necessary in cloud-native environments without compromising performance, optimizing spend and space. Together, Dremio and VAST accelerate access to data for analytics, and deliver insights to a wide range of data consumers. “We continue to see high market demand to underpin organizations’ modern data analytics infrastructure with VAST. Partnering with ecosystem leaders like Dremio drives a new approach to data analytics,” said Jeff Denworth, chief marketing officer and co-founder of VAST. “Partnering with Dremio ensures that our mutual customers have an optimized and simple out-of-the-box experience as they embrace a cloud-native architecture for their rapidly evolving data management needs.” About VAST Data VAST Data delivers the data platform at the heart of the AI-powered world, accelerating time-to-insight for workload-intensive applications. The performance, scalability, ease of use and cost efficiencies of VAST’s software helps enterprise organizations overcome the historic barriers to building all-flash data centers. Launched in 2019, VAST is the fastest-selling data infrastructure startup in history.

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BIG DATA MANAGEMENT,DATA VISUALIZATION

AtScale Announces Data Science and Enterprise AI Capabilities Within Semantic Layer Platform

AtScale | September 29, 2022

AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams, today announced at the Semantic Layer Summit an expanded set of product capabilities for organizations working to accelerate the deployment and adoption of enterprise artificial intelligence (AI). These new capabilities leverage AtScale’s unique position within the data stack with support for common cloud data warehouse and lakehouse platforms including Google BigQuery, Microsoft Azure Synapse, Amazon Redshift, Snowflake, and Databricks. Organizations across every industry are racing to realize the true potential of their data science and enterprise AI investments. IDC predicts spending on AI/ML solutions will grow 19.6% with over $500B spent in 2023. Despite this investment, Gartner reports that only 54% of AI models built will make it into production, with organizations struggling to generate business outcomes that justify investment to operationalize models. This disconnect creates an enormous opportunity for solutions that can simplify and accelerate the path to business impact for AI/ML initiatives. The AtScale Enterprise semantic layer platform now incorporates two new capabilities available to all customers leveraging AtScale AI-Link: Semantic Predictions - Predictions generated by deployed AI/ML models can be written back to cloud data platforms through AtScale. These model-generated predictive statistics inherit semantic model intelligence, including dimensional consistency and discoverability. Predictions are immediately available for exploration by business users using common BI tools (AtScale supports connectivity to Looker, PowerBI, Tableau, and Excel) and can be incorporated into augmented analytics resources for a wider range of business users. Semantic predictions accelerate the business outcomes of AI investments by making it easier and more timely to work with, share, and use AI-generated predictions. Managed Features - AtScale creates a hub of centrally governed metrics and dimensional hierarchies that can be used to create a set of managed features for AI/ML models. Managed features can be sourced from the existing library of models maintained by data stewards or by individual work groups. Furthermore, new features created by AutoML or AI platforms can also become managed features. AtScale managed features inherit semantic context, making them more discoverable and easier to work with, consistently, at any stage in ML model development. Managed features can now be served directly from AtScale, or through a feature store like FEAST, to train models in AutoML or other AI platforms. “Despite rising investments, greater adoption of AI/ML within the modern enterprise is still hindered by complexity. “The need for AI is huge, exploration is on the rise, but many businesses are still not able to use the predictive insights AI models can generate. Here at AtScale we can leverage our unique position in the data stack to streamline and simplify how the business can consume and use AI immediately, generating faster time to value from their enterprise AI investments.” Gaurav Rao, Executive Vice President and General Manager of AI/ML at AtScale About AtScale AtScale enables smarter decision-making by accelerating the flow of data-driven insights. The company’s semantic layer platform simplifies, accelerates, and extends business intelligence and data science capabilities for enterprise customers across all industries. With AtScale, customers are empowered to democratize data, implement self-service BI and build a more agile analytics infrastructure for better, more impactful decision making.

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BUSINESS INTELLIGENCE,BIG DATA MANAGEMENT,DATA ARCHITECTURE

Mode Analytics Recognized as a Leader in Snowflake’s Modern Marketing Data Stack Report

Mode Analytics | September 30, 2022

Mode Analytics today announced that it has been recognized as a Business Intelligence Leader in the inaugural Modern Marketing Data Stack Report: Your Technology Guide to Unifying, Analyzing, and Activating the Data that Powers Amazing Customer Experiences, executed and launched by Snowflake, the Data Cloud company. Snowflake’s data-backed report identifies the best of breed solutions used by Snowflake customers to show how marketers can leverage the Snowflake Data Cloud with accompanying partner solutions to best identify, serve, and convert valuable prospects into loyal customers. By analyzing usage patterns from a pool of nearly 6,000 customers, Snowflake identified six technology categories that organizations consider when building their marketing data stacks. These categories include: Analytics Integration & Modeling Identity & Enrichment Activation & Measurement Business Intelligence Data Science & Machine Learning Focusing on companies that are active members of the Snowflake Partner Network (or ones with a comparable agreement in place with Snowflake), as well as Snowflake Marketplace Providers, the report explores each of these categories that comprise the Modern Marketing Data Stack, highlighting technology partners and their solutions as “leaders” or “ones to watch” within each category. The report also details how current Snowflake customers leverage a number of these partner technologies to enable data-driven marketing strategies and informed business decisions. Snowflake’s report provides a concrete overview of the partner solution providers and data providers marketers choose to create their data stacks. “Marketing professionals continue to expand their investment in analytics to improve their organization’s digital marketing activities. “Mode has emerged as a leader in the Modern Marketing Data Stack, with joint customers leveraging their technology to interpret insights that lead to informed business decisions.” Denise Persson, Chief Marketing Officer at Snowflake Mode was identified in Snowflake’s report as a Leader in the Business Intelligence category for its particular success with Visual Explorer, Mode’s flexible visualization system that helps analysts explore data faster and provides easy-to-interpret insights to business stakeholders. Additionally, Mode and Snowflake have partnered in the past couple of years tocreate a modern data analytics stack, mobilizing the world’s data with the Snowflake Data Cloud to help joint customers quickly execute queries and perform analysis. “Mode combines the best elements of business analytics and data science into a single platform, unlocking new ways for marketers to accelerate data-driven outcomes,” said Gaurav Rewari, CEO, Mode Analytics. “Our partnership with Snowflake makes it possible for marketing and other departments across an organization to truly centralize and interact directly with their data. With Snowflake’s single, integrated data platform, built to fully leverage the speed and flexibility of the cloud, organizations can mobilize their data in near-real time.” About Mode Analytics Mode’s advanced analytics platform is designed by data experts for data experts. It allows data scientists and analysts to visualize, analyze, and share data using a powerful end-to-end workflow that covers everything from early data exploration stages to presentation-ready shareable products. Unlike traditional business intelligence tools that produce static dashboards and reports, Mode brings the best of BI and data science together in a single platform, empowering everyone at your organization to use data to make high quality, high velocity decisions. Mode also supports the analytics community with free learning resources such as SQL School, open source SQL queries, and free tools for anyone analyzing public data.

Read More

BUSINESS INTELLIGENCE,BIG DATA MANAGEMENT

VAST Data and Dremio Break Down Data Silos And Accelerate Queries At Any Scale

VAST Data | September 30, 2022

VAST Data, the data platform company for the AI-powered world, today announced a strategic partnership with Dremio, the easy and open data lakehouse platform, to enable enterprises to get from data to insights faster with a hybrid, multi-cloud architecture for scalable analytics. Regardless of physical location – on-premises or in the public cloud – Dremio customers can now analyze their data anywhere by leveraging VAST’s massively parallel architecture for concurrent and near real-time data access at any scale. VAST and Dremio are at the forefront of a market shift away from siloed data warehouses and legacy data platforms such as Hadoop. As businesses struggle with the exponential growth of data volumes and data sources, they need a highly-scalable solution for storing that data, and providing broad and concurrent access for a wide range of technical and non-technical data consumers. Paired with Dremio, VAST's Universal Storage enables organizations to escape the restrictive, walled garden environment of Hadoop and the Hadoop File System. It provides customers with an open data lakehouse platform that powers the data management, data governance, and enterprise analytics capabilities typically found in a data warehouse, powered by an all-flash data store that is purpose-built to manage large volumes of structured, semi-structured, and unstructured data. In the spirit of public cloud object storage offerings like Amazon Simple Storage Service (S3), VAST unifies an organization’s data for analytics on a common, single-tiered and linearly scalable data platform - while also enabling customers to step into an all-flash S3 experience without the flash expense that’s common with conventional systems. Dremio provides an open data lakehouse platform that executes lightning-fast SQL queries using a common semantic layer across data sources, and a simple user interface. As a result, organizations can build capabilities that are superior to even public cloud offerings with cloud-native infrastructure that provide choice and flexibility on how and where data is managed. “Partnering with VAST ensures Dremio users are equipped with the lakehouse data capacity and scalable high performance necessary to run their business intelligence workloads and data analytics applications. “As data volumes continue to grow, VAST’s disaggregated architecture enables users to easily scale the performance and capacity that businesses demand, and that our open data lakehouse platform delivers.” Roger Frey, vice president of alliances at Dremio Faster time to data access Dremio’s open lakehouse platform enables organizations to query data directly in the data lake – and on S3 architecture – without having to copy or move data. By querying data in place, Dremio eliminates the need for complex and brittle ETL pipelines and data copies. Dremio reduces the time required to fulfill data access requests from weeks or months to just hours, and makes data teams more productive. Dremio also centralizes security and governance, and its no-copy architecture reduces network and storage costs. Dremio’s simplified data architecture complements VAST’s all-flash Universal Storage storage platform, which reduces latency and delivers a high-performance infrastructure for analytics at any scale. VAST’s breakthrough Universal Storage data platform reduces the amount of storage capacity necessary in cloud-native environments without compromising performance, optimizing spend and space. Together, Dremio and VAST accelerate access to data for analytics, and deliver insights to a wide range of data consumers. “We continue to see high market demand to underpin organizations’ modern data analytics infrastructure with VAST. Partnering with ecosystem leaders like Dremio drives a new approach to data analytics,” said Jeff Denworth, chief marketing officer and co-founder of VAST. “Partnering with Dremio ensures that our mutual customers have an optimized and simple out-of-the-box experience as they embrace a cloud-native architecture for their rapidly evolving data management needs.” About VAST Data VAST Data delivers the data platform at the heart of the AI-powered world, accelerating time-to-insight for workload-intensive applications. The performance, scalability, ease of use and cost efficiencies of VAST’s software helps enterprise organizations overcome the historic barriers to building all-flash data centers. Launched in 2019, VAST is the fastest-selling data infrastructure startup in history.

Read More

BIG DATA MANAGEMENT,DATA VISUALIZATION

AtScale Announces Data Science and Enterprise AI Capabilities Within Semantic Layer Platform

AtScale | September 29, 2022

AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams, today announced at the Semantic Layer Summit an expanded set of product capabilities for organizations working to accelerate the deployment and adoption of enterprise artificial intelligence (AI). These new capabilities leverage AtScale’s unique position within the data stack with support for common cloud data warehouse and lakehouse platforms including Google BigQuery, Microsoft Azure Synapse, Amazon Redshift, Snowflake, and Databricks. Organizations across every industry are racing to realize the true potential of their data science and enterprise AI investments. IDC predicts spending on AI/ML solutions will grow 19.6% with over $500B spent in 2023. Despite this investment, Gartner reports that only 54% of AI models built will make it into production, with organizations struggling to generate business outcomes that justify investment to operationalize models. This disconnect creates an enormous opportunity for solutions that can simplify and accelerate the path to business impact for AI/ML initiatives. The AtScale Enterprise semantic layer platform now incorporates two new capabilities available to all customers leveraging AtScale AI-Link: Semantic Predictions - Predictions generated by deployed AI/ML models can be written back to cloud data platforms through AtScale. These model-generated predictive statistics inherit semantic model intelligence, including dimensional consistency and discoverability. Predictions are immediately available for exploration by business users using common BI tools (AtScale supports connectivity to Looker, PowerBI, Tableau, and Excel) and can be incorporated into augmented analytics resources for a wider range of business users. Semantic predictions accelerate the business outcomes of AI investments by making it easier and more timely to work with, share, and use AI-generated predictions. Managed Features - AtScale creates a hub of centrally governed metrics and dimensional hierarchies that can be used to create a set of managed features for AI/ML models. Managed features can be sourced from the existing library of models maintained by data stewards or by individual work groups. Furthermore, new features created by AutoML or AI platforms can also become managed features. AtScale managed features inherit semantic context, making them more discoverable and easier to work with, consistently, at any stage in ML model development. Managed features can now be served directly from AtScale, or through a feature store like FEAST, to train models in AutoML or other AI platforms. “Despite rising investments, greater adoption of AI/ML within the modern enterprise is still hindered by complexity. “The need for AI is huge, exploration is on the rise, but many businesses are still not able to use the predictive insights AI models can generate. Here at AtScale we can leverage our unique position in the data stack to streamline and simplify how the business can consume and use AI immediately, generating faster time to value from their enterprise AI investments.” Gaurav Rao, Executive Vice President and General Manager of AI/ML at AtScale About AtScale AtScale enables smarter decision-making by accelerating the flow of data-driven insights. The company’s semantic layer platform simplifies, accelerates, and extends business intelligence and data science capabilities for enterprise customers across all industries. With AtScale, customers are empowered to democratize data, implement self-service BI and build a more agile analytics infrastructure for better, more impactful decision making.

Read More

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