9 posts tagged with "Copilot"

We are excited to announce the official release of version 2.7 GitHub of the Metad Analytics Platform! This version introduces four exciting new features aimed at further enhancing user interactions and data analysis experiences. These new features include a brand-new Digital Expert role, a flexible and efficient Knowledge Base system, a powerful Toolsets, and support for Multi-Bots of Feishu in ChatBI. This article will detail how these new features can help you manage data more intelligently and improve work efficiency.

Lark Bot of ChatBI​

In a digital office environment, data-driven decision-making is becoming increasingly important. To help businesses utilize data more efficiently, the ChatBI bot provides an intelligent data querying and analysis solution on the Lark platform.
Through the ChatBI bot, Lark users can easily uncover the value of their data, enabling smarter decision-making and collaboration. Whether for personal use or team collaboration, ChatBI offers robust support for your data analysis needs.

For more details, see ChatBI Lark Bot.

ChatBI Updates​

• Improved the agent logic for creating calculation indicators, enhancing its intelligence level.
• Added a question recommendation feature to help users quickly start conversations.

Lark Single Sign-On​

• Supports identity authentication using Single Sign-On (SSO) on the Lark platform.
• Optimized the security and privacy protection mechanisms for SSO.

We are excited to announce that Metad Analytics Cloud version 2.5 is officially live! This latest version brings two major feature updates: ChatBI and Story Multi-Agent Command. These new features will further enhance the user experience, making data analysis more convenient, efficient, and intelligent.

In our previous two articles, we introduced:

• SAP S4HANA Embedded Real-Time Analytics - 1/4 Semantic Model Enhancing and creating calculated measures based on CDS Views in the S4 system.
• SAP S4HANA Embedded Real-Time Analytics - 2/4 Story Dashboard Creating story dashboards for real-time analysis of S4 system data based on CDS semantic models.

This article will explore how to manage indicators in the S4 system using CDS View and other models to build a comprehensive business indicator system.

Metad Analytics Cloud, leveraging semantic models, offers powerful indicator management functions, designed to help users efficiently manage and monitor indicators within data models. By establishing a unified semantic modeling of enterprise data based on a core OLAP engine, Metad Analytics Cloud eliminates the need for cumbersome data conversion and ETL. Through unified indicator definition functions, it enables unified management and certification of enterprise operational data. Ultimately, indicators are used for analyzing and evaluating company business and financial data through the indicator applications and story dashboards provided by Metad Analytics Cloud.

Key Performance Indicator System (Indicator System) is a set of indicators used to measure and monitor business performance. These indicators help enterprises evaluate the achievement of their strategic goals and the efficiency of their business processes, thereby guiding management decisions.

In the first article, we introduced how to enhance CDS models using the semantic modeling capabilities of Metad Analytics Cloud.. This article will explore how to use these enhanced CDS models to create visual story dashboards for better data analysis and business insights.

Story dashboards are a crucial component of Metad Analytics Cloud's embedded analytics. They help users gain a comprehensive understanding of their business from multiple dimensions and levels through rich visualization features and interactive operations. This article will introduce the key features of creating story dashboards, including calculation measures, filter bar widgets, input controls, key metrics widgets, chart widgets, and grid table widgets. Combining these features can create a story dashboard as shown below:

This series of SAP S/4HANA embedded analytics feature introductions will be explained in three parts:

1. Semantic Models: Explore how to enhance CDS models using Metad Analytics Cloud's semantic modeling capabilities, including defining calendar semantics and creating calculation measures using MDX statements, laying the foundation for subsequent story dashboards and KPI management.
2. Story Dashboard: Introduce how to create visual dashboards using enhanced CDS models to create rich and diverse stories and calculation measures.
3. KPI Management: Discuss how to define a series of KPIs for CDS models to form a systematic KPI management function, making it easy for enterprise managers to subscribe to and analyze KPIs and monitor business data in real time.

Embedded Analytics Introduction​

Metad Analytics Cloud is a powerful data analytics tool that can directly connect to the SAP S/4HANA system and utilize Transient Analytical Queries generated from CDS (Core Data Services) views for embedded real-time data analysis. By leveraging the in-memory computing capabilities of the SAP HANA database, it provides real-time data analysis and reporting functions, allowing users to gain insights directly during business operations.

This series introduces SAP S/4HANA embedded analytics functionality in three parts:

1. Semantic Model: Explore how to enhance CDS models using the semantic model functionality of Metad Analytics Cloud, including defining calendar semantics and creating calculated measures using MDX statements, laying the groundwork for subsequent stories and metrics management.
2. Story Dashboard: Learn how to create visual dashboards using enhanced CDS models to create a variety of stories and calculated measures.
3. Metrics Management: Discuss how to define a series of metrics based on CDS models to form a systematic metrics management function, allowing managers within the enterprise to subscribe to and analyze metrics and monitor business data in real time.

This article will introduce how to enhance CDS models using the semantic model functionality of Metad Analytics Cloud, including calendar semantic definitions, calculated measures, and more.

We are excited to announce that the latest version of the Metad Analysis Platform, version 2.4, is now available! As a cloud-based agile AI data analysis platform, it integrates multidimensional modeling, indicators management, and BI visualization to provide users with a one-stop data analysis solution. This update introduces the revolutionary "Copilot Multi-Agent Command" feature, further enhancing the platform's intelligence and user experience.

AI Digital Business Experts​

In Metad Analysis Platform 2.4, we have introduced a revolutionary feature—the AI Digital Business Experts Platform. This feature aims to digitize professional knowledge and experience from various industries, allowing users to quickly access and apply these valuable resources to build their own enterprise data analysis systems. Whether you are a startup or a large enterprise, this feature enables you to quickly build a data analysis framework tailored to your business needs, improving decision-making efficiency and accuracy.

The platform not only provides users with ready-to-use digital business expert resources but also supports industry experts in digitizing and sharing their expertise with users in need. This sharing and collaboration mechanism promotes the dissemination and application of knowledge, contributing to the progress of various industries.

You can browse more information about AI Digital Business Experts on the AI Digital Business Experts page.

Metad Analysis Cloud, an agile data analysis platform based on cloud computing, integrates multidimensional modeling, metric management, and BI presentation, aiming to provide users with an efficient and convenient data analysis experience. We are pleased to announce that the latest version 2.3 of Metad Analysis Cloud has been officially released. This update not only optimizes platform performance but also brings several exciting new features, particularly a comprehensive upgrade to Copilot Command and Roles.

Major Breakthroughs in Copilot​

Copilot Command is an innovative way for users to execute AI functions. By entering commands along with corresponding prompts, the Copilot Agent will invoke large language models (LLMs) and run relevant functions to complete various data analysis tasks. The new version 2.3 of Metad Analysis Cloud has made significant improvements in this feature:

In today's data-driven business environment, data analysis has become a key part of business decision-making. The Metad Analytics Cloud provides you with an intelligent data query experience, through its powerful AI copilot function based on ChatGPT, you can more efficiently query, optimize and interpret data. This article will introduce in detail how to turn on and configure the AI copilot in the Metad Analytics Cloud, and how to use the AI copilot in the Query Lab to improve the efficiency of data query.

Query Lab provides the function of flexibly operating the data source entity (physical table view or multi-dimensional data set) by using SQL query statement, which helps users in their daily data operation and maintenance work. The query lab is built in the semantic model workspace and operates and queries data through the data sources connected by the semantic model. If the user's data source is public network accessible, you can create a data source in the metad analytics cloud to connect query. If the user's data source is deployed in the private network, you can use the desktop agent to connect and query.

Next, this article will introduce how to turn on and configure AI copilot and use it to assist in querying the data sql in the laboratory, optimizing and explaining.