Astera

FEBRUARY 27, 2024

52% of IT experts consider faster analytics essential to data warehouse success. However, scaling your data warehouse and optimizing performance becomes more difficult as data volume grows. Leveraging data warehouse best practices can help you design, build, and manage data warehouses more effectively.

Data Warehouse 52

Data Warehouse Data Quality Cloud Computing Monitoring 52

Astera

SEPTEMBER 4, 2024

This feature automates communication and insight-sharing so your teams can use, interpret, and analyze other domain-specific data sets with minimal technical expertise. Automating tasks facilitates data integration activities, helping your organization manage high volumes of complex data from disparate sources.

Data Architecture 52

Data Architecture Data Governance Government Data Discovery 52

GAVS Technology

JUNE 2, 2021

Data fabric aims to simplify the management of enterprise data sources and the ability to extract insights from them. While focus on API management helps with data sharing, this functionality has to be enhanced further as data sharing also needs to take care of privacy and other data governance needs.

Healthcare 52

Healthcare Data Governance Government Data Mining 52

Astera

MARCH 20, 2024

The increasing digitization of business operations has led to the generation of massive amounts of data from various sources, such as customer interactions, transactions, social media, sensors, and more. This data, often referred to as big data, holds valuable insights that you can leverage to gain a competitive edge.

Data Management 52

Data Management Master Data Management Data Quality Data Warehouse 52

Astera

MAY 14, 2024

Metadata management is elemental in providing this context to data and is the cornerstone for effective data governance and intelligent data management, ensuring your data is reliable and authentic. Governance: Establishing metadata governance processes to ensure metadata integrity, security, and compliance.

Government 52

Government Data Governance Master Data Management Documentation 52

ElegantJ BI

NOVEMBER 16, 2016

If the value of the data, analysis and decision support is not persuasive, your business users will not adopt these business intelligence tools. By providing a full suite of features with sophisticated functionality, and a true self-serve environment, the organization can encourage and support data democratization.

Data Discovery 130

Data Discovery Business Intelligence Business Intelligence Data Visualization 130

ElegantJ BI

NOVEMBER 16, 2016

If the value of the data, analysis and decision support is not persuasive, your business users will not adopt these business intelligence tools. By providing a full suite of features with sophisticated functionality, and a true self-serve environment, the organization can encourage and support data democratization.

Data Discovery 130

Data Discovery Business Intelligence Business Intelligence Data Visualization 130

ElegantJ BI

NOVEMBER 16, 2016

If the value of the data, analysis and decision support is not persuasive, your business users will not adopt these business intelligence tools. Data Access. By providing a full suite of features with sophisticated functionality, and a true self-serve environment, the organization can encourage and support data democratization.

Data Discovery 130

Data Discovery Business Intelligence Business Intelligence Data Visualization 130

ElegantJ BI

DECEMBER 5, 2016

Factors like poor User Adoption, Data Access, Features and Benefits, Self-Serve Analytical Capability, Data Sharing and Reporting, Cost vs. Benefit, and Data Discovery issues must be considered in order to ensure the success of your self-serve business intelligence initiative.

Business Intelligence 130

Business Intelligence Business Intelligence Data Discovery Data Governance 130

ElegantJ BI

DECEMBER 5, 2016

Factors like poor User Adoption, Data Access, Features and Benefits, Self-Serve Analytical Capability, Data Sharing and Reporting, Cost vs. Benefit, and Data Discovery issues must be considered in order to ensure the success of your self-serve business intelligence initiative.

Business Intelligence 130

Business Intelligence Business Intelligence Data Discovery Data Governance 130

ElegantJ BI

DECEMBER 5, 2016

Factors like poor User Adoption, Data Access, Features and Benefits, Self-Serve Analytical Capability, Data Sharing and Reporting, Cost vs. Benefit, and Data Discovery issues must be considered in order to ensure the success of your self-serve business intelligence initiative. Data Source and Data Structural Review.

Business Intelligence 130

Business Intelligence Business Intelligence Data Discovery Data Governance 130

Astera

JANUARY 12, 2024

A solid data architecture is the key to successfully navigating this data surge, enabling effective data storage, management, and utilization. Enterprises should evaluate their requirements to select the right data warehouse framework and gain a competitive advantage.

Data Architecture 52

Data Architecture Data Warehouse Data Quality Government 52

Insight Software

OCTOBER 11, 2023

Maintaining robust data governance and security standards within the embedded analytics solution is vital, particularly in organizations with varying data governance policies across varied applications. Join disparate data sources to clean and apply structure to your data.

Embedded Analytics 52

Embedded Analytics IBM Support IBM Maintenance User Experience 52

Insight Software

OCTOBER 24, 2022

As cloud computing has advanced in popularity, data discovery applications have evolved rapidly to handle very large datasets, offering graphically rich displays such as heat maps, pie charts, and geographical maps alongside pivot tables for multi-dimensional analysis. Download Now. The Better Approach: Embedded Analytics.

IBM Support 52

IBM Support IBM Maintenance Embedded Analytics IBM Expert 52

Insight Software

MARCH 14, 2024

With Jet’s extensive capabilities for data validation, enrichment, and cleansing, it ensures that the data used for analysis is accurate and dependable. Data Discovery and Semantic Layer By facilitating effective data discovery and the development of a semantic layer, Jet gives Fabric users more control.

IBM Maintenance 52

IBM Maintenance IBM Support Data Discovery Supply Chain 52