ElegantJ BI

SEPTEMBER 7, 2016

Empower business users by letting them add new data, change data operations, change summary operations, change visualization and layout and even design dashboards, reports and cross tabs without programming skills. Myth #2 – True Self-Serve BI Tools Will Compromise Data Governance.

Business Intelligence 100

Business Intelligence Business Intelligence Data Discovery Predictive Analytics 100

Astera

APRIL 24, 2024

What is a Data Governance Framework? A data governance framework is a structured way of managing and controlling the use of data in an organization. It helps establish policies, assign roles and responsibilities, and maintain data quality and security in compliance with relevant regulatory standards.

Data Governance 52

Data Governance Government Data Architecture Data Quality 52

Astera

MARCH 28, 2024

So, organizations create a data governance strategy for managing their data, and an important part of this strategy is building a data catalog. They enable organizations to efficiently manage data by facilitating discovery, lineage tracking, and governance enforcement.

Data Governance 52

Data Governance Government Data Discovery Data Quality 52

Astera

MAY 8, 2024

A business glossary is critical in ensuring data integrity by clearly defining data collection, storage, and analysis terms. When everyone adheres to standardized terminology, it minimizes data interpretation and usage discrepancies. They also monitor resource allocation and ensure that risks are managed effectively.

Data Governance 52

Data Governance Government Healthcare Data Quality 52

Astera

JULY 12, 2024

This catalog serves as a comprehensive inventory, documenting the metadata, location, accessibility, and usage guidelines of data resources. The primary purpose of a resource catalog is to facilitate efficient data discovery, governance , and utilization.

Data Governance 52

Data Governance Government Documentation Data Quality 52

Astera

FEBRUARY 28, 2024

Given the generally complex nature of the data warehouse architecture, there are certain data warehouse best practices that focus on performance optimization, data governance and security, scalability and future-proofing, and continuous monitoring and improvement.

Data Warehouse 52

Data Warehouse Cloud Computing Data Quality Monitoring 52

Astera

MARCH 20, 2024

For example, with a data warehouse and solid foundation for business intelligence (BI) and analytics , you can respond quickly to changing market conditions, emerging trends, and evolving customer preferences. Data breaches and regulatory compliance are also growing concerns. Data Quality Management Not all data is created equal.

Data Management 52

Data Management Master Data Management Data Warehouse Data Quality 52

Data Pine

DECEMBER 11, 2019

This means that your business’s data is available and secure regardless of a data breach or system failure. Improved data governance: Vertical SaaS is positioned to address data governance procedures via the inclusion of industry-specific compliance capabilities, which has the additional benefit of providing increased transparency.

Artificial Intelligence 125

Artificial Intelligence Innovation Digital Business Intelligence 125

Astera

FEBRUARY 27, 2024

Best Practices for Data Warehouses Adopting best practices tailored to optimize performance, fortify security, establish robust governance, ensure scalability, and maintain vigilant monitoring is crucial to extract the maximum benefits from your data warehouses.

Data Warehouse 52

Data Warehouse Data Quality Cloud Computing Monitoring 52

Astera

FEBRUARY 27, 2024

Best Practices for Data Warehouses Adopting best practices tailored to optimize performance, fortify security, establish robust governance, ensure scalability, and maintain vigilant monitoring is crucial to extract the maximum benefits from your data warehouses.

Data Warehouse 52

Data Warehouse Data Quality Cloud Computing Monitoring 52

Astera

MAY 14, 2024

Let’s look at some of the metadata types below: Operational metadata: details how and when data occurs and transforms. This metadata type helps to manage, monitor, and optimize system architecture performance. Examples include time stamps, execution logs, data lineage, and dependency mapping. Image by Astera.

Government 52

Government Data Governance Master Data Management Documentation 52

Astera

OCTOBER 30, 2024

This is because the integration of AI transforms the static repository into a dynamic, self-improving system that not only stores metadata but also enhances data context and accessibility to drive smarter decision-making across the organization. And when everyone has easy access to data, they can collaborate and meet demands more effectively.

Data Discovery 52

Data Discovery Supply Chain Data Quality Government 52

Astera

JUNE 14, 2024

Automated data governance is a relatively new concept that is fundamentally altering data governance practices. Traditionally, organizations have relied on manual processes to ensure effective data governance. This approach has given governance a reputation as a restrictive discipline.

Data Governance 40

Data Governance Government Data Quality Data Discovery 40

ElegantJ BI

NOVEMBER 16, 2016

Features and Benefits When your enterprise considers self-serve BI tools, it must look carefully at the features and benefits of these tools and compare them to the requirements and needs of the IT organization (for data governance and data watermarking, as appropriate), and for users at all levels including executives, analysts and business users.

Data Discovery 130

Data Discovery Business Intelligence Business Intelligence Data Visualization 130

ElegantJ BI

NOVEMBER 16, 2016

Features and Benefits When your enterprise considers self-serve BI tools, it must look carefully at the features and benefits of these tools and compare them to the requirements and needs of the IT organization (for data governance and data watermarking, as appropriate), and for users at all levels including executives, analysts and business users.

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

NOVEMBER 16, 2016

When your enterprise considers self-serve BI tools, it must look carefully at the features and benefits of these tools and compare them to the requirements and needs of the IT organization (for data governance and data watermarking, as appropriate), and for users at all levels including executives, analysts and business users.

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