ElegantJ BI

SEPTEMBER 6, 2019

SSDP (otherwise known as self-serve data preparation) is the logical evolution of business intelligence analytical tools. With self-serve tools, data discovery and analytics tools are accessible to team members and business users across the enterprise. What is SSDP?

Data Discovery 130

Data Discovery Business Intelligence Business Intelligence Government 130

ElegantJ BI

SEPTEMBER 6, 2019

SSDP (otherwise known as self-serve data preparation) is the logical evolution of business intelligence analytical tools. With self-serve tools, data discovery and analytics tools are accessible to team members and business users across the enterprise. What is SSDP?

Data Discovery 130

Data Discovery Business Intelligence Business Intelligence Government 130

ElegantJ BI

SEPTEMBER 6, 2019

SSDP (otherwise known as self-serve data preparation) is the logical evolution of business intelligence analytical tools. With self-serve tools, data discovery and analytics tools are accessible to team members and business users across the enterprise. Self-Serve Data Prep in Action. What is SSDP?

Data Discovery 130

Data Discovery Business Intelligence Business Intelligence Government 130

Astera

JUNE 14, 2024

Previously, data governance processes focused on rigid procedures and strict controls over data assets. Active data governance is essential to ensure quality and accessibility when managing large volumes of data. Here’s a breakdown of its key components: Data Quality: Ensuring that data is complete and reliable.

Data Governance 52

Data Governance Government Data Quality Monitoring 52

Astera

SEPTEMBER 4, 2024

Unified data governance Even with decentralized data ownership, the data mesh approach emphasizes the need for federated data governance , helping you implement shared standards, policies, and protocols across all your decentralized data domains.

Data Architecture 52

Data Architecture Data Governance Government Data Discovery 52

Astera

FEBRUARY 28, 2024

Data Governance and Documentation Establishing and enforcing rules, policies, and standards for your data warehouse is the backbone of effective data governance and documentation. Keeping track of metadata can help you understand the data, facilitate data integration , enable data lineage tracing, and enhance data quality.

Data Warehouse 52

Data Warehouse Cloud Computing Data Quality Monitoring 52

Astera

FEBRUARY 27, 2024

It also supports predictive and prescriptive analytics, forecasting future outcomes and recommending optimal actions based on data insights. Enhancing Data Quality A data warehouse ensures high data quality by employing techniques such as data cleansing, validation, integration, and standardization during the ETL process.

Data Warehouse 52

Data Warehouse Data Quality Cloud Computing Monitoring 52

Astera

FEBRUARY 27, 2024

It also supports predictive and prescriptive analytics, forecasting future outcomes and recommending optimal actions based on data insights. Enhancing Data Quality A data warehouse ensures high data quality by employing techniques such as data cleansing, validation, integration, and standardization during the ETL process.

Data Warehouse 52

Data Warehouse Data Quality Cloud Computing Monitoring 52

Data Pine

MAY 31, 2022

Since we live in a digital age, where data discovery and big data simply surpass the traditional storage and manual implementation and manipulation of business information, companies are searching for the best possible solution for handling data. It is evident that the cloud is expanding.

Cloud Computing 95

Cloud Computing Cybersecurity Business Intelligence Business Intelligence 95

ElegantJ BI

MARCH 19, 2021

When considering the advantages of data popularity and sharing, one must also consider that not all popular data will be high-quality data (and vice versa). So, there is definitely a need to provide both approaches in data analysis.

Data Quality 130

Data Quality Agile Business Intelligence Business Intelligence 130

ElegantJ BI

MARCH 19, 2021

When considering the advantages of data popularity and sharing, one must also consider that not all popular data will be high-quality data (and vice versa). So, there is definitely a need to provide both approaches in data analysis. Original Post : Data Agility and ‘Popularity’?

Data Quality 130

Data Quality Agile Business Intelligence Business Intelligence 130

Data Pine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 144

Business Intelligence Business Intelligence Artificial Intelligence Data Discovery 144

ElegantJ BI

MARCH 19, 2021

When considering the advantages of data popularity and sharing, one must also consider that not all popular data will be high-quality data (and vice versa). So, there is definitely a need to provide both approaches in data analysis. Original Post : Data Agility and ‘Popularity’?

Data Quality 130

Data Quality Agile Business Intelligence Business Intelligence 130

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

ElegantJ BI

DECEMBER 27, 2016

Understanding the social aspect of data analysis, and data popularity, can help IT staff and executives gain insight and offer the most beneficial support to the organization. Imagine an environment where users can access a business intelligence and analysis portal and see popular data to rank and share and comment.

Business Intelligence 130

Business Intelligence Business Intelligence Data Quality Data Discovery 130

ElegantJ BI

DECEMBER 27, 2016

Understanding the social aspect of data analysis, and data popularity, can help IT staff and executives gain insight and offer the most beneficial support to the organization. Imagine an environment where users can access a business intelligence and analysis portal and see popular data to rank and share and comment.

Business Intelligence 130

Business Intelligence Business Intelligence Data Quality Data Discovery 130

ElegantJ BI

DECEMBER 27, 2016

Understanding the social aspect of data analysis, and data popularity, can help IT staff and executives gain insight and offer the most beneficial support to the organization. Imagine an environment where users can access a business intelligence and analysis portal and see popular data to rank and share and comment.

Business Intelligence 130

Business Intelligence Business Intelligence Data Quality Data Discovery 130

ElegantJ BI

JANUARY 22, 2017

The advent of tools like self-serve data preparation , plug n’ play predictive analysis and smart data visualization provide support for business users to leverage sophisticated tools and algorithms in an easy-to-use environment and improve data agility and timeliness.

Business Intelligence 100

Business Intelligence Business Intelligence Data Quality Data Discovery 100

ElegantJ BI

JANUARY 22, 2017

The advent of tools like self-serve data preparation , plug n’ play predictive analysis and smart data visualization provide support for business users to leverage sophisticated tools and algorithms in an easy-to-use environment and improve data agility and timeliness.

Business Intelligence 100

Business Intelligence Business Intelligence Data Quality Data Discovery 100

ElegantJ BI

JANUARY 22, 2017

The advent of tools like self-serve data preparation , plug n’ play predictive analysis and smart data visualization provide support for business users to leverage sophisticated tools and algorithms in an easy-to-use environment and improve data agility and timeliness. About Kartik Patel.

Business Intelligence 100

Business Intelligence Business Intelligence Data Quality Data Discovery 100

Astera

JANUARY 12, 2024

It was developed by Dan Linstedt and has gained popularity as a method for building scalable, adaptable, and maintainable data warehouses. Self-Serve Data Infrastructure as a Platform: A shared data infrastructure empowers users to independently discover, access, and process data, reducing reliance on data engineering teams.

Data Architecture 52

Data Architecture Data Warehouse Data Quality Government 52

Insight Software

OCTOBER 11, 2023

A Quick Overview of Logi Symphony Download Now Here are the key gains your applications team receives with Logi Symphony: All Things Data Improve data quality and collaboration to enable consumers with the tools to readily understand their data. White Label embedded content to match the branding of your application.

Embedded Analytics 52

Embedded Analytics IBM Support IBM Maintenance User Experience 52

Insight Software

MARCH 14, 2024

Jet’s interface lets you handle data administration easily, without advanced coding skills. You don’t need technical skills to manage complex data workflows in the Fabric environment. 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