Astera

APRIL 4, 2024

Data governance’s primary purpose is to ensure organizational data assets’ quality, integrity, security, and effective use. The key objectives of Data Governance include: Enhancing Clear Ownership: Assigning roles to ensure accountability and effective management of data assets.

Data Governance 52

Data Governance Government E-Commerce Data Quality 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

Astera

APRIL 28, 2023

For example, GE Healthcare leverage AI-powered data cleansing tools to improve the quality of data in its electronic medical records, reducing the risk of errors in patient diagnosis and treatment. Continuous Data Quality Monitoring According to Gartner , poor data quality cost enterprises an average of $15 million per year.

E-Commerce 40

E-Commerce Supply Chain Artificial Intelligence Data Warehouse 40

Astera

MARCH 28, 2024

The former offers a comprehensive view of an organization’s data assets. It facilitates data discovery and exploration by enabling users to easily search and explore available data assets. This functionality includes data definitions, schema details, data lineage, and usage statistics.

Data Governance 52

Data Governance Government Data Discovery Data Quality 52

Astera

MARCH 20, 2024

Enterprise data management (EDM) is a holistic approach to inventorying, handling, and governing your organization’s data across its entire lifecycle to drive decision-making and achieve business goals. It provides a strategic framework to manage enterprise data with the highest standards of data quality , security, and accessibility.

Data Management 52

Data Management Master Data Management Government Data Warehouse 52

Astera

APRIL 24, 2024

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

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

Tableau

AUGUST 21, 2023

Catalog Enhanced data trust, visibility, and discoverability Tableau Catalog automatically catalogs all your data assets and sources into one central list and provides metadata in context for fast data discovery. It also lets users view and monitor the details of their flows.

Visualization 97

Visualization Monitoring Data Quality Data Discovery 97

Astera

MAY 8, 2024

Improved Data Quality and Governance: Access to high-quality data is crucial for making informed business decisions. A business glossary is critical in ensuring data integrity by clearly defining data collection, storage, and analysis terms.

Data Governance 52

Data Governance Government Healthcare Data Quality 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

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. Governance/Control.

Cloud Computing 95

Cloud Computing Cybersecurity Business Intelligence Business Intelligence 95

Astera

MAY 30, 2024

Variety : Data comes in all formats – from structured, numeric data in traditional databases to emails, unstructured text documents, videos, audio, financial transactions, and stock ticker data. Veracity: The uncertainty and reliability of data. Veracity addresses the trustworthiness and integrity of the data.

Data Analytics 52

Data Analytics Data Analytics Data Mining Supply Chain 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

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

Astera

JUNE 14, 2024

Instead of relying solely on manual efforts, automated data governance uses reproducible processes to maintain data quality, enrich data assets, and simplify workflows. This approach streamlines data management, maintains data integrity, and ensures consistent data quality and context over time.

Data Governance 40

Data Governance Government Data Quality Data Discovery 40

Data Pine

DECEMBER 4, 2019

This data analytics buzzword is somehow a déjà-vu. Augmented analytics was indeed previously referred to as “Smart Data Discovery”. It is the combination of several data processes that, instead of just giving back data, but provides a valuable, strategy-changing recommendation. Augmented Analytics. Graph Analytics.

Business Intelligence 84

Business Intelligence Business Intelligence IBM Support IBM Maintenance 84