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

AUGUST 15, 2024

It’s also more contextual than general data orchestration since it’s tied to the operational logic at the core of a specific pipeline. Since data pipeline orchestration executes an interconnected chain of events in a specific sequence, it caters to the unique data requirements a pipeline is designed to fulfill.

Data Quality 52

Data Quality Monitoring Artificial Intelligence Government 52

Astera

JANUARY 2, 2024

Securing Data: Protecting data from unauthorized access or loss is a critical aspect of data management which involves implementing security measures such as encryption, access controls, and regular audits. Organizations must also establish policies and procedures to ensure data quality and compliance.

Data Management 52

Data Management Data Warehouse Data Governance Data Quality 52

Astera

JULY 18, 2023

Data wrangling tools are powerful solutions designed to simplify and automate the process of data preparation. They enable data professionals to clean, transform, and organize raw data efficiently, saving countless hours of manual work while ensuring data quality and consistency.

Data Quality 52

Data Quality Visualization Data Visualization Documentation 52

Astera

APRIL 29, 2024

How are the data quality issues identified and resolved within the strategy? Why is a Data Governance Strategy Needed? IDC predicts that by 2025, the worldwide volume of data is expected to expand by 163 zettabytes, covering information across physical systems, devices, and clouds.

Data Governance 52

Data Governance Government IBM Maintenance Data Quality 52

Astera

MAY 6, 2024

To assist users in navigating this choice, the following guide outlines the essential considerations for choosing a data mining tool that aligns with their specific needs: 1. Documentation and Training : Adequate learning materials and troubleshooting guides are essential for mastering the tool and resolving potential issues.

Data Mining 52

Data Mining Supply Chain Data Quality Visualization 52

Astera

MARCH 11, 2024

Transformation Capabilities: Some tools offer powerful transformation capabilities, including visual data mapping and transformation logic, which can be more intuitive than coding SQL transformations manually. Transform and shape your data according to your business needs using pre-built transformations and functions without writing any code.

Data Warehouse 52

Data Warehouse Documentation Monitoring Data Quality 52

Astera

SEPTEMBER 4, 2024

This approach involves delivering accessible, discoverable, high-quality data products to internal and external users. By taking on the role of data product owners, domain-specific teams apply product thinking to create reliable, well-documented, easy-to-use data products. That’s where Astera comes in.

Data Architecture 52

Data Architecture Data Governance Government Data Discovery 52

Astera

JULY 3, 2024

Scalability considerations are essential to accommodate growing data volumes and changing business needs. Data Modeling Data modeling is a technique for creating detailed representations of an organization’s data requirements and relationships.

Data Architecture 40

Data Architecture Data Warehouse Data Governance Government 40

Astera

SEPTEMBER 20, 2023

Data Movement Data movement from source to destination, with minimal transformation. Data movement involves data transformation, cleansing, formatting, and standardization. Data Quality Consideration Emphasis is on data availability rather than extensive data quality checks.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data E-Commerce 52

Astera

SEPTEMBER 20, 2023

Data Movement Data movement from source to destination, with minimal transformation. Data movement involves data transformation, cleansing, formatting, and standardization. Data Quality Consideration Emphasis is on data availability rather than extensive data quality checks.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data E-Commerce 52

Astera

APRIL 28, 2023

The sheer volume of data makes extracting insights and identifying trends difficult, resulting in missed opportunities and lost revenue. Additionally, traditional data management systems are not equipped to handle the complexity of modern data sources, such as social media, mobile devices, and digitized documents.

Data Management 93

Data Management Artificial Intelligence Predictive Analytics Data Quality 93

Astera

MAY 26, 2023

On top of that, each invoice document—with its own distinct layout—carried long lists of goods being ordered for broad categories of products. The retailer had a ten-person team responsible for extracting information, such as order numbers, vendor information, dates, shipping details etc., and entering it into the system manually.

Retail 40

Retail Supply Chain Finance Data Quality 40

Sisense

FEBRUARY 10, 2021

This presented the first challenge for our product team in building Cascade Insight: What is the data that is most important to capture? However, defining the data requirements was important for understanding what data you need to measure to provide analytical insights.

Skillset 52

Skillset Data Requirement Data Quality Documentation 52

Cprime

MAY 4, 2023

Big Data Integration Moving and managing the massive volume, variety, and velocity of big data requires advanced tools and techniques. Your big data integration system needs intelligent big data pipelines that can automatically move, consolidate, and transform big data from multiple data sources while maintaining lineage.

Data Warehouse 52

Data Warehouse Big Data Finance Data Quality 52

Astera

OCTOBER 27, 2023

Practical Tips To Tackle Data Quality During Cloud Migration The cloud offers a host of benefits that on-prem systems don’t. Here are some tips to ensure data quality when taking your data warehouse to the cloud. The added layer of governance enhances the overall data quality management efforts of an organization.

Data Warehouse 52

Data Warehouse Data Quality Big Data Predictive Analytics 52

The BAWorld

NOVEMBER 22, 2021

Completeness is a data quality dimension and measures the existence of required data attributes in the source in data analytics terms, checks that the data includes what is expected and nothing is missing. Consistency is a data quality dimension and tells us how reliable the data is in data analytics terms.

Data Analytics 98

Data Analytics Data Analytics Data Warehouse Data Modelling 98

Astera

APRIL 24, 2024

Enterprise-Grade Integration Engine : Offers comprehensive tools for integrating diverse data sources and native connectors for easy mapping. Interactive, Automated Data Preparation : Ensures data quality using data health monitors, interactive grids, and robust quality checks.

IBM Support 52

IBM Support IBM cost IBM Maintenance Visualization 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