Astera

MARCH 19, 2024

It involves developing and enforcing policies, procedures, and standards to ensure data is consistently available, accurate, secure, and compliant throughout its lifecycle. At its core, data governance aims to answer questions such as: Who owns the data? What data is being collected and stored?

Data Governance 52

Data Governance Government Data Quality Monitoring 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

JANUARY 23, 2024

Businesses need scalable, agile, and accurate data to derive business intelligence (BI) and make informed decisions. Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively. The combination of data vault and information marts solves this problem.

Agile 52

Agile Data Warehouse Data Quality Government 52

Astera

OCTOBER 19, 2023

Easy-to-Use, Code-Free Environment By eliminating the need for writing complex code, data preparation tools reduce the risk of errors. These tools allow users to manipulate and transform data without the potential pitfalls of manual coding. Adaptability is another important requirement.

IBM Support 40

IBM Support Data Quality IBM Maintenance Real-time Data 40

Astera

MAY 6, 2024

Best For: Businesses that require a wide range of data mining algorithms and techniques and are working directly with data inside Oracle databases. Sisense Sisense is a data analytics platform emphasizing flexibility in handling diverse data architectures. Data quality is a priority for Astera.

Data Mining 52

Data Mining Supply Chain Data Quality Visualization 52

Astera

MAY 31, 2024

Here are the critical components of data science: Data Collection : Accumulating data from diverse sources like databases, APIs , and web scraping. Data Cleaning and Preprocessing : Ensuring data quality by managing missing values, eliminating duplicates, normalizing data, and preparing it for analysis.

Data Analytics 52

Data Analytics Data Analytics Data Warehouse Healthcare 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