Astera

APRIL 29, 2024

An effective data governance strategy is crucial to manage and oversee data effectively, especially as data becomes more critical and technologies evolve. However, creating a solid strategy requires careful planning and execution, involving several key steps and responsibilities.

Data Governance 52

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Dataversity

JULY 17, 2023

For data-driven organizations, this leads to successful marketing, improved operational efficiency, and easier management of compliance issues. However, unlocking the full potential of high-quality data requires effective Data Management practices.

Data Requirement 130

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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. Data breaches and regulatory compliance are also growing concerns.

Data Management 52

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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

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Astera

AUGUST 12, 2024

Financial data integration faces many challenges that hinder its effectiveness and efficiency in detecting and preventing fraud. Challenges of Financial Data Integration Data Quality and Availability Data quality and availability are crucial for financial data integration project, especially detecting fraud.

IBM Support 52

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Astera

FEBRUARY 9, 2024

Financial data integration faces many challenges that hinder its effectiveness and efficiency in detecting and preventing fraud. Challenges of Financial Data Integration Data Quality and Availability Data quality and availability are crucial for any data integration project, especially for fraud detection.

IBM Support 52

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The Data Administration Newsletter

OCTOBER 5, 2022

Governance for Acquired Data / Selecting Sources Our next column in the series explores challenges with governing acquired data, and then we’ll introduce a framework for managing acquired data— the data acquisition lifecycle.

Government 52

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Tableau

APRIL 6, 2022

An example would be reducing patient no-shows by predicting, based on data, how to improve the chances that the patient makes their appointment. SDOH data is an absolute necessity for the effective analysis of potential health inequities and associated mitigation strategies. CW : Retrospective data analysis isn't sufficient.

Healthcare 98

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Tableau

APRIL 6, 2022

An example would be reducing patient no-shows by predicting, based on data, how to improve the chances that the patient makes their appointment. SDOH data is an absolute necessity for the effective analysis of potential health inequities and associated mitigation strategies. CW : Retrospective data analysis isn't sufficient.

Healthcare 98

Healthcare Supply Chain Real-time Data Government 98

Astera

OCTOBER 12, 2023

million to the US government when their lack of proper screening mechanisms led to 500 PayPal transactions worth $44,000, violating sanctions against Iran, Cuba, and Sudan. The platform also allows you to implement rigorous data validation checks and customize rules based on your specific requirements.

Data Quality 52

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Astera

SEPTEMBER 4, 2024

This feature automates communication and insight-sharing so your teams can use, interpret, and analyze other domain-specific data sets with minimal technical expertise. Shared data governance is crucial to ensuring data quality, security, and compliance without compromising on the flexibility afforded to your teams by the data mesh approach.

Data Architecture 52

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Astera

JANUARY 23, 2024

Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively. Traditional data warehouses with predefined data models and schemas are rigid, making it difficult to adapt to evolving data requirements.

Agile 52

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Netmind

MARCH 9, 2021

Data Management. A good data management strategy includes defining the processes for data definition, collection, analysis, and usage, including data quality assurance (and privacy), and the levels of accountability and collaboration throughout the process. Data Governance. Gracias, — Alfred.

Data Governance 52

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Netmind

NOVEMBER 1, 2020

Data Management. A good data management strategy includes defining the processes for data definition, collection, analysis, and usage, including data quality assurance (and privacy), and the levels of accountability and collaboration throughout the process. Data Governance . Gracias, — Alfred.

Data Governance 52

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Business Analysis Hub

AUGUST 22, 2023

Ensure data quality and governance: AI relies heavily on data. Ensure you have high-quality data and robust data governance practices in place. Analyse data requirements : Assess the data required to build your AI solution.

Artificial Intelligence 52

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Astera

JULY 3, 2024

It creates a space for a scalable environment that can handle growing data, making it easier to implement and integrate new technologies. Moreover, a well-designed data architecture enhances data security and compliance by defining clear protocols for data governance.

Data Architecture 40

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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

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Astera

JANUARY 18, 2024

So, in case your data requires extensive transformation or cleaning, Fivetran is not the ideal solution. Fivetran might be a viable solution if your data is already in good shape, and you need to leverage the computing power of the destination system. Change data capture (CDC) for all relational databases in one platform.

Data Warehouse 52

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Astera

SEPTEMBER 25, 2024

With Astera, users can: Extract data from PDFs using our LLM-powered solution. Cleanse and validate Integrate data from CRMs, databases, EDI files, and APIs. Load data to various cloud data warehouses and lakes. Govern their data assets. AI-powered data mapping. Data validation checks to flag errors.

Real-time Data 52

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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

AUGUST 16, 2024

Let’s look at some reasons data migration projects fail: Risk of Data Integrity Loss Data quality maintenance is crucial to a smooth data migration process, especially when dealing with large volumes of data. Furthermore, 38% of companies experience delayed migration efforts by over a quarter.

Digital Transformation 52

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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

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Cprime

MAY 4, 2023

Data Warehouse Automation The data warehouse automation process accelerates the availability of analytics-ready data by automating the data warehouse lifecycle—from data modeling and real-time ingestion to data marts and governance.

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

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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

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Astera

JANUARY 12, 2024

Modern data architecture is characterized by flexibility and adaptability, allowing organizations to seamlessly integrate structured and unstructured data, facilitate real-time analytics, and ensure robust data governance and security, fostering data-driven insights.

Data Architecture 52

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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

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Insight Software

MARCH 4, 2024

The CSRD is a phased directive that requires all large companies and listed companies in the EU to disclose information on their environmental, social, and governance (ESG) performance, risks, and impacts. What types of existing IT systems are commonly used to store data required for ESRS disclosures?

IBM Support 52

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