Dataversity

MARCH 10, 2021

Taking a holistic approach to data requires considering the entire data lifecycle – from gathering, integrating, and organizing data to analyzing and maintaining it. Companies must create a standard for their data that fits their business needs and processes. Click to learn more about author Olivia Hinkle.

Data Management 246

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Domo

MAY 30, 2024

By establishing a strong foundation, improving your data integrity and security, and fostering a data-quality culture, you can make sure your data is as ready for AI as you are. You could also establish key performance indicators (KPIs) related to data quality and integrate them into performance evaluations.

Data Quality 105

Data Quality Data Governance Government Data Security 105

Smart Data Collective

JUNE 19, 2019

Aligning these elements of risk management with the handling of big data requires that you establish real-time monitoring controls. They include the identification of the potential risk, analysis of its potential effects, prioritizing, and developing a plan on how to manage the risk in case it occurs.

Big Data 230

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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

Data Requirement Data Management Data Quality Data Governance 130

Ntara

JANUARY 13, 2025

Benefits of investing in PIM software first PIM may be more critical when you have significant compliance or regulatory data required to sell your products. In some industries, that type of data might be more critical (or more of a bottleneck to selling) than having a robust visual media library.

Retail 52

Retail Digital Government Data Governance 52

ElegantJ BI

SEPTEMBER 30, 2021

Suitable For: Use by business units, departments or specific roles within the organization that have a need to analyze and report and require high quality data and good performance. Advantages: Can provide secured access to data required by certain team members and business units.

Data Management 130

Data Management Data Warehouse Government Data Governance 130

ElegantJ BI

SEPTEMBER 30, 2021

Suitable For: Use by business units, departments or specific roles within the organization that have a need to analyze and report and require high quality data and good performance. Advantages: Can provide secured access to data required by certain team members and business units.

Data Management 130

Data Management Data Warehouse Government Data Governance 130

ElegantJ BI

SEPTEMBER 30, 2021

Suitable For: Use by business units, departments or specific roles within the organization that have a need to analyze and report and require high quality data and good performance. Advantages: Can provide secured access to data required by certain team members and business units.

Data Management 130

Data Management Data Warehouse Government Data Governance 130

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.

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IBM Support Data Quality IBM Maintenance IBM cost 52

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?

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Astera

OCTOBER 12, 2023

The platform also allows you to implement rigorous data validation checks and customize rules based on your specific requirements. Furthermore, by providing real-time data health checks, the platform provides instant feedback on the data quality, enabling you to keep track of changes.

Data Quality 52

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Astera

APRIL 28, 2023

Final Verdict: Intelligent Systems are Changing the Game Intelligent systems are revolutionizing data management by providing new and innovative ways to analyze, process, and interpret vast amounts of data. Serving as a unified data management solution.

Data Management 93

Data Management Artificial Intelligence Predictive Analytics Data Quality 93

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

IBM Support Data Quality IBM Maintenance IBM cost 52

Astera

JANUARY 9, 2023

With the advancements in cloud technology, a single cloud provider can easily fulfill all data requirements. Moreover, you should have complete data visibility to carry out a meaningful analysis. Data Quality. Let’s delve into the details. Why Multi-Cloud Strategy Makes Sense?

Data Quality 52

Data Quality Visualization Data Requirement Data Management 52

Astera

MARCH 26, 2023

This can hinder the ability to gain meaningful insights from data Inaccurate data Quality and accuracy of data are crucial in the insurance industry, given their significant impact on decision-making and risk assessment.

Documentation 52

Documentation Data Quality Data Warehouse Data Management 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 Data Warehouse Data Quality 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.

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Data Governance Government IBM Maintenance Data Quality 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.

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Data Quality Monitoring Artificial Intelligence Government 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

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

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. This includes data collection, storage, and analysis.

Artificial Intelligence 52

Artificial Intelligence Monitoring Customer Experience Customer Experience 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

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

Tableau

APRIL 6, 2022

SDOH data is an absolute necessity for the effective analysis of potential health inequities and associated mitigation strategies. Healthcare organizations are also working to mature their data quality and management solutions to ensure they have fully integrated, high-quality, trusted, accurate, complete, and standardized SDOH data.

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Healthcare Supply Chain Real-time Data Government 98

Tableau

APRIL 6, 2022

SDOH data is an absolute necessity for the effective analysis of potential health inequities and associated mitigation strategies. Healthcare organizations are also working to mature their data quality and management solutions to ensure they have fully integrated, high-quality, trusted, accurate, complete, and standardized SDOH data.

Healthcare 98

Healthcare Supply Chain Real-time Data Government 98

Astera

OCTOBER 25, 2023

Data transformation is a process that can help them overcome these challenges by changing the structure and format of raw data to make it more suitable for analysis. This improves data quality and facilitates analysis, enabling them to leverage more effectively in decision making.

Data Quality 52

Data Quality Supply Chain Healthcare Planning 52

Astera

JUNE 5, 2024

This streaming data is ingested through efficient data transfer protocols and connectors. Stream Processing Stream processing layers transform the incoming data into a usable state through data validation, cleaning, normalization, data quality checks, and transformations.

Real-time Data 52

Real-time Data Data Warehouse Healthcare Monitoring 52

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

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

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. 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

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. Data quality is a priority for Astera. Advanced Data Transformation : Offers a vast library of transformations for preparing analysis-ready data.

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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

Data Warehouse Data Quality Data Management Government 52

Astera

NOVEMBER 1, 2023

Data Integration: A data warehouse enables seamless integration of data from various systems and eliminates data silos and promotes interoperability and overall performance. Data-driven Finance with Astera Download Now Who Can Benefit from a Finance Data Warehouse?

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Data Warehouse Finance Data Quality Banking 52

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

Government Data Requirement Data Quality Data Governance 52

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. How do we ensure good data governance?

Data Governance 52

Data Governance Government Data Requirement Vision 52

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