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

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

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

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

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?

Data Warehouse 52

Data Warehouse Finance Data Quality Banking 52

Astera

FEBRUARY 7, 2024

Moreover, highly complex data require more development and maintenance resources to maintain zero-ETL solutions. Compared to zero-ETL , traditional ETL is well-suited for complex data transformations and extensive preprocessing. and transformations during the staging phase.

Real-time Data 52

Real-time Data Cybersecurity Data Quality Monitoring 52

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

MAY 9, 2023

Data Quality While traditional data integration tools have been sufficient to tackle data quality issues, up till now, they can no longer handle the extent of data coming in from a myriad of sources.

Healthcare 40

Healthcare Retail Finance Data Quality 40

Astera

MAY 4, 2023

This, in turn, enables businesses to automate the time-consuming task of manual data entry and processing, unlocking data for business intelligence and analytics initiatives. However , a Forbes study revealed up to 84% of data can be unreliable. Luckily, AI- enabled data prep can improve data quality in several ways.

Business Intelligence 40

Business Intelligence Business Intelligence Predictive Analytics Supply Chain 40

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

Astera

JULY 20, 2023

Best Practices for Successful EDI Mapping To achieve the most seamless interoperability capabilities and maximize the benefits of utilizing EDI tools, businesses can adhere to key best practices that ensure efficient mapping processes and optimal data compatibility.

Supply Chain 52

Supply Chain Documentation Digital Data Requirement 52

Smart Data Collective

JULY 9, 2023

By harmonising and standardising data through ETL, businesses can eliminate inconsistencies and achieve a single version of truth for analysis. Improved Data Quality Data quality is paramount when it comes to making accurate business decisions.

Data Warehouse 213

Data Warehouse Data Quality Data Analytics Data Analytics 213

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

Astera

MAY 25, 2023

However, businesses can also leverage data integration and management tools to enhance their security posture. How is big data secured? Big data is extremely valuable, but also vulnerable. Protecting big data requires a multi-faceted approach to security. Access Control Controlling access to sensitive data is key.

Big Data 40

Big Data Data Security Monitoring Data Quality 40

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. How do we ensure good data governance?

Data Governance 52

Data Governance Government Data Requirement Vision 52

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 176

Big Data Logistics Monitoring Data Analytics 176

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 Data Governance 52

Astera

JUNE 14, 2023

Limitations of Manual Document Data Extraction Besides being error-prone and time-consuming, manual document data extraction has several other challenges and limitations, including: Lack of Scalability: Manual methods are not scalable, making it challenging to handle increasing volumes of documents efficiently.

Documentation 52

Documentation Supply Chain Healthcare Logistics 52

Astera

DECEMBER 21, 2022

What’s more, modern data warehouses come with access control features to ensure that the data required for business intelligence is only visible to relevant personnel.

Data Warehouse 52

Data Warehouse Supply Chain Business Intelligence Business Intelligence 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?

Data Governance 52

Data Governance Government Data Quality Monitoring 52

Smart Data Collective

SEPTEMBER 21, 2021

The data contained can be both structured and unstructured and available in a variety of formats such as files, database applications, SaaS applications, etc. Processing such kinds of data require advanced technologies from ELT processing to real-time streaming. Data quality and governance.

Data Management 258

Data Management Government Data Quality Data Discovery 258

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

Data Mining 52

Data Mining Supply Chain Data Quality Visualization 52

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

Data Quality Monitoring Real-time Data 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

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 Government 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

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

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 Data Requirement Visualization Data Management 52

Astera

MAY 26, 2023

On average, it took the retailer 15 days (about 2 weeks) to process the invoices—from data extraction to payment. Consequently, the inefficient process was time-consuming and error-prone, causing delays in account payables, data quality discrepancies, and supply-chain disruptions.

Retail 40

Retail Supply Chain Finance Data Quality 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

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 Data Governance Government 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 Data Governance Government 130