IBM Big Data Hub

JANUARY 9, 2024

This may also entail working with new data through methods like web scraping or uploading. Data governance is an ongoing process in the data lifecycle to help ensure compliance with laws and company best practices. Data integration: These tools enable companies to combine disparate data sources into one secure location.

Enterprise 79

Enterprise Data Integration Data Quality Contextual Data 79

CIO Business Intelligence

JUNE 7, 2022

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results.

Data Analytics 133

Data Analytics Diagnostic Analytics Management Analytics 133

erwin

APRIL 29, 2020

Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. Business terms and data policies should be implemented through standardized and documented business rules.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on.

Data Governance 40

Data Governance Risk Metadata Management 40

Data Virtualization

JULY 20, 2023

But there’s a lot of confusion in the marketplace today between different types of architectures, specifically data mesh and data fabric, so I’ll. The post Logical Data Management and Data Mesh appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Management 40

Management Data Integration IT Data Quality 40

Ontotext

AUGUST 24, 2023

The second approach is to use some Data Integration Platform. As an enterprise-supported tool, it has already established how to make all data transformations. Then the recommended approach is to use one of the many JSON to RDF transformation frameworks to produce RDF data.

Data Integration 52

Data Integration Modeling Business Objectives Optimization 52

Cloudera

JUNE 1, 2021

It also means we can complete our business transformation with the systems, processes and people that support a new operating model. . And, the Enterprise Data Cloud category we invented is also growing. Said simply, Datacoral offers a fully-managed service for worry-free data integrations. Our strategy.

Uncertainty 144

Uncertainty Cost-Benefit Risk Strategy 144

DataKitchen

JULY 27, 2023

Let’s go through the ten Azure data pipeline tools Azure Data Factory : This cloud-based data integration service allows you to create data-driven workflows for orchestrating and automating data movement and transformation. It has a data pipeline tool , as well.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

Ontotext

MARCH 27, 2024

They also don’t have features for enterprise data management such as schema language, data validation capabilities, interoperable serialization formats, or a proper modeling language. RDF is used extensively for data publishing and data interchange and is based on W3C and other industry standards.

Modeling 69

Modeling Metadata Data Quality Enterprise 69

CIO Business Intelligence

JANUARY 9, 2023

Customers are increasingly demanding access to real-time data, and freight transportation provider Estes Express Lines is among the rising tide of enterprises overhauling their data operations to deliver it. We then started our exploration for a platform to solve the data problem,” Cournoyer says.

Data Strategy 111

Data Strategy Strategy Data Governance Marketing 111

Alation

JANUARY 17, 2023

What if, experts asked, you could load raw data into a warehouse, and then empower people to transform it for their own unique needs? Today, data integration platforms like Rivery do just that. By pushing the T to the last step in the process, such products have revolutionized how data is understood and analyzed.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Business Intelligence 52

AWS Big Data

OCTOBER 11, 2023

Due to this low complexity, the solution uses AWS serverless services to ingest the data, transform it, and make it available for analytics. The data ingestion process copies the machine-readable files from the hospitals, validates the data, and keeps the validated files available for analysis.

Visualization 92

Visualization Dashboards Data-driven Gap analysis 92

AWS Big Data

JULY 25, 2023

About Talend Talend is an AWS ISV Partner with the Amazon Redshift Ready Product designation and AWS Competencies in both Data and Analytics and Migration. Talend Cloud combines data integration, data integrity, and data governance in a single, unified platform that makes it easy to collect, transform, clean, govern, and share your data.

Data Analytics 52

Data Analytics Data Warehouse Analytics Data Processing 52

AWS Big Data

MARCH 3, 2023

Tricentis is the global leader in continuous testing for DevOps, cloud, and enterprise applications. Finally, data integrity is of paramount importance. Every event in the data source can be relevant, and our customers don’t tolerate data loss, poor data quality, or discrepancies between the source and Tricentis Analytics.

Software 52

Software Data Lake Testing Cost-Benefit 52

Alation

AUGUST 11, 2022

Everybody’s trying to solve this same problem (of leveraging mountains of data), but they’re going about it in slightly different ways. Data fabric is a technology architecture. It’s a data integration pattern that brings together different systems, with the metadata, knowledge graphs, and a semantic layer on top.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

Alation

OCTOBER 28, 2021

Too much access increases the risk that data can be changed or stolen. Remove Low Quality, Unused, or “Stale” Data. In healthcare especially, data integrity is incredibly important. Low quality, unused, or “stale” data can negatively impact research by skewing findings.

Data Governance 52

Data Governance Measurement Modeling Metrics 52

Octopai

NOVEMBER 13, 2022

The modern data stack is a data management system built out of cloud-based data systems. A given modern data stack will usually include components for data ingestion from your data sources, data transformation, data storage, data analysis and reporting.

Enterprise 77

Enterprise Data Warehouse Reporting Metadata 77

CIO Business Intelligence

NOVEMBER 7, 2023

In fact, as companies undertake digital transformations , usually the data transformation comes first, and doing so often begins with breaking down data — and political — silos in various corners of the enterprise. Some of this data might previously have been accessible to only a small number of groups or users.

Data Warehouse 141

Data Warehouse Digital Transformation Reporting Data-driven 141

AWS Big Data

NOVEMBER 7, 2023

For these, AWS Glue provides fast, scalable data transformation. Third, AWS continues adding support for more data sources including connections to software as a service (SaaS) applications, on-premises applications, and other clouds so organizations can act on their data. Visit Data integration with AWS to learn more.

Dashboards 73

Dashboards Data-driven Data Integration Data Lake 73

Smarten

AUGUST 4, 2023

The Right Self-Serve Data Preparation Solution is Sophisticated, Easy-to-Use and Ensures User Adoption! When your enterprise decides to roll out analytics for business users, it is important to implement the right solution. Sophisticated Functionality – Don’t sacrifice functionality to get ease-of-use.

Data Lake 52

Data Lake Machine Learning Data Integration Optimization 52

AWS Big Data

NOVEMBER 29, 2023

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake 99

Data Lake Management Metrics Data Warehouse 99

erwin

MARCH 26, 2020

Or the product line manager who wants to understand enterprise impact of pricing changes. David Loshin explores this concept in an erwin-sponsored whitepaper, Data Intelligence: Empowering the Citizen Analyst with Democratized Data. Reducing the IT bottleneck that creates barriers to data accessibility.

Data-driven 90

Data-driven Unstructured Data Enterprise Finance 90

Smarten

JANUARY 4, 2018

Think of your data warehouse as an active repository that is ever changing as new data sources keep on getting added and existing data sources keep on getting updated. In order to manage the environment, an organization must dedicate resources to monitor and track ETL process, its data flow, data integration and data updates.

Data Warehouse 40

Data Warehouse OLAP Data Governance Optimization 40

Sisense

MAY 14, 2019

What was once a bold prediction is becoming more obvious by the day; current leaders in every industry are either disruptors that dominate legacy industries leveraging big data, or they’re traditional enterprises that see data as an opportunity to transform their products and services.

Data-driven 49

Data-driven Machine Learning Business Intelligence Consulting 49

FineReport

MARCH 31, 2024

Furthermore, these tools boast customization options, allowing users to tailor data sources to address areas critical to their business success, thereby generating actionable insights and customizable reports. Best BI Tools for Data Analysts 3.1 Pricing might be relatively high for customers with fewer users. Try FineBI Now 3.3

Dashboards 52

Dashboards Visualization Data mining Data-driven 52

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Introduction. CRM platforms).

Data Processing 88

Data Processing Data Warehouse Enterprise Visualization 88

CIO Business Intelligence

JANUARY 24, 2024

Ankit Thakkar, Enterprise Data Management Leader, Thermo Fisher Scientific In the context of Business Process Automation (BPA), a layered-based architecture for Agent Swarms can significantly enhance efficiency, adaptability, and decision-making.

IoT 107

IoT Machine Learning Optimization Internet of Things 107

Jet Global

AUGUST 24, 2023

It streamlines data integration, ensures real-time access to accurate information, enhances collaboration, and provides the flexibility needed to adapt to evolving ERP systems and business requirements. Data transformation ensures that the data aligns with the requirements of the new cloud ERP system.

Finance 52

Finance Reporting Data Integration Risk 52

Jet Global

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping is important for several reasons.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

NOVEMBER 7, 2023

When extracting your financial and operational reporting data from a cloud ERP, your enterprise organization needs accurate, cost-efficient, user-friendly insights into that data. Enterprise-level organizations like yours often have multiple data sources and systems. The alternative to BICC is BI Publisher (BIP)​.

Enterprise 52

Enterprise Data Warehouse Operational Reporting Reporting 52

Jet Global

MARCH 14, 2024

Microsoft recently introduced a comprehensive analytics solution for its enterprise customers, Microsoft Fabric. The solution offers data movement, data science, real-time analytics, and business intelligence within a single platform. It enhances analytics capabilities, streamlines migration, and enhances data integration.

Analytics 52

Analytics Management Reporting Enterprise 52

Jet Global

MARCH 13, 2024

Complex Data Structures and Integration Processes Dynamics data structures are already complex – finance teams navigating Dynamics data frequently require IT department support to complete their routine reporting. With Atlas, you can put your data security concerns to rest.

Reporting 52

Reporting Finance Data Quality Sales 52

Jet Global

MAY 1, 2023

Traditional BI Platforms Traditional BI platforms are centrally managed, enterprise-class platforms. These sit on top of data warehouses that are strictly governed by IT departments. The role of traditional BI platforms is to collect data from various business systems. addresses).

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52