Alation

JANUARY 17, 2023

A typical modern data stack consists of the following: A data warehouse. Extract, load, Transform (ELT) tools. Data ingestion/integration services. Data orchestration tools. Business intelligence (BI) platforms. How Did the Modern Data Stack Get Started? Who Can Adopt the Modern Data Stack?

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Business Intelligence 52

Alation

JUNE 28, 2022

This is where metadata, or the data about data, comes into play. Having a data catalog is the cornerstone of your data governance strategy, but what supports your data catalog? Your metadata management framework provides the underlying structure that makes your data accessible and manageable.

Metadata 52

Metadata Management Data Governance Machine Learning 52

CIO Business Intelligence

JANUARY 9, 2023

To fuel self-service analytics and provide the real-time information customers and internal stakeholders need to meet customers’ shipping requirements, the Richmond, VA-based company, which operates a fleet of more than 8,500 tractors and 34,000 trailers, has embarked on a data transformation journey to improve data integration and data management.

Data Strategy 111

Data Strategy Strategy Data Governance Marketing 111

IBM Big Data Hub

MARCH 14, 2024

.   Request a live demo or start a proof of concept with Amazon RDS for Db2 Db2 Warehouse SaaS on AWS The cloud-native Db2 Warehouse fulfills your price and performance objectives for mission-critical operational analytics, business intelligence (BI) and mixed workloads.

Cost-Benefit 80

Cost-Benefit Metadata Optimization Management 80

AWS Big Data

JULY 25, 2023

Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver fast performance for even the most demanding and unpredictable workloads, and you pay only for what you use. For more information on how to set up a tDBBulkExec component, see tDBBulkExec.

Data Analytics 52

Data Analytics Data Warehouse Analytics Data Processing 52

Octopai

NOVEMBER 23, 2020

In fact, the LIBOR transition program marks one of the largest data transformation obstacles ever seen in financial services. Building an inventory of what will be affected is a huge undertaking across all of the data, reports, and structures that must be accounted for. A New Approach to Enterprise Business Intelligence.

Metadata 54

Metadata Enterprise Business Intelligence Data Governance 54

CIO Business Intelligence

JUNE 14, 2022

I think that speaks volumes to the type of commitment that organizations have to make around data in order to actually move the needle.”. So if funding and C-suite attention aren’t enough, what then is the key to ensuring an organization’s data transformation is successful? Analytics, Chief Data Officer, Data Management

Data Transformation 145

Data Transformation Data-driven Data Strategy Strategy 145

CIO Business Intelligence

NOVEMBER 7, 2023

Given the importance of sharing information among diverse disciplines in the era of digital transformation, this concept is arguably as important as ever. The aim is to normalize, aggregate, and eventually make available to analysts across the organization data that originates in various pockets of the enterprise.

Data Warehouse 142

Data Warehouse Digital Transformation Reporting Data-driven 142

Smarten

JANUARY 4, 2018

So, the idea of data access by business users may cause concern, and the IT staff may wonder whether this access signals the end of the true ETL process along with the comprehensive maintenance and data governance policies. Give the Power to Business Users. Preserving Traditional ETL.

Data Warehouse 40

Data Warehouse OLAP Data Governance Optimization 40

CIO Business Intelligence

JANUARY 24, 2024

Gather/Insert data on market trends, customer behavior, inventory levels, or operational efficiency. IoT, Web Scraping, API, IDP, RPA Data Processing Data Pipelines and Analysis Layer Employ data pipelines with algorithms to filter, sort, and interpret data, transforming raw information into actionable insights.

IoT 110

IoT Machine Learning Optimization Internet of Things 110

AWS Big Data

DECEMBER 21, 2023

This is the Data Mart stage. The data products from the Business Vault and Data Mart stages are now available for consumers. smava decided to use Tableau for business intelligence, data visualization, and further analytics.

Data Lake 87

Data Lake Data Warehouse Data-driven B2B 87

Birst BI

FEBRUARY 25, 2020

Managing large-scale data warehouse systems has been known to be very administrative, costly, and lead to analytic silos. The good news is that Snowflake, the cloud data platform, lowers costs and administrative overhead. The result is a lower total cost of ownership and trusted data and analytics.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Architecture Enterprise 52

Jet Global

FEBRUARY 23, 2024

This field guide to data mapping will explore how data mapping connects volumes of data for enhanced decision-making. Why Data Mapping is Important Data mapping is a critical element of any data management initiative, such as data integration, data migration, data transformation, data warehousing, or automation.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

MARCH 14, 2024

The solution offers data movement, data science, real-time analytics, and business intelligence within a single platform. It offers a transparent and accurate view of how data flows through the system, ensuring robust compliance.

Analytics 52

Analytics Management Reporting Enterprise 52