AWS Big Data

MARCH 27, 2024

AWS has invested in a zero-ETL (extract, transform, and load) future so that builders can focus more on creating value from data, instead of having to spend time preparing data for analysis. You can send data from your streaming source to this resource for ingesting the data into a Redshift data warehouse.

Data Analytics 94

Data Analytics Analytics Data Warehouse Data Lake 94

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

BI-Survey

FEBRUARY 17, 2020

From operational systems to support “smart processes”, to the data warehouse for enterprise management, to exploring new use cases through advanced analytics : all of these environments incorporate disparate systems, each containing data fragments optimized for their own specific task. .

Data Governance 89

Data Governance Data Warehouse Data Quality Data Strategy 89

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. You can use the same capabilities to serve financial reporting, measure operational performance, or even monetize data assets.

Data Strategy 70

Data Strategy Strategy Data Warehouse Prescriptive Analytics 70

IBM Big Data Hub

APRIL 27, 2022

The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale. Read: The first capability of a data fabric is a semantic knowledge data catalog, but what are the other 5 core capabilities of a data fabric? 11 May 2021. .

Management 90

Management Metadata Data Architecture Data Lake 90

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . QuerySurge – Continuously detect data issues in your delivery pipelines. Meta-Orchestration .

Testing 307

Testing Machine Learning Consulting Data Quality 307

AWS Big Data

JULY 19, 2023

Amazon Redshift is a fully managed and petabyte-scale cloud data warehouse that is used by tens of thousands of customers to process exabytes of data every day to power their analytics workload. You can structure your data, measure business processes, and get valuable insights quickly can be done by using a dimensional model.

Modeling 69

Modeling Sales Data Warehouse Snapshot 69

Jen Stirrup

MARCH 29, 2023

However, to turn data into a business problem, organizations need support to move away from technical issues to start getting value as quickly as possible. SAP Datasphere simplifies data integration, cataloging, semantic modeling, warehousing, federation, and virtualization through a unified interface. Why is this interesting?

Metadata 120

Metadata Data Warehouse Data Integration Business Intelligence 120

IBM Big Data Hub

OCTOBER 12, 2023

Accounting for the complexities of the AI lifecycle Unfortunately, typical data storage and data governance tools fall short in the AI arena when it comes to helping an organization perform the tasks that underline efficient and responsible AI lifecycle management. And that makes sense.

Cost-Benefit 85

Cost-Benefit Metadata Data Governance Modeling 85

datapine

FEBRUARY 22, 2022

IT should be involved to ensure governance, knowledge transfer, data integrity, and the actual implementation. This should also include creating a plan for data storage services. Are the data sources going to remain disparate? Or does building a data warehouse make sense for your organization?

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

FineReport

APRIL 3, 2024

In today’s data-driven landscape, businesses are leaning more on BI tools , particularly BI dashboard solutions, to enhance decision-making through data visualization. Throughout this article, we’ll explore the importance of BI, data visualization, and dashboard tools in navigating intricate data landscapes.

Dashboards 52

Dashboards Visualization Metrics Interactive 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 helps standardize, visualize, and understand data across different systems and applications.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

OCTOBER 11, 2023

Another hurdle is the task of managing diverse data sources, as organizations typically store data in various formats and locations. Ensuring that embedded analytics can access and analyze data from these multiple sources can pose a substantial technical difficulty, requiring powerful data integration capabilities.

Dashboards 52

Dashboards Data-driven Software Reporting 52

Jet Global

OCTOBER 26, 2023

Low data quality causes not only costly errors and compliance issues, it also reduces stakeholder confidence in the reported information. Both JDE and EBS are highly complex and may involve multiple modules that store data in different formats. None of which is good for your team.

Data Quality 52

Data Quality Reporting Operational Reporting Finance 52

Jet Global

MARCH 14, 2024

Jet streamlines many aspects of data administration, greatly improving data solutions built on Microsoft Fabric. It enhances analytics capabilities, streamlines migration, and enhances data integration. Through Jet’s integration with Fabric, your organization can better handle, process, and use your data.

Analytics 52

Analytics Management Reporting Enterprise 52

Jet Global

MARCH 13, 2024

Data Quality and Consistency Maintaining data quality and consistency across diverse sources is a challenge, even when integrating legacy data from within the Microsoft ecosystem. With Atlas, you can put your data security concerns to rest.

Reporting 52

Reporting Finance Data Quality Sales 52

Jet Global

FEBRUARY 29, 2024

3) Data Fragmentation and Inconsistency Large organizations often grapple with disparate, ungoverned data sets scattered across various spreadsheets and systems. This fragmentation results in the lack of a reliable, single source of truth for budget data, making it challenging to maintain data integrity and consistency.

Forecasting 52

Forecasting Reporting Data-driven Software 52

Jet Global

OCTOBER 24, 2022

Analytics and data visualizations have the power to elevate a software product, making it a powerful tool that helps each user fulfill their mission more effectively. Modern analytics offers a different approach that incorporates data access, data governance, and dashboard interactivity – simplifying access to information.

Analytics 52

Analytics Dashboards Software Reporting 52

Jet Global

MARCH 23, 2023

Whatever their needs are, provide your end-users with tailored self-service capabilities for a more productive, engaging, and satisfying data experience. Some organizations tightly control access to their data, which can frustrate users who want to run their own queries to combine data sets or create dashboards from a single set of data.

Dashboards 52

Dashboards Reporting Key Performance Indicator Interactive 52