DataKitchen

SEPTEMBER 21, 2023

Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers.

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

Ontotext

MARCH 8, 2023

As such, most large financial organizations have moved their data to a data lake or a data warehouse to understand and manage financial risk in one place. Yet, the biggest challenge for risk analysis continues to suffer from lack of a scalable way of understanding how data is interrelated.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data.

Data Strategy 66

Data Strategy Strategy Data Warehouse Prescriptive Analytics 66

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. OwlDQ — Predictive data quality.

Testing 307

Testing Machine Learning Consulting Data Quality 307

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

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? What’s a data mesh?

Management 88

Management Metadata Data Architecture Data Lake 88

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

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

FEBRUARY 1, 2023

AWS Glue for ETL To meet customer demand while supporting the scale of new businesses’ data sources, it was critical for us to have a high degree of agility, scalability, and responsiveness in querying various data sources. Clients access this data store with an API’s.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

AWS Big Data

MARCH 3, 2023

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52

IBM Big Data Hub

OCTOBER 12, 2023

To optimize data analytics and AI workloads, organizations need a data store built on an open data lakehouse architecture. This type of architecture combines the performance and usability of a data warehouse with the flexibility and scalability of a data lake.

Cost-Benefit 83

Cost-Benefit Metadata Data Governance Modeling 83

Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. It can be used for something as visual as reducing traffic jams, to personalizing products and services, to improving the experience in multiplayer video games. We would like to talk about data visualization and its role in the big data movement.

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

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

AWS Big Data

APRIL 25, 2023

Data and its various uses is increasingly evident in companies, and each professional has their preferences about which technologies to use to visualize data, which isn’t necessarily in line with the technological needs and infrastructure of a company. In this post, we discuss why we chose QuickSight and how we implemented it.

Visualization 52

Visualization IT Data-driven Reporting 52

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 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

APRIL 14, 2022

Having accurate data is crucial to this process, but finance teams struggle to easily access and connect with data. Improve data quality. Some functional areas use business intelligence and data visualization tools, but operate in isolation with their own data sets, driving decisions related to that function only.

Data Collection 52

Data Collection Management Finance Reporting 52

Jet Global

OCTOBER 26, 2023

If your finance team is using JD Edwards (JDE) and Oracle E-Business Suite (EBS), it’s like they rely on well-maintained and accurate master data to drive meaningful insights through reporting. For these teams, data quality is critical. Ensuring that data is integrated seamlessly for reporting purposes can be a daunting task.

Data Quality 52

Data Quality Reporting Operational Reporting Finance 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

FEBRUARY 23, 2024

A Centralized Hub for Data Data silos are the number one inhibitor to commerce success regardless of your business model. Through effective workflow, data quality, and governance tools, a PIM ensures that disparate content is transformed into a company-wide strategic asset.

Management 52

Management Sales Publishing Data Quality 52

Jet Global

MARCH 13, 2024

Its easy-to-configure, pre-built templates get you up and running fast without having to understand complex Dynamics data structures. Free your team to explore data and create or modify reports on their own with no hard coding or programming skills required. With Atlas, you can put your data security concerns to rest.

Reporting 52

Reporting Finance Data Quality Sales 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

SEPTEMBER 22, 2023

Use Case #1 – Using ChatGPT to Analyze Any Data Set with Logi With the power of ChatGPT, Logi Symphony offers single-click data analysis by extracting insights from visual representations. You can create a button within Logi Symphony that extracts data from charts or visualizations and sends it to ChatGPT for analysis.

Dashboards 52

Dashboards Data-driven Visualization Interactive 52