datapine

MAY 29, 2019

BI architecture has emerged to meet those requirements, with data warehousing as the backbone of these processes. One of the BI architecture components is data warehousing. What Is Data Warehousing And Business Intelligence? BI Architecture Framework In Modern Business. What Is BI Architecture?

Business Intelligence 196

Business Intelligence Data Warehouse Dashboards Visualization 196

TDAN

NOVEMBER 17, 2020

There is a great deal of talk in our industry about the importance of having common, standard data semantics and language and the value this brings. However, I think one of the greatest obstacles in achieving this is what I call data ‘mine’ing. I am not talking about ‘data mining’ meaning, “the process of collecting, […].

Data mining 82

Data mining Metadata Data Governance Business Intelligence 82

Peter James Thomas

AUGUST 18, 2018

From 2000 to 2015, I had some success [5] with designing and implementing Data Warehouse architectures much like the following: As a lot of my work then was in Insurance or related fields, the Analytical Repositories tended to be Actuarial Databases and / or Exposure Management Databases, developed in collaboration with such teams.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Rita Sallam

JULY 22, 2016

By contrast, traditional BI platforms are designed to support modular development of IT-produced analytic content, specialized tools and skills, and significant upfront data modeling, coupled with a predefined metadata layer, is required to access their analytic capabilities.

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

CIO Business Intelligence

JANUARY 15, 2024

Her team spent about a year trying to understand the information landscape, the data, and the metadata schemas. Cleared for launch Bugbee is no stranger to data management and data stewardship. She cut her teeth in the field working to improve metadata quality in Data.gov and on President Obama’s Climate Data Initiative.

Informatics 131

Informatics Metadata Data mining Digital Transformation 131

Smart Data Collective

SEPTEMBER 26, 2021

Data is processed to generate information, which can be later used for creating better business strategies and increasing the company’s competitive edge. The raw data can be fed into a database or data warehouse. An analyst can examine the data using business intelligence tools to derive useful information. .

Metadata 96

Metadata Visualization Unstructured Data Data mining 96

DataKitchen

OCTOBER 11, 2021

Monitoring Job Metadata. Monitoring and tracking is an essential feature that many data teams are looking to add to their pipelines. Figure 7 shows how the DataKitchen DataOps Platform helps to keep track of all the instances of a job being submitted and its metadata. Priyanjna Sharma.

Testing 157

Testing Metadata Dashboards Statistics 157

IBM Big Data Hub

MARCH 14, 2024

This involves unifying and sharing a single copy of data and metadata across IBM® watsonx.data ™, IBM® Db2 ®, IBM® Db2® Warehouse and IBM® Netezza ®, using native integrations and supporting open formats, all without the need for migration or recataloging. .

Cost-Benefit 84

Cost-Benefit Metadata Optimization Management 84

Alation

AUGUST 26, 2021

What Is Data Intelligence? Data intelligence is a system to deliver trustworthy, reliable data. It includes intelligence about data, or metadata. IDC coined the term, stating, “data intelligence helps organizations answer six fundamental questions about data.”

Metadata 64

Metadata Data Governance Dashboards Software 64

Jet Global

MAY 1, 2023

Learn how embedded analytics are different from traditional business intelligence and what analytics users expect. Embedded Analytics Definition Embedded analytics are the integration of analytics content and capabilities within applications, such as business process applications (e.g., that gathers data from many sources.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52