Analytics Vidhya

JULY 16, 2022

This article was published as a part of the Data Science Blogathon. Introduction The volume of data collected worldwide has drastically increased over the past decade. Nowadays, data is continuously generated if we open an app, perform a Google search, or simply move from place to place with our mobile devices.

Data Collection 331

Data Collection Data Science Publishing Analytics 331

Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Digital Transformation 103

Digital Transformation Manufacturing Data Warehouse Predictive Analytics 103

Occam's Razor

JUNE 25, 2012

The way data is collected online and what happens to it is a much-scrutinized issue (and rightly so). Digital data collection is also exceedingly complex, perhaps a reflection of the organic nature, and subsequent explosion, of the internet. Web Data Collection Context: Cookies and Tools.

Data Collection 130

Data Collection Advertising Metrics Reporting 130

AWS Big Data

DECEMBER 5, 2023

The counties that are in lighter shades represent limited survey responses and need to be included in the targeted data collection strategy. Finally, the dashboard’s user-friendly interface made survey data more accessible to a wider range of stakeholders. She helps customers architect data analytics solutions at scale on AWS.

Measurement 80

Measurement Dashboards Data Warehouse Analytics 80

Smart Data Collective

APRIL 8, 2020

Dating back to the 1970s, the data warehousing market emerged when computer scientist Bill Inmon first coined the term ‘data warehouse’. Created as on-premise servers, the early data warehouses were built to perform on just a gigabyte scale. The post How Will The Cloud Impact Data Warehousing Technologies?

Technology 132

Technology Data Warehouse Big Data Machine Learning 132

CIO Business Intelligence

OCTOBER 26, 2023

This is done by mining complex data using BI software and tools , comparing data to competitors and industry trends, and creating visualizations that communicate findings to others in the organization.

Business Intelligence 126

Business Intelligence Data-driven Statistics Data Warehouse 126

Sisense

APRIL 26, 2020

In our modern digital world, proper use of data can play a huge role in a business’s success. Datasets are exploding at an ever-accelerating rate, so collecting and analyzing data to maximum effect is crucial. Companies and businesses focus a lot on data collection in order to make sure they can get valuable insights out of it.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

CIO Business Intelligence

MAY 16, 2022

From the factory floor to online commerce sites and containers shuttling goods across the global supply chain, the proliferation of data collected at the edge is creating opportunities for real-time insights that elevate decision-making. How edge refines data strategy.

IoT 98

IoT Data Warehouse Internet of Things Machine Learning 98

CIO Business Intelligence

MARCH 16, 2022

The same goes for the adoption of data warehouse and business intelligence. The telecom sector prepares the data warehouse and business intelligence use cases even before they go live with their first customer. With regard to analytics in general, sadly, many organisations fail in their efforts to become data-driven.

Technology 115

Technology Digital Transformation Business Intelligence Data Warehouse 115

CIO Business Intelligence

SEPTEMBER 14, 2023

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines that convert raw data into formats usable by data scientists, data-centric applications, and other data consumers.

Analytics 135

Analytics Data Science Unstructured Data Data mining 135

FineReport

AUGUST 14, 2020

It is composed of three functional parts: the underlying data, data analysis, and data presentation. The underlying data is in charge of data management, covering data collection, ETL, building a data warehouse, etc.

Reporting 98

Reporting Informatics OLAP Data Warehouse 98

CIO Business Intelligence

SEPTEMBER 8, 2022

Most organizations understand the profound impact that data is having on modern business. In Foundry’s 2022 Data & Analytics Study , 88% of IT decision-makers agree that data collection and analysis have the potential to fundamentally change their business models over the next three years.

Cost-Benefit 122

Cost-Benefit Internet of Things Data-driven Data Lake 122

Cloudera

JUNE 2, 2022

Use cases demand that data no longer be distributed to just a data warehouse or subset of data sources, but to a diverse set of hybrid services across cloud providers and on-prem. . Instead they built or purchased tools for data collection that are confined with a class of sources and destinations.

Enterprise 98

Enterprise Data Lake Data Collection Data-driven 98

Jet Global

APRIL 2, 2019

And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing.

Enterprise 80

Enterprise OLAP Business Intelligence Internet of Things 80

CIO Business Intelligence

JULY 13, 2022

Use cases demand that data no longer be distributed to just a data warehouse or subset of data sources, but to a diverse set of hybrid services across cloud providers and on-prem. . Instead they built or purchased tools for data collection that are confined with a class of sources and destinations.

Enterprise 88

Enterprise Data Lake Data Collection Data-driven 88

DataRobot Blog

MARCH 8, 2023

Today, SAP and DataRobot announced a joint partnership to enable customers connect core SAP software, containing mission-critical business data, with the advanced Machine Learning capabilities of DataRobot to make more intelligent business predictions with advanced analytics.

Enterprise 105

Enterprise Experimentation Machine Learning Modeling 105

Cloudera

OCTOBER 11, 2022

The takeaway – businesses need control over all their data in order to achieve AI at scale and digital business transformation. The challenge for AI is how to do data in all its complexity – volume, variety, velocity. But it isn’t just aggregating data for models. Data needs to be prepared and analyzed.

Snapshot 89

Snapshot Data Science Digital Transformation Metadata 89

CIO Business Intelligence

NOVEMBER 22, 2022

External data sharing gets strategic Data sharing between business partners is becoming far easier and much more cooperative, observes Mike Bechtel, chief futurist at business advisory firm Deloitte Consulting. Data collection and management shouldn’t be classified as just another project, Gusher notes.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

CIO Business Intelligence

AUGUST 9, 2022

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. Data engineer job description.

Analytics 131

Analytics Data Science Statistics Unstructured Data 131

TDAN

JULY 4, 2023

The O*NET Data Collection Program, which is sponsored by the U.S. Department of Labor, is seeking the input of expert Data Warehousing Specialists. You have the opportunity to participate […]

Data Collection 52

Data Collection Data Architecture Data Warehouse Data Strategy 52

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 84

Data Science Data Analytics Prescriptive Analytics Analytics 84

Smart Data Collective

JULY 20, 2021

10 Panoply: In the world of CRM technology, Panoply is a data warehouse build that automates data collection, query optimization and storage management. This tool will help you to sync and store data from multiple sources quickly. With this tool, data transfer is faster and dynamic.

Sales 140

Sales Big Data Marketing Software 140

Ontotext

NOVEMBER 16, 2023

In a data mesh, domains are represented by a node, which can be an operational data store (ODS), a data warehouse, or a data lake tailored to the domain’s requirements. Don’t Forget Team Structure Team and organizational structure are an important aspect to consider for data mesh.

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

FineReport

SEPTEMBER 3, 2020

However, with the growing amount of data from many sources, the pain points of using Excel are more and more apparent. For example, data collection is time-consuming, and the data from scattered business systems can not be integrated. For most companies, using Excel to create reports is the most common reporting solution.

Reporting 95

Reporting Dashboards Visualization Data Warehouse 95

Jet Global

AUGUST 14, 2019

And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing.

Enterprise 70

Enterprise OLAP Business Intelligence Cost-Benefit 70

FineReport

FEBRUARY 28, 2020

According to the process from data to knowledge, the functional architecture of a general enterprise reporting system is shown below:It is divided into three functional levels: the underlying data, data analysis, and data presentation.

Reporting 96

Reporting Enterprise Visualization Business Intelligence 96

FineReport

MARCH 21, 2020

The process of data collection is time-consuming. Usually, the data is stored in Excel. But there is still a lot of repetitive work since different departments have separate data with Excel to calculate, accompanied by the tedious formula. . Many report styles in the company are the same.

Reporting 91

Reporting Data Warehouse Manufacturing Enterprise 91

Alation

JANUARY 26, 2023

Below are some examples of common data governance goals: All data collection, storage, and usage must meet the terms of legislation. Avoid fines that could result from issues such as data leakage or lack of data minimization practices. This is “table stakes” for any data governance program!).

Data Governance 52

Data Governance Strategy Data Quality Marketing 52

Jet Global

OCTOBER 2, 2019

And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing.

Enterprise 40

Enterprise OLAP Business Intelligence Cost-Benefit 40

Octopai

OCTOBER 3, 2019

Not only that, but the data you have is probably from multiple sources, stored in multiple formats, and housed in various file systems, databases, and data warehouses. It’s a big problem, and it’s getting bigger all the time because your business is growing.

Enterprise 41

Enterprise Metadata Data Warehouse Consulting 41

FineReport

MAY 9, 2020

The data layer of FineReport is responsible for data management, including data collection, ETL, building a data warehouse, etc. It supports multiple data sources and data integration. . Take FineReport as an example to help you better understand the 3-tier architecture.

Reporting 52

Reporting Sales Data Warehouse Software 52

Cloudera

FEBRUARY 16, 2022

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle. So, we have workspaces, projects and sessions in that order.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

Alation

APRIL 25, 2023

This reduces wait time, conserves resources, and allows you to put that data to work for your business faster. Data Extraction If you choose to maintain ownership of your data, you’ll need a way to transfer it from your central data warehouse to the operational tool that’ll help put it to use.

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

Alation

APRIL 25, 2023

This reduces wait time, conserves resources, and allows you to put that data to work for your business faster. Data Extraction If you choose to maintain ownership of your data, you’ll need a way to transfer it from your central data warehouse to the operational tool that’ll help put it to use.

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. Data ingestion/integration services. Data orchestration tools.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Business Intelligence 52

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. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 67

Data Strategy Strategy Data Warehouse Prescriptive Analytics 67

FineReport

MAY 31, 2023

There are also some other key challenges that will often be encountered during the process of creating financial dashboards: Data Integration : One of the primary challenges is integrating data from various sources. Ensuring seamless data integration and accuracy across these sources can be complex and time-consuming.

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52