BizAcuity

MARCH 1, 2019

IoT is basically an exchange of data or information in a connected or interconnected environment. As IoT devices generate large volumes of data, AI is functionally necessary to make sense of this data. Data is only useful when it is actionable for which it needs to be supplemented with context and creativity.

IoT 56

IoT Internet of Things Big Data Data-driven 56

CIO Business Intelligence

OCTOBER 1, 2022

The digital transformation of P&G’s manufacturing platform will enable the company to check product quality in real-time directly on the production line, maximize the resiliency of equipment while avoiding waste, and optimize the use of energy and water in manufacturing plants.

Manufacturing 145

Manufacturing Digital Transformation IoT Internet of Things 145

CIO Business Intelligence

JULY 12, 2023

However, some things are common to virtually all types of manufacturing: expensive equipment and trained human operators are always required, and both the machinery and the people need to be deployed in an optimal manner to keep costs down. We created a data lake, so we have access to all that data in a very efficient way,” says Papermaster.

Manufacturing 98

Manufacturing Cost-Benefit Data Lake Optimization 98

AWS Big Data

MARCH 7, 2023

Flexible and easy to use – The solutions should provide less restrictive, easy-to-access, and ready-to-use data. They should also provide optimal performance with low or no tuning. A data hub contains data at multiple levels of granularity and is often not integrated. Data repositories represent the hub.

Analytics 72

Analytics Data Warehouse Data Lake Metadata 72

Cloudera

MAY 12, 2021

Use cases could include but are not limited to: predictive maintenance, log data pipeline optimization, connected vehicles, industrial IoT, fraud detection, patient monitoring, network monitoring, and more. DATA FOR ENTERPRISE AI. Nominations for the 2021 Cloudera Data Impact Awards are open from now until July 23.

Digital Transformation 70

Digital Transformation Machine Learning Optimization Data Lake 70

Cloudera

FEBRUARY 19, 2018

But when companies are looking towards new technologies such as data lakes, machine learning or predictive analytics, SAP alone is just not enough. To keep up with tech trends, businesses have to face the challenges of integrating SAP with non-SAP technologies and embark on a crusade against data silos.

Data Lake 47

Data Lake Finance Data Governance Big Data 47

Cloudera

DECEMBER 19, 2018

The most common big data use case is data warehouse optimization. Big data architecture is used to augment different applications, operating alongside or in a discrete fashion with a data warehouse. A big data implementation may even replace a data warehouse entirely with a data lake.

Data Warehouse 53

Data Warehouse Analytics Big Data Data Architecture 53

CIO Business Intelligence

JUNE 29, 2022

If you can’t make sense of your business data, you’re effectively flying blind. Insights hidden in your data are essential for optimizing business operations, finetuning your customer experience, and developing new products — or new lines of business, like predictive maintenance. Azure Data Factory.

Data Lake 113

Data Lake Analytics Data Warehouse Machine Learning 113

Cloudera

JUNE 9, 2022

From origin through all points of consumption both on-prem and in the cloud, all data flows need to be controlled in a simple, secure, universal, scalable, and cost-effective way. controlling distribution while also allowing the freedom and flexibility to deliver the data to different services is more critical than ever. .

Data Collection 95

Data Collection IoT Data Lake Unstructured Data 95

Cloudera

JANUARY 30, 2023

Gartner defines dark data as “The information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing).”

IoT 84

IoT Data Architecture Data Lake Machine Learning 84

DataKitchen

JULY 27, 2023

Let’s go through the ten Azure data pipeline tools Azure Data Factory : This cloud-based data integration service allows you to create data-driven workflows for orchestrating and automating data movement and transformation. You can use it for big data analytics and machine learning workloads.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

IBM Big Data Hub

JANUARY 18, 2023

A read-optimized platform that can integrate data from multiple applications emerged. In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Value of the data projects are difficult to realize.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

AWS Big Data

MAY 16, 2024

Because of this, many organizations are utilizing them as a support geography, aggregating their data to these grids to optimize both their storage and analysis. Indexed data can be quickly joined across different datasets and aggregated at different levels of precision.

Data Warehouse 108

Data Warehouse Visualization Cost-Benefit Optimization 108

Sisense

MAY 11, 2021

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right data model is an important part of your data strategy.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

AWS Big Data

NOVEMBER 14, 2023

Ten years ago, we launched Amazon Kinesis Data Streams , the first cloud-native serverless streaming data service, to serve as the backbone for companies, to move data across system boundaries, breaking data silos. Amazon Kinesis Data Streams is a foundational data strategy pillar for tens of thousands of customers.

IoT 55

IoT Data-driven Data Lake Data Strategy 55

Cloudera

SEPTEMBER 15, 2023

This information is essential for the management of the telco business, from fault resolution to making sure families have the right content package for their needs, to supply chain dashboards for businesses based on IoT data. Access and the exchange of data is critical for managing the operations in many industries.

Strategy 81

Strategy Advertising IoT B2B 81

Corinium

APRIL 25, 2019

And it’s become a hyper-competitive business, so enhancing customer service through data is critical for maintaining customer loyalty. And more recently, we have also seen innovation with IOT (Internet Of Things). In data-driven organizations, data is flowing. The protection policy follows the data.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

AWS Big Data

FEBRUARY 21, 2023

Data warehouses are mostly built using the dimensional model approach, which has consistently met business needs. Additionally, scalability of the dimensional model is complex and poses a high risk of data integrity issues. What is a dimensional data model?

Data Warehouse 65

Data Warehouse Data Lake Cost-Benefit Metadata 65

CIO Business Intelligence

APRIL 18, 2023

Cloud-based network management increases agility and allows resource-constrained IT departments to focus on optimizing the network, not deploying, managing, or upgrading the network management system. Cloud-based network management also better aligns spend through a subscription, OpEx-driven model. Start using APs as an IoT gateway.

IoT 52

IoT Internet of Things Data Lake Optimization 52

Alation

APRIL 14, 2022

Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth. And, as organizations progress and grow, “data drift” starts to impact data usage, models, and your business. Pushing data to a data lake and assuming it is ready for use is shortsighted.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

erwin

DECEMBER 13, 2018

Data operations (DataOps) gains traction/will be fully optimized: Much like how DevOps has taken hold over the past decade, 2019 will see a similar push for DataOps. Data is no longer just an IT issue. As organizations become data-driven and awash in an overwhelming amount of data from multiple data sources (AI, IOT, ML, etc.),

Data Governance 91

Data Governance IoT Internet of Things Data-driven 91

Cloudera

OCTOBER 7, 2021

The need for a decentralized data mesh architecture stems from the challenges organizations faced when implementing more centralized data management architectures – challenges that can attributed to both technology (e.g., need to integrate multiple “point solutions” used in a data ecosystem) and organization reasons (e.g.,

Metadata 125

Metadata Cost-Benefit Enterprise Interactive 125

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