Cloudera

AUGUST 18, 2021

In digital transformation projects, it’s easy to imagine the benefits of cloud, hybrid, artificial intelligence (AI), and machine learning (ML) models. ML models powering AI use cases are becoming more and more ubiquitous in a variety of environments, especially at industrial organizations adopting Industry 4.0 technologies.

Data Lake 87

Data Lake IoT Internet of Things Data-driven 87

DataKitchen

SEPTEMBER 20, 2021

As productivity improves, you can widen the DataOps circle and carefully invest in strategic process change that serves as the foundation for further improvements in team velocity. For example, data cleansing, ETL, running a model, or even provisioning cloud infrastructure. Figure 8 shows an example Maturity Model result.

Testing 246

Testing Metrics Measurement Dashboards 246

IBM Big Data Hub

AUGUST 17, 2023

Enhanced monetary policy: CBDC can provide central banks with better control over monetary policy by allowing for more direct control over money supply and velocity, and by granting them visibility over payment flows.

Finance 74

Finance Risk Insurance Technology 74

Domino Data Lab

JUNE 15, 2021

Over the next decade, the companies that will beat competitors will be “model-driven” businesses. These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. Why Snowflake UDFs. 5.21.2'.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

CIO Business Intelligence

FEBRUARY 14, 2023

To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. It’s clear how these real-time data sources generate data streams that need new data and ML models for accurate decisions.

Data Strategy 113

Data Strategy Strategy Machine Learning Data-driven 113

erwin

DECEMBER 3, 2020

It’s no secret that data has grown in volume, variety and velocity, with 2.5 The answer is radical transformation, made possible by an intelligent, data-driven approach to: New business models. quintillion bytes generated every day and 90 percent of the world’s data volume created just in the last two years. New products and services.

Metadata 141

Metadata Data Governance Data-driven Digital Transformation 141

Domino Data Lab

JULY 28, 2021

In its current form , data science is limitless in terms of industry and application; the only requirement is data, compute power, data scientists, and a desire to become model-driven. Only when the business understands the models will they trust them to drive decisions in the business. The Data Science Toolkit.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Jedox

JUNE 26, 2019

Anders Liu-Lindberg, as a guest to the Jedox blog. Over the next few weeks, we’ll be sharing a blog series by Mr. Liu-Lindberg that focuses on how FP&A is changing in a rapidly evolving global marketplace and how you can help elevate the value your finance department provides across your organization. FP&A needs a new mindset.

Finance 89

Finance Data-driven Forecasting Marketing 89

erwin

MAY 14, 2021

But even though speed is critical, businesses must take the time to model and document new applications for compliance and transparency. For decades, data modeling has been the optimal way to design and deploy new relational databases with high-quality data sources and support application development.

Modeling 80

Modeling Digital Transformation Data-driven Optimization 80

Ontotext

FEBRUARY 12, 2024

Enterprises should identify and adopt specific data mesh elements to achieve velocity. Likewise, domain teams should oversee end-to-end processes from data collection, transformations, cleaning, and enrichment to modeling. Each team should be accountable for providing their prepared data sets to downstream systems.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

AWS Big Data

MARCH 8, 2023

This is especially important if you’re making time-sensitive decisions in a high-velocity data environment. Choose Use existing custom plugin and then pick the custom plugin with name starting msk-blog-debezium-source-plugin. key.converter.compatibility and value.converter.compatibility define the compatibility model.

Data-driven 72

Data-driven Testing Data Processing Management 72

Cloudera

AUGUST 31, 2020

COVID-19 has accelerated radical changes in businesses, business models, and customer behavior making it clear that we are entering the second phase of this revolution. The velocity that data enters an enterprise, together with the variety of sources, can make it difficult to process data and generate meaningful insights in real-time. .

Analytics 94

Analytics Manufacturing Insurance Data Processing 94

Sisense

JANUARY 21, 2021

The type of tool you need depends on (among other factors) the volume, variety, and velocity of the data you plan to process. Learn more: Evaluation, implementation, building a support model. Whether you produce an app, service, or experience, you’re definitely collecting a lot of data in the process. Hundreds of megabytes?

Analytics 98

Analytics Cost-Benefit Business Intelligence Visualization 98

erwin

MARCH 8, 2019

Then the metadata is classified against a semantic model held in a business glossary. After this classification, a second data quality check is performed based on the best-practice rules associated with the semantic model.

Data Governance 65

Data Governance Metadata Data Warehouse Data Quality 65

Ontotext

APRIL 10, 2024

Knowledge graphs and semantic metadata Knowledge graphs (KGs) are the key to: Advanced Data Architecture & Models like Data Fabric, Data Mesh Unified Data Access Semantic Data Integration These fundamental capabilities of KGs enable them to bridge the chasm between information and knowledge in the DIKW pyramid.

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

Cloudera

MARCH 27, 2019

Apache NiFi is a very mature open source solution meant for large scale, high velocity enterprise data ingestion use cases. MiNiFi also supports TensorFlow and this allows for Machine Learning (ML) models to be executed at the edge. It allows you to develop, deploy, run and monitor edge flow apps and ML models at the edge.

Management 71

Management IoT Machine Learning Digital Transformation 71

Cloudera

JUNE 7, 2021

We wanted the ability to easily incorporate existing enterprise data sources and high velocity/low latency streams of data in simple ways. faa_aircraft` (. `tailnumber` VARCHAR ( 255 ), `model` VARCHAR ( 255 ), ` serial ` VARCHAR ( 255 ), `icao` VARCHAR ( 255 ), `owner` VARCHAR ( 255 ). CREATE TABLE `CDP_Hive_Catalog`.`airplanes`.`faa_aircraft`

Analytics 61

Analytics Manufacturing Measurement IoT 61

Cloudera

FEBRUARY 7, 2019

The company sought a data management platform that would allow its enterprise to handle greater data variety, velocity and volume in a cost-effective manner. Enabling this transformation is the HDP platform, along with SAS Viya on Google Cloud , which has delivered machine learning models and personalization at scale.

Big Data 63

Big Data Data Analytics Analytics Data Architecture 63

Ontotext

JANUARY 6, 2021

Humans are stuck on this planet until they devised a way to travel, 11kms per second, the escape velocity from Earth. A knowledge graph provides centralized information control for enterprises at a time when it is no longer possible to integrate all transactional information due to its extreme volume, velocity and variety.

Metadata 59

Metadata Unstructured Data Cost-Benefit Enterprise 59

Ontotext

NOVEMBER 16, 2023

It also improves development velocity with better data governance and access with improved data quality aligned with business needs. Domains can ingest operational data, build analytical data models as data products, and publish them with data contracts to serve other domains’ data needs.

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

DataRobot Blog

MARCH 10, 2022

By enabling data scientists to rapidly iterate through model development, validation, and deployment, DataRobot provides the tools to blitz through steps four and five of the machine learning lifecycle with AutoML and Auto Time-Series capabilities. Train, Compare, Rank, Validate, and Select Models for Production.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

Cloudera

MAY 9, 2019

The report created a readiness model with five dimensions and various metrics under each dimension. The five dimensions of the readiness model are –. Data readiness – These set of metrics help you measure if your organization is geared up to handle the sheer volume, variety and velocity of IoT data. See you there!

IoT 52

IoT Modeling Metrics Measurement 52

Jedox

JUNE 26, 2019

Anders Larsen Lie-Lindberg, as a guest to the Jedox blog. Over the next few weeks, we’ll be sharing a blog series by Mr. Lindberg that focuses on how FP&A is changing in a rapidly evolving global marketplace and how you can help elevate the value your finance department provides across your organization. Taking action.

Finance 40

Finance Data-driven Forecasting Marketing 40

Sisense

MARCH 14, 2021

Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly.

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

AWS Big Data

OCTOBER 10, 2023

Due to the volume, velocity, and variety of data being ingested in data lakes, it can get challenging to develop and maintain policies and procedures to ensure data governance at scale for your data lake. AWS CloudFormation lets you model, provision, and manage AWS and third-party resources by treating infrastructure as code.

Data Quality 87

Data Quality Data Governance Data Lake Testing 87

AWS Big Data

APRIL 26, 2023

This is a guest blog post co-written with Corey Johnson from Huron. Enables a hub-and-spoke model for Data Access in multiple AWS accounts in a data mesh fashion. Conclusion This blog explained how Huron built an Asset Tracker to keep track of all QuickSight resources across the organization.

Metadata 52

Metadata Dashboards Visualization Consulting 52

Rocket-Powered Data Science

MARCH 19, 2019

Diversity in data is one of the three defining characteristics of big data — high data variety — along with high data volume and high velocity. We wrote a longer complete version of this article here: “ Busting Bias with More Data Variety ” at the Western Digital DataMakesPossible.com blog site.

Big Data 192

Big Data Statistics Manufacturing Data Science 192

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. Datatron — Automates deployment and monitoring of AI models.

Testing 307

Testing Machine Learning Consulting Data Quality 307

DataKitchen

JULY 27, 2023

Data warehousing with Azure Synapse Analytics and Power BI : Data from various sources can be ingested into Azure Synapse Analytics, where it can be transformed and modeled to suit the needs of your analysis. Azure Machine Learning can then use this data to train, test, and deploy machine learning models.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

O'Reilly on Data

MAY 14, 2020

Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model. In reality, many candidate models (frequently hundreds or even thousands) are created during the development process. Modelling: The model is often misconstrued as the most important component of an AI product.

Management 363

Management Experimentation B2B Machine Learning 363

Cloudera

OCTOBER 19, 2023

Large 5G networks will host tens of millions of connected devices (somewhere in the 1,000x capacity compared to 4G), each instrumented to generate telemetry data, giving telcos the ability to model and simulate operations at a level of detail previously impossible.

Data-driven 73

Data-driven Data Architecture Predictive Modeling Digital Transformation 73

CIO Business Intelligence

JUNE 23, 2022

Multi-cloud provides increased velocity, scale, and choice by enabling enterprises to create and deploy new services across clouds. Patrick Moorhead of Moor Insights and Strategy writes in his Forbes blog that, “If you are in the multi-cloud camp, which I think most of you are, then VMware Cloud provides a simple single cloud operating model.

Enterprise 95

Enterprise Machine Learning Management Optimization 95

erwin

AUGUST 28, 2020

While the exponential increase in the volume and variety of data has provided unparalleled insights for some businesses, only those with the means to deal with the velocity of data have reaped the rewards. By velocity, we mean the speed at which data can be processed and made useful. More on “The Three Vs of Data” here.

Data Governance 145

Data Governance IT Cost-Benefit Metadata 145

Cloudera

NOVEMBER 19, 2020

Some of these algorithms can be adaptive to quickly update the model to take into account new, previously unseen fraud tactics allowing for dynamic rule adjustment. This type of framework requires a streaming environment that provides continuous updates coupled with model performance monitoring to ensure consistent performance. .

Machine Learning 114

Machine Learning Data-driven Modeling Deep Learning 114

IBM Big Data Hub

MAY 23, 2023

“The biggest challenge for security is the complexity, the scale and the velocity at which it needs to operate. ” Smart and networked devices are becoming ubiquitous, yet existing security models are often designed only to protect the endpoint and the data center with technologies like firewalls.

Cost-Benefit 96

Cost-Benefit Strategy Reporting Marketing 96

AWS Big Data

JULY 12, 2023

This blog post is co-written with Zygimantas Koncius from Etleap. In this post, we explain how data teams can quickly configure low-latency data pipelines that ingest and model data from a variety of sources, using Etleap’s end-to-end pipelines with Amazon Redshift and dbt.

Data Warehouse 52

Data Warehouse Modeling Dashboards Data Lake 52