Smart Data Collective

OCTOBER 13, 2021

As we have already said, the challenge for companies is to extract value from data, and to do so it is necessary to have the best visualization tools. Over time, it is true that artificial intelligence and deep learning models will be help process these massive amounts of data (in fact, this is already being done in some fields).

Visualization 145

Visualization Cost-Benefit Big Data Prescriptive Analytics 145

CIO Business Intelligence

MARCH 9, 2022

Data-driven organizations understand that data, when analyzed, is a strategic asset. It forms the basis for making informed decisions around product innovation, dynamic pricing, market expansion, and supply chain optimization. Ready to evolve your analytics strategy or improve your data quality? It’s the “new oil.”

Data-driven 110

Data-driven Analytics Machine Learning Deep Learning 110

CIO Business Intelligence

JANUARY 20, 2023

Blocking the move to a more AI-centric infrastructure, the survey noted, are concerns about cost and strategy plus overly complex existing data environments and infrastructure. Though experts agree on the difficulty of deploying new platforms across an enterprise, there are options for optimizing the value of AI and analytics projects. [2]

Analytics 137

Analytics Key Performance Indicator Unstructured Data Deep Learning 137

CIO Business Intelligence

MARCH 22, 2022

One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Another option is a data warehouse, which stores processed and refined data. Ready to evolve your analytics strategy or improve your data quality?

Analytics 115

Analytics Key Performance Indicator Data Warehouse Data-driven 115

Birst BI

NOVEMBER 20, 2018

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. After a few iterations, this results in a well-defined business question with identifiable supporting data.

Statistics 84

Statistics Machine Learning Deep Learning Sales 84

IBM Big Data Hub

MAY 9, 2023

After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. With watsonx.data , businesses can quickly connect to data, get trusted insights and reduce data warehouse costs.

Data Warehouse 106

Data Warehouse Machine Learning Cost-Benefit Metadata 106

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. Scale the problem to handle complex data structures. A Program Synthesis Primer ” – Aws Albarghouthi (2017-04-24).

Metadata 105

Metadata Data Science Machine Learning Data-driven 105

Domino Data Lab

OCTOBER 14, 2020

We needed an “evolvable architecture” which would work with the next deep learning framework or compute platform. This ability enhances the efficiency of operational management and optimizes the cost of experimentation. Hyperparameter Tuning. TFRecord and Parquet). Putting it all together.

Experimentation 99

Experimentation Deep Learning Modeling Testing 99

CIO Business Intelligence

AUGUST 21, 2024

The flashpoint moment is that rather than being based on rules, statistics, and thresholds, now these systems are being imbued with the power of deep learning and deep reinforcement learning brought about by neural networks,” Mattmann says. Plus, each agent can be optimized for its specific tasks.

Risk 136

Risk Insurance Cost-Benefit Software 136

Jen Stirrup

MAY 5, 2020

You can also use Azure Data Lake storage as well, which is optimized for high-performance analytics. It has native integration with other data sources, such as SQL Data Warehouse, Azure Cosmos, database storage, and even Azure Blob Storage as well. Azure Data Lake Store. Azure Databricks.

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89

IBM Big Data Hub

FEBRUARY 19, 2024

Cost optimization can be achieved through a combination of productivity enhancements, infrastructure savings and reductions in operating expenses. In 2023, the global data monetization market was valued at USD 3.5 When it comes to data optimization, most organizations focus solely on infrastructure cost reduction.

Cost-Benefit 63

Cost-Benefit Data-driven Optimization Marketing 63

IBM Big Data Hub

MAY 17, 2023

The data captured by the sensors and housed in the cloud flow into real-time monitoring for 24/7 visibility into your assets, enabling the Predictive Failure Model. DaaS uses built-in deep learning models that learn by analyzing images and video streams for classification.

Data Collection 76

Data Collection Visualization Data-driven Unstructured Data 76

Cloudera

FEBRUARY 16, 2022

This allows data scientists, engineers and data management teams to have the right level of access to effectively perform their role. By logging the performance of every combination of search parameters within an experiment, we can choose the optimal set of parameters when building a model.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

IBM Big Data Hub

JULY 6, 2023

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored. One challenge in applying data science is to identify pertinent business issues.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Cloudera

MAY 22, 2018

delivers the modern platform for machine learning and analytics, optimized for the cloud. It makes machine learning accessible and collaborative and easy to put into production. It enables a rich data warehouse experience, only with more fluidity and exploration of ad hoc questions. Cloudera Enterprise 6.0

Enterprise 40

Enterprise Recreation/Entertainment Data Warehouse Machine Learning 40

IBM Big Data Hub

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. Fortunately, data stores serve as secure data repositories and enable foundation models to scale in both terms of their size and their training data.

Modeling 55

Modeling Cost-Benefit Machine Learning Data Lake 55

Cloudera

SEPTEMBER 12, 2018

AbbVie, one of the world’s largest global research and development pharmaceutical companies, established a big data platform to provide end-to-end operations visibility, agility, and responsiveness. Modern Data Warehousing: Barclays (nominated together with BlueData ). IQVIA is re-envisioning healthcare using a data-driven approach.

Machine Learning 45

Machine Learning Big Data Data Science Data Warehouse 45

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning in marketing and sales According to Forbes , marketing and sales teams prioritize AI and ML more than any other enterprise department. Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). Computer vision fuels self-driving cars.

Machine Learning 48

Machine Learning Marketing Forecasting Modeling 48

Alation

APRIL 14, 2022

Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. Organizations launched initiatives to be “ data-driven ” (though we at Hired Brains Research prefer the term “data-aware”). Automatic sampling to test transformation. Scheduling. Target Matching.

Metadata 59

Metadata Data Governance Data-driven Modeling 59

O'Reilly on Data

SEPTEMBER 11, 2019

Deep learning,” for example, fell year over year to No. Increasingly, the term “data engineering” is synonymous with the practice of creating data pipelines, usually by hand. In quite another respect, however, modern data engineering has evolved to support a range of scenarios that simply were not imaginable 40 years ago.

IoT 25

IoT Big Data Data Warehouse Uncertainty 25

Domino Data Lab

JULY 22, 2019

The data governance, however, is still pretty much over on the data warehouse. Toward the end of the 2000s is when you first started getting teams and industry, as Josh Willis was showing really brilliantly last night, you first started getting some teams identified as “data science” teams.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

AWS Big Data

OCTOBER 25, 2024

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. Amazon Redshift’s advanced Query Optimizer is a crucial part of that leading performance.

Statistics 108

Statistics Data Warehouse Metadata Data Lake 108

CIO Business Intelligence

MARCH 13, 2025

Introducing business intelligence required a great deal of change management work, because from a data use that wasnt very sophisticated and organized, and very do-it-yourself, we moved to a consistent and verified data warehouse, he says. The technical work of Sicca is typical of ICT in a distributed multinational.

Digital Transformation 117

Digital Transformation Insurance Management Deep Learning 117