AWS Big Data

NOVEMBER 10, 2023

This integration expands the possibilities for AWS analytics and machine learning (ML) solutions, making the data warehouse accessible to a broader range of applications. Your applications can seamlessly read from and write to your Amazon Redshift data warehouse while maintaining optimal performance and transactional consistency.

Data Processing 95

Data Processing Data Lake Data Warehouse Optimization 95

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

IBM Big Data Hub

APRIL 28, 2022

Problem : Traditionally, developing a solid backorder forecast model that takes every factor into consideration would take anywhere from weeks to months as sales data, inventory or lead-time data and supplier data would all reside in disparate data warehouses. How does a data fabric impact the bottom line?

Metadata 80

Metadata Data Warehouse Forecasting Predictive Modeling 80

DataRobot

JANUARY 20, 2022

— Collaborating via Snowflake Data Cloud and DataRobot AI Cloud Platform will enable multiple organizations to build a community movement where experimentation, innovation, and creativity flourish. Looking forward through data. Grasping the digital opportunity.

Data-driven 145

Data-driven Experimentation Modeling Predictive Modeling 145

Sisense

MARCH 27, 2019

With millions of streams and touch points, most businesses create gigabytes of data every minute, yet our rate of data comprehension cannot keep up. To get the most out of data, companies need to analyze it as soon as it is created—when it can provide the most immediate and relevant insights. Who Uses Real-Time BI?

Business Intelligence 74

Business Intelligence Data Warehouse Predictive Modeling Visualization 74

BizAcuity

JULY 13, 2021

In many cases, source data is captured in various databases and the need for data consolidation arises and typically it takes around 6-9 months to complete, and with a high budget in terms of provisioning for servers, either in cloud or on-premise, licenses for data warehouse platform, reporting system, ETL tools, etc.

Optimization 52

Optimization Marketing Analytics Data Warehouse 52

Cloudera

NOVEMBER 2, 2020

Users today are asking ever more from their data warehouse. As an example of this, in this post we look at Real Time Data Warehousing (RTDW), which is a category of use cases customers are building on Cloudera and which is becoming more and more common amongst our customers. What is Real Time Data Warehousing?

Data Warehouse 94

Data Warehouse Optimization Dashboards Interactive 94

IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 79

Data Science Data Analytics Prescriptive Analytics Analytics 79

Domino Data Lab

JANUARY 11, 2021

Arming data science teams with the access and capabilities needed to establish a two-way flow of information is one critical challenge many organizations face when it comes to unlocking value from their modeling efforts. Domino integrates with Snowflake to solve this challenge by providing a modern approach to data.

Recreation/Entertainment 103

Recreation/Entertainment Data Science Data Warehouse Modeling 103

Smarten

MAY 30, 2023

Integrate objects (Dashboards, Crosstab, Tabular, KPIs, Graphs, Reports, models, Clickless Analytics and more).’ It is also supported by advanced analytics components including natural language processing (NLP) search analytics, and assisted predictive modeling to enable the Citizen Data Scientist culture.

Slice and Dice 52

Slice and Dice Business Intelligence Cost-Benefit Dashboards 52

AWS Big Data

MARCH 7, 2023

Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.

Analytics 74

Analytics Data Warehouse Data Lake Metadata 74

Cloudera

APRIL 13, 2021

But while the company is united by purpose, there was a time when its teams were kept apart by a data platform that lacked the scalability and flexibility needed for collaboration and efficiency. Disparate data silos made real-time streaming analytics, data science, and predictive modeling nearly impossible.

Enterprise 76

Enterprise Cost-Benefit Data Warehouse Data Architecture 76

Smarten

NOVEMBER 26, 2023

TechTarget defines business intelligence this way: ‘Business intelligence (BI) is a technology-driven process for analyzing data and delivering actionable information that helps executives, managers and workers make informed business decisions.’ The primary difference between traditional and modern BI lies in flexibility and accessibility.

Marketing 40

Marketing Business Intelligence Key Performance Indicator Prescriptive Analytics 40

Smart Data Collective

SEPTEMBER 26, 2021

There’s not much value in holding on to raw data without putting it to good use, yet as the cost of storage continues to decrease, organizations find it useful to collect raw data for additional processing. The raw data can be fed into a database or data warehouse. If it’s not done right away, then later.

Metadata 98

Metadata Visualization Unstructured Data Data mining 98

AWS Big Data

NOVEMBER 14, 2023

However, in many organizations, data is typically spread across a number of different systems such as software as a service (SaaS) applications, operational databases, and data warehouses. Such data silos make it difficult to get unified views of the data in an organization and act in real time to derive the most value.

IoT 55

IoT Data-driven Data Lake Data Strategy 55

Cloudera

MAY 12, 2021

We welcome organizations that have built and deployed use cases for enterprise-scale machine learning and have industrialized AI to automate, secure, and optimize data-driven decision-making and/or applications to enter this category. HYBRID & MULTI-CLOUD INNOVATION. SECURITY AND GOVERNANCE LEADERSHIP. PEOPLE FIRST.

Digital Transformation 68

Digital Transformation Machine Learning Optimization Data Lake 68

Smarten

JUNE 5, 2019

CEO Patel, says, “As the Smarten product evolves, it is truly exciting to see the ways in which business users, data scientists, IT staff and business managers have embraced and adopted advanced analytics as part of the day-to-day and strategic business decision process.”

Analytics 40

Analytics Predictive Modeling Data Analytics Data Warehouse 40

IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 56

Machine Learning Data Science Statistics Deep Learning 56

CIO Business Intelligence

MAY 10, 2022

A customer data platform (CDP) is a prepackaged, unified customer database that pulls data from multiple sources to create customer profiles of structured data available to other marketing systems. They eliminate data silos, and, unlike a traditional data warehouse, CDPs don’t require technical expertise to set up or maintain.

Advertising 104

Advertising Interactive Marketing Data Integration 104

IBM Big Data Hub

OCTOBER 16, 2023

Reinforcement learning uses ML to train models to identify and respond to cyberattacks and detect intrusions. Banks and other financial institutions train ML models to recognize suspicious online transactions and other atypical transactions that require further investigation. Spotify uses ML models to generate its song recommendations.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

Jet Global

SEPTEMBER 1, 2023

Data-Driven Decision Making: Embedded predictive analytics empowers the development team to make informed decisions based on data insights. By integrating predictive models directly into the application, developers can provide real-time recommendations, forecasts, or insights to end-users.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 52

Birst BI

OCTOBER 30, 2018

The credit scores generated by the predictive model are then used to approve or deny credit cards or loans to customers. A well-designed credit scoring algorithm will properly predict both the low- and high-risk customers. Integrate the data sources of the various behavioral attributes into a functional data model.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

Cloudera

DECEMBER 9, 2020

As the center of excellence for data analytics, the EDO leads Globe Telecom’s data revolution. It changes the way the company makes decisions through data insights and helps to increase the value Globe Telecom is able to deliver to its customers.

Predictive Modeling 71

Predictive Modeling Data Warehouse Enterprise Data Strategy 71

AWS Big Data

JULY 28, 2023

Amazon Redshift is a petabyte-scale, enterprise-grade cloud data warehouse service delivering the best price-performance. Today, tens of thousands of customers run business-critical workloads on Amazon Redshift to cost-effectively and quickly analyze their data using standard SQL and existing business intelligence (BI) tools.

Data Lake 69

Data Lake Data Governance Data Warehouse Modeling 69

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. A data catalog sophisticated enough to support the other components.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

CDW Research Hub

JULY 17, 2019

For example, Sirius recently helped an organization improve their employee experience simply by analyzing badge scans and sensor data from lights and parking garages.

Predictive Modeling 44

Predictive Modeling Machine Learning Sales Modeling 44

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

Jet Global

JULY 17, 2023

Finally, you can use classification or regression models to predict missing values. Predicting Missing Values Using an Algorithm Another way to predict missing values is to create a simple regression model. The column to predict here is the Salary, using other columns in the dataset.

Predictive Analytics 52

Predictive Analytics Reporting Software Strategy 52

Smarten

DECEMBER 3, 2023

Perhaps you want to shift from a data warehouse to cloud storage. If you choose the right self service data analytics tools with the right self service analytics capabilities, you can avoid a cumbersome, long training schedule.

Analytics 40

Analytics Data Warehouse Metrics Interactive 40

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

Cloudera

DECEMBER 20, 2017

Analytic software and statistical tools help us build models to detect patterns and make predictions to extend our reach. Increasingly, there is enough data and complexity, that we can’t define a model capturing each input and output scenario up front. That’s where machine learning (ML) comes in.

Machine Learning 40

Machine Learning Reporting Modeling Statistics 40

O'Reilly on Data

SEPTEMBER 11, 2019

2 in frequency in proposal topics; a related term, “models,” is No. Terms that relate to data engineering, data management, and data analytics dominate the top tiers of proposal topics. An ML-related topic, “models,” was No. For example, even though ML and ML-related concepts —a related term, “ML models,” (No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Jet Global

MAY 1, 2023

These licensing terms are critical: Perpetual license vs subscription: Subscription is a pay-as-you-go model that provides flexibility as you evaluate a vendor. Pricing model: The pricing scale is dependent on several factors. Some cloud applications can even provide new benchmarks based on customer data.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52