Cloudera

NOVEMBER 1, 2022

These systems can pose operational risks, including rising costs and the inability to meet mission requirements. . In the private sector, excluding highly regulated industries like financial services, the migration to the public cloud was the answer to most IT modernization woes, especially those around data, analytics, and storage.

Cost-Benefit 92

Cost-Benefit Data Architecture Risk IoT 92

IBM Big Data Hub

OCTOBER 30, 2023

IBM, a pioneer in data analytics and AI, offers watsonx.data, among other technologies, that makes possible to seamlessly access and ingest massive sets of structured and unstructured data. AWS’s secure and scalable environment ensures data integrity while providing the computational power needed for advanced analytics.

Insurance 78

Insurance Data Warehouse Data-driven Unstructured Data 78

CDW Research Hub

JULY 27, 2021

Data volumes are growing exponentially, and traditional, on-premises data warehouses are constrained, overly complex, and costly to scale. In this way, the Cloud Data Warehouse Accelerator enables a seamless transition to Snowflake. Mitigate risks with a seamless cloud migration.

Data Warehouse 52

Data Warehouse Data Lake Data Architecture Consulting 52

Laminar Security

DECEMBER 13, 2023

Close to 70% of respondents in an ISC report indicated that they believe their organization lacks requisite cybersecurity staff to handle cloud data risk effectively. Learn in this article how Laminar harnesses AI for data discovery and classification and reduces public cloud data risks.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

AWS Big Data

FEBRUARY 29, 2024

First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses. The user permissions are evaluated using AWS Lake Formation to filter the relevant data.

Data Governance 96

Data Governance Unstructured Data Metadata Data Lake 96

Cloudera

OCTOBER 11, 2022

Let this sink in a while – AI at scale isn’t magic, it’s data. What these data leaders are saying is that if you can’t do data at scale , you can’t possibly do AI at scale. Risk increases. Data and AI projects cost more and take longer. This leads to the obvious question – how do you do data at scale ?

Snapshot 98

Snapshot Data Science Digital Transformation Metadata 98

AWS Big Data

MAY 28, 2024

The ingested data gets transformed and analyzed in near real time using Amazon Managed Service for Apache Flink. Stream data can further be enriched using lookup data hosted in a data warehouse such as Amazon Redshift. Shwetha Radhakrishnan is a Solutions Architect for AWS with a focus in Data Analytics.

IoT 92

IoT Analytics Dashboards Data-driven 92

Cloudera

AUGUST 2, 2018

It goes unnoticed but cyber threats are routinely hunted down by correlating hundreds of attributes from disparate data sources – even as data types and formats evolve. Leading insurers are underwriting policies with lower risks. We call it ‘Modern Data Warehousing’. The key ingredient is something we call H-3.

Data Warehouse 53

Data Warehouse Insurance Experimentation Software 53

Sisense

JANUARY 13, 2020

From a practical perspective, the computerization and automation of manufacturing hugely increase the data that companies acquire. And cloud data warehouses or data lakes give companies the capability to store these vast quantities of data. Improving the supply chain and mitigating its risk.

Manufacturing 52

Manufacturing Data Lake Big Data Data Warehouse 52

CIO Business Intelligence

APRIL 6, 2022

Large enterprises that have traditionally been risk-averse are now adopting new approaches such as investing in design and innovation studios, partnering with startups for niche capabilities, and gaining early access to technology through strategic partnerships with hyper-scalers.

Experimentation 102

Experimentation Data Lake Uncertainty Enterprise 102

CIO Business Intelligence

SEPTEMBER 6, 2022

As a result, Pimblett now runs the organization’s data warehouse, analytics, and business intelligence. Establishing a clear and unified approach to data. Pimblett hasn’t yet catalogued all of Very’s data, however. We’re picking off the highest potential value and highest risk areas,” he says.

IT 98

IT Forecasting Data Lake Enterprise 98

datapine

JANUARY 24, 2021

This genie (who we’ll call Data Dan) embodies the idea of a perfect data analytics platform through his magic powers. Now, with Data Dan, you only get to ask him three questions. The questions to ask when analyzing data will be the framework, the lens, that allows you to focus on specific aspects of your business reality.

IT 317

IT Statistics KPI Data-driven 317

IBM Big Data Hub

JUNE 8, 2022

Implementing the right data strategy spurs innovation and outstanding business outcomes by recognizing data as a critical asset that provides insights for better and more informed decision-making. By taking advantage of data, enterprises can shape business decisions, minimize risk for stakeholders, and gain competitive advantage.

Data-driven 56

Data-driven Enterprise Data Governance Data Lake 56

CIO Business Intelligence

NOVEMBER 22, 2022

“Unique insights derived from an organization’s data constitute a competitive advantage that’s inherent to their business and not easily copied by competitors,” she observes. Failing to meet these needs means getting left behind and missing out on the many opportunities made possible by advances in data analytics.”

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

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. Read more about last years Data Impact Award winners. HYBRID & MULTI-CLOUD INNOVATION.

Digital Transformation 71

Digital Transformation Machine Learning Optimization Data Lake 71

Smart Data Collective

MARCH 21, 2022

More case studies are added every day and give a clear hint – data analytics are all set to change, again! . Data Management before the ‘Mesh’. In the early days, organizations used a central data warehouse to drive their data analytics. It’s a tough road but worth the effort. .

Management 66

Management Data Lake Uncertainty Data Warehouse 66

AWS Big Data

MARCH 27, 2024

Achieving this will also improve general public health through better and more timely interventions, identify health risks through predictive analytics, and accelerate the research and development process. You can send data from your streaming source to this resource for ingesting the data into a Redshift data warehouse.

Data Analytics 92

Data Analytics Analytics Data Warehouse Data Lake 92

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 54

Machine Learning Data Science Statistics Deep Learning 54

AWS Big Data

APRIL 19, 2023

Many customers run big data workloads such as extract, transform, and load (ETL) on Apache Hive to create a data warehouse on Hadoop. Instead, we can use automation to speed up the process of migration and reduce heavy lifting tasks, costs, and risks. He is passionate about big data and data analytics.

Metadata 81

Metadata Testing Data Lake Consulting 81

Smart Data Collective

JUNE 7, 2019

A classic application of stream processing is the financial institutions where they can see fluctuations in the stock market in real time and balance again the portfolios based on computed and up-to-minute risk assessments. From this application, below are some benefits of stream processing: Provides a path for more data analysis.

Big Data 81

Big Data Marketing Cost-Benefit Unstructured Data 81

IBM Big Data Hub

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

IBM Big Data Hub

DECEMBER 16, 2022

It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

AWS Big Data

JULY 25, 2023

Today, in order to accelerate and scale data analytics, companies are looking for an approach to minimize infrastructure management and predict computing needs for different types of workloads, including spikes and ad hoc analytics. Prerequisites To complete the integration, you need a Redshift Serverless data warehouse.

Data Analytics 52

Data Analytics Data Warehouse Analytics Data Processing 52

datapine

MARCH 31, 2022

This could involve anything from learning SQL to buying some textbooks on data warehouses. BI Data Scientist. A data scientist has a similar role as the BI analyst, however, they do different things. Being numbers and data-driven: There are many expectations when it comes to working with BI and data analytics.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

datapine

AUGUST 29, 2022

The saying “knowledge is power” has never been more relevant, thanks to the widespread commercial use of big data and data analytics. The rate at which data is generated has increased exponentially in recent years. Essential Big Data And Data Analytics Insights. million searches per day and 1.2

Big Data 263

Big Data Data Analytics Analytics Data mining 263

AWS Big Data

DECEMBER 8, 2023

More and more of FanDuel’s community of analysts and business users looked for comprehensive data solutions that centralized the data across the various arms of their business. Their individual, product-specific, and often on-premises data warehouses soon became obsolete.

Data Warehouse 69

Data Warehouse Cost-Benefit KPI Risk 69

Cloudera

MARCH 2, 2018

The private sector already very successfully uses data analytics and machine learning not only to realise efficiency gains but also – even more importantly – to create completely new services and business models. Identify those most at risk or most affected by a problem more accurately by using predictive analytics.

Data-driven 43

Data-driven Prescriptive Analytics Descriptive Analytics Machine Learning 43

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 Optimization 84

AWS Big Data

MAY 16, 2024

As an AWS Partner, CARTO offers a software solution on the curated digital catalog AWS Marketplace that seamlessly integrates distinctive capabilities for spatial visualization, analysis, and app development directly within the AWS data warehouse environment. To learn more, visit CARTO.

Data Warehouse 108

Data Warehouse Visualization Cost-Benefit Optimization 108

BizAcuity

APRIL 14, 2022

Its effective data analytics that allows personalization in marketing & sales, identifying new opportunities, making important decisions and being sustainable for the long term. Competitive Advantages to using Big Data Analytics. Big Data Storage Optimization. Enterprise Big Data Strategy.

Big Data 52

Big Data Data Analytics Management Unstructured Data 52

AWS Big Data

JANUARY 24, 2023

In this post, you will learn how to build a serverless analytics application using Amazon Redshift Data API and Amazon API Gateway WebSocket and REST APIs. The Data API simplifies access to Amazon Redshift because you don’t need to configure drivers and manage database connections.

Analytics 59

Analytics Data-driven Management Reporting 59

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

Corinium

APRIL 25, 2019

Ahead of the Chief Data Analytics Officers & Influencers, Insurance event we caught up with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity to discuss how the industry is evolving. Life insurance needs accurate data on consumer health, age and other metrics of risk. ML can keep up.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

CIO Business Intelligence

MARCH 9, 2022

Applying artificial intelligence (AI) to data analytics for deeper, better insights and automation is a growing enterprise IT priority. But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI.

Data Lake 117

Data Lake Unstructured Data Data Warehouse Data Quality 117

AWS Big Data

FEBRUARY 21, 2023

Data warehouses play a vital role in healthcare decision-making and serve as a repository of historical data. A healthcare data warehouse can be a single source of truth for clinical quality control systems. What is a dimensional data model? What is a dimensional data model?

Data Warehouse 66

Data Warehouse Data Lake Cost-Benefit Metadata 66

IBM Big Data Hub

MAY 9, 2023

With watsonx.data , businesses can quickly connect to data, get trusted insights and reduce data warehouse costs. A data store built on open lakehouse architecture, it runs both on premises and across multi-cloud environments. Savings may vary depending on configurations, workloads and vendors.

Data Warehouse 107

Data Warehouse Machine Learning Cost-Benefit Metadata 107

IBM Big Data Hub

DECEMBER 7, 2023

Today, OLAP database systems have become comprehensive and integrated data analytics platforms, addressing the diverse needs of modern businesses. They are seamlessly integrated with cloud-based data warehouses, facilitating the collection, storage and analysis of data from various sources.

OLAP 58

OLAP Slice and Dice Cost-Benefit Data Warehouse 58