Cloudera

JANUARY 28, 2022

Then there are the more extensive discussions – scrutiny of the overarching, data strategy questions related to privacy, security, data governance /access and regulatory oversight. These are not straightforward decisions, especially when data breaches always hit the top of the news headlines.

Cloud 120

Cloud Banking Data Governance Government 120

Cloudera

FEBRUARY 15, 2024

Risk management and governance: Regulators are increasingly focusing on risk governance, risk sustainability, and the detection, mitigation, tracking, and remediation of threat actors. Financial institutions must demonstrate robust risk accountability and governance, as well as maintain consumer protections.

Insurance 93

Insurance Banking Data Architecture Data Ingestion 93

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . ModelOp — Governs, monitors, and orchestrates models across the enterprise. Process Analytics.

Consulting 124

Consulting Machine Learning Data Science Data Pipeline 124

Cloudera

DECEMBER 11, 2020

The Data Security and Governance category, at the annual Data Impact Awards, has never been so important. The sudden rise in remote working, a huge influx in data as the world turned digital, not to mention the never-ending list of regulations businesses need to remain compliant with (how many acronyms can you name in full?

Data Lake 98

Data Lake Government Data Security Unstructured Data 98

Cloudera

OCTOBER 5, 2023

Your organization is not alone — many organizations struggle to move towards data as the cornerstone of their organization. Here are five challenges that you need to overcome to become a data leader: Bad data governance Your insights are only as good as your data. Data now becomes a product.

Data Governance 89

Data Governance Unstructured Data Government High Quality Data 89

Snowflake

JANUARY 25, 2024

It’s essential for organizations to leverage vast amounts of structured and unstructured data for effective generative AI (gen AI) solutions that deliver a clear return on investment. Data security and governance aren’t the only reasons leading organizations will take this approach.

Healthcare 94

Healthcare Hospitality Electronics Medical 94

Snowflake

SEPTEMBER 19, 2023

AI unlocks new data use cases. With the ability to handle unstructured data types and larger volumes of data, AI gives us the tools to tackle more complex, exciting problems. But now this enables a newer kind of insights from all this unstructured data that has been untapped so far.

Machine Learning 101

Machine Learning Unstructured Data Data Analytics Government 101

Precisely

MARCH 7, 2024

Yet experts warn that without proactive attention to data quality and data governance, AI projects could face considerable roadblocks. AI technology can ingest and synthesize large volumes of both structured and unstructured data very quickly, offering claims guidance that helps adjusters to better assess cases.

Insurance 72

Insurance Process Medical Data Governance 72

Monte Carlo

OCTOBER 31, 2024

Key Differences Between AI Data Engineers and Traditional Data Engineers While traditional data engineers and AI data engineers have similar responsibilities, they ultimately differ in where they focus their efforts. Challenges Faced by AI Data Engineers Just because “AI” involved doesn’t mean all the challenges go away!

Data Engineering 52

Data Engineering Data Engineer Engineering Unstructured Data 52

Snowflake

JULY 9, 2024

To better understand a customer’s current data reality we ask a series of questions: Do you have access to all of your internal data? Have you unlocked data from existing applications, systems or business unit silos? Have you transformed your unstructured data into structured, usable data?

Government 87

Government Education Data Governance Unstructured Data 87

Cloudera

JULY 29, 2022

It supports business objectives like increasing revenues, improving customer experience, and driving profitability by giving business units and users access to relevant data so they can quickly gain the insight they need. The proof is in the pudding.

Data Architecture 94

Data Architecture Architecture Unstructured Data Government 94

Knowledge Hut

DECEMBER 22, 2023

Statistics are used by data scientists to collect, assess, analyze, and derive conclusions from data, as well as to apply quantifiable mathematical models to relevant variables. Microsoft Excel An effective Excel spreadsheet will arrange unstructured data into a legible format, making it simpler to glean insights that can be used.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

Data Engineering Weekly

MARCH 3, 2024

link] AWS: Data governance in the age of generative AI The AWS Big Data Blog discusses the importance of data governance in the age of generative AI, emphasizing the need for robust data management strategies to ensure data quality, privacy, and security across structured and unstructured data sources.

Data Engineering 69

Data Engineering Data Engineer Pipeline-centric Engineering 69

Cloudera

AUGUST 21, 2024

The root of the problem comes down to trusted data. Pockets and siloes of disparate data can accumulate across an enterprise or legacy data warehouses may not be equipped to properly manage a sea of structured and unstructured data at scale.

Unstructured Data 70

Unstructured Data Business Intelligence Data Architecture Data Warehouse 70

The Modern Data Company

MAY 8, 2023

Potential downsides of data lakes include governance and integration challenges. Data lakes often lack robust data governance, leading to data quality, consistency, and security issues. Data Lakehouse: Bridging Data Worlds A data lakehouse combines the best features of data lakes and data warehouses.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

Potential downsides of data lakes include governance and integration challenges. Data lakes often lack robust data governance, leading to data quality, consistency, and security issues. Data Lakehouse: Bridging Data Worlds A data lakehouse combines the best features of data lakes and data warehouses.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

Potential downsides of data lakes include governance and integration challenges. Data lakes often lack robust data governance, leading to data quality, consistency, and security issues. Data Lakehouse: Bridging Data Worlds A data lakehouse combines the best features of data lakes and data warehouses.

Data Management 52

Data Management Management Data Lake Data Governance 52

Snowflake

NOVEMBER 29, 2023

While the former can be solved by tokenization strategies provided by external vendors, the latter mandates the need for patient-level data enrichment to be performed with sufficient guardrails to protect patient privacy, with an emphasis on auditability and lineage tracking.

Metadata 89

Metadata Healthcare Medical Data Storage 89

Cloudera

JUNE 22, 2023

It ensures compliance with regulatory requirements while shifting non-sensitive data and workloads to the cloud. Its built-in intelligence automates common data management and data integration tasks, improves the overall effectiveness of data governance, and permits a holistic view of data across the cloud and on-premises environments.

Data Cleanse 70

Data Cleanse Data Governance Unstructured Data Cloud Storage 70

Cloudera

NOVEMBER 18, 2021

This included using NiFi to automatically collect and centralize documents consisting of unstructured data and then leveraging advanced natural language processing to extract tacit knowledge and perform sentiment analysis on unstructured text and images from more than 20 million documents. Security & Governance Leadership.

Banking 82

Banking Data Lake Telecommunication Data 82

Databand.ai

JULY 19, 2023

Data lineage tools are not a new concept. However, their importance has grown significantly in recent years due to the increasing complexity of data architectures and the growing need for data governance and compliance. In this article: Why Are Data Lineage Tools Important?

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

Knowledge Hut

APRIL 23, 2024

They also facilitate historical analysis, as they store long-term data records that can be used for trend analysis, forecasting, and decision-making. Big Data In contrast, big data encompasses the vast amounts of both structured and unstructured data that organizations generate on a daily basis.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Snowflake

NOVEMBER 11, 2024

Snowflake is committed to doing just that by continually adding features to help our customers simplify how they architect their data infrastructure. At BUILD 2024, we announced several enhancements and innovations designed to help you build and manage your data architecture on your terms. Here’s a closer look.

Data Architecture 85

Data Architecture Architecture Data Lake Kafka 85

RandomTrees

NOVEMBER 25, 2024

The considerable amount of unstructured data required Random Trees to create AI models that ensure privacy and data handling. Overcoming Implementation Challenges The project faced some difficulties along the way.

Telecommunication 52

Telecommunication IT Unstructured Data Data Mining 52

Snowflake

SEPTEMBER 21, 2023

The data driving the provider’s application is stored and processed in the provider’s own Snowflake account. Beyond delivering powerful analytical experiences, providers differentiate their products by offering live, ready-to-query data to their customers through the Snowflake Data Cloud.

Google Cloud 91

Google Cloud Unstructured Data Technology Data 91

The Modern Data Company

MAY 10, 2023

Data governance and security: Evaluate the native security, data governance, and data quality management features. Because data lakes can have performance limitations for these use cases, a data warehouse may be a better fit. Data warehouses offer the optimized performance you need.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Cloudera

SEPTEMBER 15, 2022

Mark: While most discussions of modern data platforms focus on comparing the key components, it is important to understand how they all fit together. The collection of source data shown on your left is composed of both structured and unstructured data from the organization’s internal and external sources.

Data Lake 88

Data Lake Analytics Application Cloud Storage Architecture 88

Striim

SEPTEMBER 11, 2024

Data warehouses offer high performance and scalability, enabling organizations to manage large volumes of structured data efficiently. Data Lakes: Data lakes are designed to store structured, semi-structured, and unstructured data, providing a flexible and scalable solution.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

The Modern Data Company

MAY 10, 2023

Data governance and security: Evaluate the native security, data governance, and data quality management features. Because data lakes can have performance limitations for these use cases, a data warehouse may be a better fit. Data warehouses offer the optimized performance you need.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

Data governance and security: Evaluate the native security, data governance, and data quality management features. Because data lakes can have performance limitations for these use cases, a data warehouse may be a better fit. Data warehouses offer the optimized performance you need.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Monte Carlo

OCTOBER 10, 2024

Let’s dive into the responsibilities, skills, challenges, and potential career paths for an AI Data Quality Analyst today. Table of Contents What Does an AI Data Quality Analyst Do? Handling unstructured data Many AI models are fed large amounts of unstructured data, making data quality management complex.

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

AltexSoft

FEBRUARY 11, 2023

In the EU, the General Data Protection Regulation (GDPR) sets guidelines for collecting, storing, and processing personal information. This privacy law must be kept in mind when building data architecture. It defines metrics and best practices to ensure data quality as well as data privacy and security.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Precisely

MAY 13, 2024

The right data integration solution helps you streamline operations, enhance data quality, reduce costs, and make better data-driven decisions. It synthesizes all the metadata around your organization’s data assets and arranges the information into a simple, easy-to-understand format.

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

Monte Carlo

FEBRUARY 21, 2023

A data fabric isn’t a standalone technology—it’s a data management architecture that leverages an integrated data layer atop underlying data in order to empower business leaders with real-time analytics and data-driven insights. That’s a model worth looking at when it comes to data governance,” says Bob.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 21, 2023

A data fabric isn’t a standalone technology—it’s a data management architecture that leverages an integrated data layer atop underlying data in order to empower business leaders with real-time analytics and data-driven insights. That’s a model worth looking at when it comes to data governance,” says Bob.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

AltexSoft

SEPTEMBER 23, 2021

A data hub serves as a single point of access for all data consumers, whether it be an application, a data scientist, or a business user. So, it also allows for managing data for various tasks, providing centralized governance and data flow control capabilities. Data lake vs data hub.

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Towards Data Science

DECEMBER 4, 2023

We’ll build a data architecture to support our racing team starting from the three canonical layers : Data Lake, Data Warehouse, and Data Mart. Data Lake A data lake would serve as a repository for raw and unstructured data generated from various sources within the Formula 1 ecosystem: telemetry data from the cars (e.g.

Data Engineering 81

Data Engineering Data Engineer Engineering Data Lake 81