AWS Big Data

MARCH 7, 2023

Sources Data can be loaded from multiple sources, such as systems of record, data generated from applications, operational data stores, enterprise-wide reference data and metadata, data from vendors and partners, machine-generated data, social sources, and web sources.

Analytics 72

Analytics Data Warehouse Data Lake Metadata 72

Cloudera

AUGUST 31, 2021

Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis. Consideration of both data & metadata in the migration.

Data Warehouse 85

Data Warehouse Cost-Benefit Metadata Data-driven 85

IBM Big Data Hub

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. Data quality Data quality is essentially the measure of data integrity.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

AWS Big Data

MAY 7, 2024

Figure 1: Flow of actions for self-service analytics around data assets stored in relational databases First, the data producer needs to capture and catalog the technical metadata of the data asset. The producer also needs to manage and publish the data asset so it’s discoverable throughout the organization.

Metadata 76

Metadata Data Lake Data Processing Data-driven 76

Peter James Thomas

AUGUST 18, 2018

One of the early promises of a Data Lake approach was that – once all relevant data had been ingested – this would be directly leveraged by Data Scientists to derive insight. Over time, it became clear that it would be useful to also have some merged / conformed and cleansed data structures in the Data Lake.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Peter James Thomas

AUGUST 29, 2018

My role encompasses being the business driver for the data platform that we are rolling out across the organisation and its success in terms of the data going onto the platform and the curation of that data in a governed state, depending on the consumer requirements. Data democratisation is enabled by the data platform.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61

Alation

JULY 19, 2022

As they attempt to put machine learning models into production, data science teams encounter many of the same hurdles that plagued data analytics teams in years past: Finding trusted, valuable data is time-consuming. Obstacles, such as user roles, permissions, and approval request prevent speedy data access.

Metadata 59

Metadata Data Quality Statistics Data Science 59

IBM Big Data Hub

JANUARY 17, 2023

These new technologies and approaches, along with the desire to reduce data duplication and complex ETL pipelines, have resulted in a new architectural data platform approach known as the data lakehouse – offering the flexibility of a data lake with the performance and structure of a data warehouse.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Cloudera

AUGUST 30, 2022

For state and local agencies, data silos create compounding problems: Inaccessible or hard-to-access data creates barriers to data-driven decision making. Legacy data sharing involves proliferating copies of data, creating data management, and security challenges. Towards Data Science ).

Data Architecture 82

Data Architecture Data Lake Metadata Data Warehouse 82

CIO Business Intelligence

MARCH 9, 2022

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. Each ETL step risks introducing failures or bugs that reduce data quality. .

Data Lake 117

Data Lake Unstructured Data Data Warehouse Data Quality 117

Alation

SEPTEMBER 23, 2021

What Is a Data Catalog? A data catalog is a centralized storage bank of metadata on information sources from across the enterprise, such as: Datasets. Business intelligence reports. The data catalog also stores metadata (data about data, like a conversation), which gives users context on how to use each asset.

Enterprise 52

Enterprise Metadata Data Governance Visualization 52

datapine

JANUARY 31, 2022

Using business intelligence and analytics effectively is the crucial difference between companies that succeed and companies that fail in the modern environment. Experience the power of Business Intelligence with our 14-days free trial! Why Is Business Intelligence So Important?

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Alation

DECEMBER 15, 2021

This includes where the organization stores, processes, and transmits it (details an organization must be ready to share with auditors, or increasingly, with individuals whose personal data has been captured and seek to have a say in how companies use it.). At the same time, it enhances data security and compliance programs.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

CIO Business Intelligence

AUGUST 11, 2023

In this model, the centralized hub — Enterprise D&A — will provide the platform, security, and best practices, along with tracking and governance of data products. The spokes — Dow’s businesses and functions — will perform much of their own analytics and data science.

Data Analytics 98

Data Analytics Analytics Data-driven Business Driver 98

Andrew White

MARCH 22, 2019

to weave together the governance and management of master data, application data, and less-widely shared data, and just enough enterprise metadata management. Your Future Requires You to Define Your Real Master Data. Don’t You Need to Understand Your Business Information Architecture?

Analytics 68

Analytics Metadata Data Governance Management 68

AWS Big Data

JUNE 14, 2023

We were frustrated by the limitations of our previous business intelligence (BI) solution, which was holding us back from our vision to accelerate data sharing, team collaboration, and security within Amazon. Reusable – The ultimate goal of FAIRS is to optimize the reuse of data.

Dashboards 77

Dashboards KPI Scorecard Metadata 77

Jet Global

MARCH 14, 2024

The solution offers data movement, data science, real-time analytics, and business intelligence within a single platform. Jet’s interface lets you handle data administration easily, without advanced coding skills. You don’t need technical skills to manage complex data workflows in the Fabric environment.

Analytics 52

Analytics Management Reporting Enterprise 52