AWS Big Data

JANUARY 31, 2023

Data governance is increasingly top-of-mind for customers as they recognize data as one of their most important assets. Effective data governance enables better decision-making by improving data quality, reducing data management costs, and ensuring secure access to data for stakeholders.

Data Lake 83

Data Lake Data Governance Data Architecture Data Warehouse 83

erwin

DECEMBER 12, 2019

Here are some benefits of metadata management for data governance use cases: Better Data Quality: Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair. by up to 70 percent.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

CIO Business Intelligence

AUGUST 9, 2022

Database-centric: In larger organizations, where managing the flow of data is a full-time job, data engineers focus on analytics databases. Database-centric data engineers work with data warehouses across multiple databases and are responsible for developing table schemas. Data engineer job description.

Analytics 130

Analytics Data Science Statistics Unstructured Data 130

Jedox

OCTOBER 22, 2020

Today, though, the growing volume of data (currently measured in brontobytes = 10^ 27th power) and the advanced technologies available mean you can get much deeper insights much faster than you could in the past. In addition to using data to inform your future decisions, you can also use current data to make immediate decisions.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

erwin

DECEMBER 12, 2019

Here are some benefits of metadata management for data governance use cases: Better Data Quality: Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair. by up to 70 percent.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

Peter James Thomas

AUGUST 18, 2018

That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. Of course some architectures featured both paradigms as well. In Closing.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Alation

JULY 28, 2022

It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. However, CIOs declare that agility, innovation, security, adopting new capabilities, and time to value — never cost — are the top drivers for cloud data warehousing.

Data Warehouse 69

Data Warehouse Cost-Benefit Data Governance Data-driven 69

Cloudera

AUGUST 30, 2022

Legacy data sharing involves proliferating copies of data, creating data management, and security challenges. Data quality issues deter trust and hinder accurate analytics. Modern data architectures. Deploying modern data architectures. Forrester ). Forrester ).

Data Architecture 79

Data Architecture Data Lake Metadata Data Warehouse 79

Ontotext

FEBRUARY 26, 2024

They have massive amounts of data they’re collecting and storing in their relational databases, document stores, data lakes, and data warehouses. But until they connect the dots across their data, they will stay in this stage. This is where graph technologies come in. We call this the “bad data tax.”

Technology 52

Technology Cost-Benefit Data-driven Metadata 52

CIO Business Intelligence

NOVEMBER 29, 2022

A sea of complexity For years, data ecosystems have gotten more complex due to discrete (and not necessarily strategic) data-platform decisions aimed at addressing new projects, use cases, or initiatives. Layering technology on the overall data architecture introduces more complexity.

Data Architecture 110

Data Architecture Data Integration IoT Strategy 110

CIO Business Intelligence

MARCH 9, 2022

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. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure. Learn more at [link]. .

Data Lake 116

Data Lake Unstructured Data Data Warehouse Data Quality 116

BizAcuity

APRIL 14, 2022

Big Data technology in today’s world. Did you know that the big data and business analytics market is valued at $198.08 Or that the US economy loses up to $3 trillion per year due to poor data quality? quintillion bytes of data which means an average person generates over 1.5 Poor data quality.

Big Data 52

Big Data Data Analytics Management Unstructured Data 52

Peter James Thomas

MAY 3, 2019

This refers to a wide range of activities from Data Governance to Data Management to Data Quality improvement and indeed related concepts such as Master Data Management. Data Architecture / Infrastructure. When I first started focussing on the data arena, Data Warehouses were state of the art.

Strategy 55

Strategy Data Architecture Data Quality Data Strategy 55

Alation

JANUARY 25, 2022

Gartner is explicit: Data catalogs play a foundational role in the data fabric. And leaders are recognizing the value of a strong data foundation. Indeed, the foundation of your data architecture and strategy – and thus your business strategy – begins with choosing the best data catalog to support your business.

Metadata 52

Metadata IT Metrics Data-driven 52

CIO Business Intelligence

NOVEMBER 7, 2023

Centralized reporting boosts data value For more than a decade, pediatric health system Phoenix Children’s has operated a data warehouse containing more than 120 separate data systems, providing the ability to connect data from disparate systems.

Data Warehouse 142

Data Warehouse Digital Transformation Data-driven Reporting 142

Ontotext

AUGUST 4, 2023

Working across data islands leads to siloed thinking and the inability to implement critical business initiatives such as Customer, Product, or Asset 360. As data is generated, stored, and used across data centers, edge, and cloud providers, managing a distributed storage environment is complex with no map to guide technology professionals.

Metadata 52

Metadata Data-driven Data Architecture Data Quality 52

IBM Big Data Hub

AUGUST 4, 2023

Today, the way businesses use data is much more fluid; data literate employees use data across hundreds of apps, analyze data for better decision-making, and access data from numerous locations. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

AWS Big Data

FEBRUARY 27, 2024

The following are the key components of the Bluestone Data Platform: Data mesh architecture – Bluestone adopted a data mesh architecture, a paradigm that distributes data ownership across different business units. This enables data-driven decision-making across the organization.

Data-driven 95

Data-driven Data Lake Data Quality Data Governance 95

Ontotext

FEBRUARY 12, 2024

This is especially beneficial when teams need to increase data product velocity with trust and data quality, reduce communication costs, and help data solutions align with business objectives. In most enterprises, data is needed and produced by many business units but owned and trusted by no one.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

Alation

APRIL 19, 2022

Data modernization is the process of transferring data to modern cloud-based databases from outdated or siloed legacy databases, including structured and unstructured data. In that sense, data modernization is synonymous with cloud migration. Only then can you extract insights across fragmented data architecture.

Cost-Benefit 52

Cost-Benefit Data Governance Manufacturing Data Architecture 52

BI-Survey

DECEMBER 22, 2020

Which conceptual, architectural and technological approaches can support these efforts? Many declarations of intent regarding data, but serious investment lacking. In principle, everyone agrees that data is important, and its targeted use can make a decisive contribution to improved company results.

Data-driven 52

Data-driven Data Strategy Strategy Enterprise 52