AWS Big Data

DECEMBER 4, 2024

Domain ownership recognizes that the teams generating the data have the deepest understanding of it and are therefore best suited to manage, govern, and share it effectively. This principle makes sure data accountability remains close to the source, fostering higher data quality and relevance.

Metadata 93

Metadata Data Governance Data Quality Data-driven 93

O'Reilly on Data

JUNE 19, 2019

In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. Quality depends not just on code, but also on data, tuning, regular updates, and retraining.

Modeling 257

Modeling Machine Learning Testing Metrics 257

O'Reilly on Data

MARCH 31, 2020

You might have millions of short videos , with user ratings and limited metadata about the creators or content. Job postings have a much shorter relevant lifetime than movies, so content-based features and metadata about the company, skills, and education requirements will be more important in this case.

Management 363

Management Machine Learning Experimentation Metrics 363

AWS Big Data

OCTOBER 9, 2024

Some customers build custom in-house data parity frameworks to validate data during migration. Others use open source data quality products for data parity use cases. This takes away important person hours from the actual migration effort into building and maintaining a data parity framework.

Data Quality 95

Data Quality Data Lake Data Warehouse Metrics 95

AWS Big Data

JANUARY 15, 2025

An extract, transform, and load (ETL) process using AWS Glue is triggered once a day to extract the required data and transform it into the required format and quality, following the data product principle of data mesh architectures. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog.

IoT 100

IoT Machine Learning Metadata Data-driven 100

DataKitchen

SEPTEMBER 21, 2023

These layers help teams delineate different stages of data processing, storage, and access, offering a structured approach to data management. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

DataKitchen

OCTOBER 11, 2021

Based on business rules, additional data quality tests check the dimensional model after the ETL job completes. While implementing a DataOps solution, we make sure that the pipeline has enough automated tests to ensure data quality and reduce the fear of failure. Monitoring Job Metadata.

Testing 152

Testing Metadata Dashboards Statistics 152

Alation

OCTOBER 18, 2022

Alation is pleased to be named a dbt Metrics Partner and to announce the start of a partnership with dbt, which will bring dbt data into the Alation data catalog. In the modern data stack, dbt is a key tool to make data ready for analysis. Data Transformation in the Modern Data Stack.

Metadata 52

Metadata Metrics Recreation/Entertainment Data Quality 52

Alation

MAY 24, 2022

In a sea of questionable data, how do you know what to trust? Data quality tells you the answer. It signals what data is trustworthy, reliable, and safe to use. It empowers engineers to oversee data pipelines that deliver trusted data to the wider organization. Today, as part of its 2022.2

Data Quality 52

Data Quality Metadata Reporting Metrics 52

IBM Big Data Hub

JULY 28, 2022

Metadata enrichment is about scaling the onboarding of new data into a governed data landscape by taking data and applying the appropriate business terms, data classes and quality assessments so it can be discovered, governed and utilized effectively. Scalability and elasticity. Public API.

Metadata 56

Metadata Machine Learning Data Quality Statistics 56

CIO Business Intelligence

NOVEMBER 22, 2022

Data fabric is an architecture that enables the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems. The fabric, especially at the active metadata level, is important, Saibene notes.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

DataKitchen

JUNE 24, 2021

GE formed its Digital League to create a data culture. One of the keys for our success was really focusing that effort on what our key business initiatives were and what sorts of metrics mattered most to our customers. Chapin also mentioned that measuring cycle time and benchmarking metrics upfront was absolutely critical. “It

Metrics 211

Metrics ROI Measurement Cost-Benefit 211

Cloudera

MARCH 3, 2022

In 2017, Anthem reported a data breach that exposed thousands of its Medicare members. The medical insurance company wasn’t hacked, but its customers’ data was compromised through a third-party vendor’s employee. 86% of Experian survey respondents’, for instance, are prioritizing moving their data to the cloud in 2022.

Data Governance 100

Data Governance Enterprise Data Quality Metadata 100

AWS Big Data

FEBRUARY 29, 2024

Implement data privacy policies. Implement data quality by data type and source. Let’s look at some of the key changes in the data pipelines namely, data cataloging, data quality, and vector embedding security in more detail. Link structured and unstructured datasets.

Data Governance 109

Data Governance Unstructured Data Metadata Data Lake 109

erwin

SEPTEMBER 2, 2024

Added data quality capability ready for an AI era Data quality has never been more important than as we head into this next AI-focused era. erwin Data Quality is the data quality heart of erwin Data Intelligence. erwin Data Quality is the data quality heart of erwin Data Intelligence.

Data Quality 52

Data Quality Data Processing Measurement Metadata 52

Andrew White

JANUARY 12, 2021

Predicts 2021: Data and Analytics Strategies to Govern, Scale and Transform Digital Business : By 2024, 30% of organizations will invest in data and analytics governance platforms, thus increasing the business impact of trusted insights and new efficiencies.

Analytics 144

Analytics Metadata Enterprise Data-driven 144

AWS Big Data

DECEMBER 4, 2024

It’s the preferred choice when customers need more control and customization over the data integration process or require complex transformations. This flexibility makes Glue ETL suitable for scenarios where data must be transformed or enriched before analysis.

Data Integration 97

Data Integration Data Lake Statistics Data-driven 97

AWS Big Data

SEPTEMBER 11, 2024

The application supports custom workflows to allow demand and supply planning teams to collaborate, plan, source, and fulfill customer orders, then track fulfillment metrics via persona-based operational and management reports and dashboards. This metadata file is later used to read source file names during processing into the staging layer.

Data Architecture 96

Data Architecture Optimization Data Warehouse Metadata 96

AWS Big Data

MAY 1, 2024

Solution overview OneData defines three personas: Publisher – This role includes the organizational and management team of systems that serve as data sources. Responsibilities include: Load raw data from the data source system at the appropriate frequency. Provide and keep up to date with technical metadata for loaded data.

Dashboards 122

Dashboards Publishing Data-driven Cost-Benefit 122

CIO Business Intelligence

APRIL 29, 2022

Despite soundings on this from leading thinkers such as Andrew Ng , the AI community remains largely oblivious to the important data management capabilities, practices, and – importantly – the tools that ensure the success of AI development and deployment. Further, data management activities don’t end once the AI model has been developed.

Data Governance 145

Data Governance IT Data Lake Risk 145

CIO Business Intelligence

DECEMBER 21, 2022

Defined as an enabler of frictionless access of data sharing in a distributed data environment, data fabric aims to help companies access, integrate, and manage their data no matter where that data is stored using semantic knowledge graphs, active metadata management, and embedded machine learning.

IT 131

IT Business Intelligence Sales Key Performance Indicator 131

Octopai

OCTOBER 26, 2022

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. SPC tests can do the same thing for the data flowing through your pipelines.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

Alation

OCTOBER 5, 2021

These divergences of focus can lead to consumers feeling bogged down by overly complicated processes or leadership teams being unable to see initiative investments reap the desired rewards of their predictive business success metrics. (1). Incomplete data. Data governance and AI. Lack of commitment.

Data Governance 96

Data Governance Strategy Data Quality Metrics 96

datapine

JANUARY 31, 2022

As Dan Jeavons Data Science Manager at Shell stated: “what we try to do is to think about minimal viable products that are going to have a significant business impact immediately and use that to inform the KPIs that really matter to the business”. A great way to illustrate the operational benefits of business intelligence.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

AWS Big Data

JULY 20, 2023

Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack. Moreover, running advanced analytics and ML on disparate data sources proved challenging.

Data Lake 93

Data Lake Analytics Snapshot Data Quality 93

Cloudera

MARCH 22, 2023

Cloudera Data Platform (CDP) is no different: it’s a hybrid data platform that meets organizations’ needs to get to grips with complex data anywhere, turning it into actionable insight quickly and easily. There are many logs and metrics, and they are all over the place.

Metrics 88

Metrics Data Governance Cost-Benefit Dashboards 88

Cloudera

JULY 20, 2021

Invest in maturing and improving your enterprise business metrics and metadata repositories, a multitiered data architecture, continuously improving data quality, and managing data acquisitions. Then back this up by embedding compliance and security protocols throughout the insights generation cycle.

Manufacturing 106

Manufacturing Forecasting IoT Insurance 106

erwin

MARCH 8, 2019

A number of best practices and technology solutions were used to establish the data required for managing the registration and classification of data feeds: The underlying metadata is harvested followed by an initial quality check. Then the metadata is classified against a semantic model held in a business glossary.

Data Governance 65

Data Governance Metadata Data Warehouse Data Quality 65

AWS Big Data

NOVEMBER 6, 2023

The following graph describes a simple data quality check pipeline using setup and teardown tasks. Airflow will cache variables and connections locally so that they can be accessed faster during DAG parsing, without having to fetch them from the secrets backend, environments variables, or metadata database.

Metrics 116

Metrics Metadata Snapshot Management 116

Alation

AUGUST 26, 2021

What Is Data Intelligence? Data intelligence is a system to deliver trustworthy, reliable data. It includes intelligence about data, or metadata. IDC coined the term, stating, “data intelligence helps organizations answer six fundamental questions about data.” Yet finding data is just the beginning.

Metadata 64

Metadata Data Governance Dashboards Software 64

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 78

Analytics Data Warehouse Data Lake Metadata 78

Alation

JULY 6, 2021

Modern data governance relies on automation, which reduces costs. Automated tools make data governance processes very cost-effective. Machine learning plays a key role, as it can increase the speed and accuracy of metadata capture and categorization. This empowers leaders to see and refine human processes around data.

Data Governance 52

Data Governance Metadata Data Quality Risk 52

Cloudera

JUNE 29, 2023

Use predictive analytics and ML to formalize key intraday liquidity metrics and monitor liquidity positions in real time. Deliver real-time analytic dashboards, suitable for different stakeholders, that integrate data from payment systems, nostro accounts , internal transactions, and other sources. Enhance counterparty risk assessment.

Data Architecture 85

Data Architecture Risk Management Risk Management 85

Alation

JULY 19, 2022

A data catalog can assist directly with every step, but model development. And even then, information from the data catalog can be transferred to a model connector , allowing data scientists to benefit from curated metadata within those platforms. How Data Catalogs Help Data Scientists Ask Better Questions.

Metadata 59

Metadata Data Quality Statistics Data Science 59

AWS Big Data

MARCH 26, 2024

In 2022, AWS commissioned a study conducted by the American Productivity and Quality Center (APQC) to quantify the Business Value of Customer 360. The following figure shows some of the metrics derived from the study. Then, you transform this data into a concise format. Organizations using C360 achieved 43.9%

Data Strategy 78

Data Strategy Strategy Data Warehouse Prescriptive Analytics 78

Alation

NOVEMBER 11, 2021

Modern data catalogs are far more than a metadata repository or your grandfather’s data dictionary. They continually analyze data and metadata to provide insight that enables data governance at scale. Data Quality Metrics. Cataloging & Classification.

Data Governance 52

Data Governance Data Quality Metrics Metadata 52

IBM Big Data Hub

JUNE 8, 2022

However, a foundational step in evolving into a data-driven organization requires trusted, readily available, and easily accessible data for users within the organization; thus, an effective data governance program is key. Here are a few common data management challenges: Regulatory compliance on data use.

Data-driven 56

Data-driven Enterprise Data Governance Data Lake 56