Ontotext

JUNE 10, 2021

Not Every Graph is a Knowledge Graph: Schemas and Semantic Metadata Matter. To be able to automate these operations and maintain sufficient data quality, enterprises have started implementing the so-called data fabrics , that employ diverse metadata sourced from different systems. Such examples are provenance (e.g.

Metadata 119

Metadata Cost-Benefit OLAP Modeling 119

AWS Big Data

AUGUST 15, 2024

Data quality is crucial in data pipelines because it directly impacts the validity of the business insights derived from the data. Today, many organizations use AWS Glue Data Quality to define and enforce data quality rules on their data at rest and in transit.

Data Quality 109

Data Quality Visualization Metadata Key Performance Indicator 109

AWS Big Data

DECEMBER 4, 2024

Data teams struggle to find a unified approach that enables effortless discovery, understanding, and assurance of data quality and security across various sources. Collaboration is seamless, with straightforward publishing and subscribing workflows, fostering a more connected and efficient work environment.

Data Analytics 102

Data Analytics Analytics Data Lake Data Quality 102

AWS Big Data

DECEMBER 4, 2024

These formats, exemplified by Apache Iceberg, Apache Hudi, and Delta Lake, addresses persistent challenges in traditional data lake structures by offering an advanced combination of flexibility, performance, and governance capabilities. These are useful for flexible data lifecycle management.

Snapshot 83

Snapshot Metadata Data Lake Optimization 83

erwin

MARCH 30, 2020

To marry the epidemiological data to the population data it will require a tremendous amount of data intelligence about the: Source of the data; Currency of the data; Quality of the data; and. Unraveling Data Complexities with Metadata Management. Data lineage to support impact analysis.

Metadata 122

Metadata IT Data Governance Data Quality 122

David Menninger's Analyst Perspectives

OCTOBER 8, 2024

As I recently noted , the term “data intelligence” has been used by multiple providers across analytics and data for several years and is becoming more widespread as software providers respond to the need to provide enterprises with a holistic view of data production and consumption.

Data Quality 130

Data Quality Data Governance Enterprise Visualization 130

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. The status and statistics of the CDC load are published into CloudWatch.

Data Integration 112

Data Integration Data Lake Statistics Data-driven 112

DataKitchen

OCTOBER 11, 2021

Figure 2: Example data pipeline with DataOps automation. In this project, I automated data extraction from SFTP, the public websites, and the email attachments. The automated orchestration published the data to an AWS S3 Data Lake. Monitoring Job Metadata. Adding Tests to Reduce Stress.

Testing 154

Testing Metadata Dashboards Statistics 154

Jim Harris

MAY 5, 2017

Spelling, pronunciation, and examples of usage are included in the dictionary definition of a word, which is a good example of one of the many uses of metadata, namely to provide a definition, description, and context for data. In practice, I haven’t encountered a metadata dictionary that could deliver on that promise.

Metadata 61

Metadata Publishing Management IT 61

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. Public API. See the official public APIs.

Metadata 56

Metadata Machine Learning Data Quality Statistics 56

AWS Big Data

SEPTEMBER 11, 2024

This also includes building an industry standard integrated data repository as a single source of truth, operational reporting through real time metrics, data quality monitoring, 24/7 helpdesk, and revenue forecasting through financial projections and supply availability projections.

Data Architecture 109

Data Architecture Optimization Data Warehouse Metadata 109

erwin

JANUARY 23, 2025

Data intelligence software is continuously evolving to enable organizations to efficiently and effectively advance new data initiatives. With a variety of providers and offerings addressing data intelligence and governance needs, it can be easy to feel overwhelmed in selecting the right solution for your enterprise.

Metadata 52

Metadata Data Quality Data Governance Software 52

Octopai

APRIL 19, 2021

Aptly named, metadata management is the process in which BI and Analytics teams manage metadata, which is the data that describes other data. In other words, data is the context and metadata is the content. Without metadata, BI teams are unable to understand the data’s full story.

Metadata 59

Metadata Management Business Intelligence Data Governance 59

AWS Big Data

JULY 25, 2024

Instead of a central data platform team with a data warehouse or data lake serving as the clearinghouse of all data across the company, a data mesh architecture encourages distributed ownership of data by data producers who publish and curate their data as products, which can then be discovered, requested, and used by data consumers.

Data Lake 113

Data Lake Metadata Sales Publishing 113

erwin

JULY 12, 2019

It also helps enterprises put these strategic capabilities into action by: Understanding their business, technology and data architectures and their inter-relationships, aligning them with their goals and defining the people, processes and technologies required to achieve compliance. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

AWS Big Data

MAY 7, 2024

This post explains how you can extend the governance capabilities of Amazon DataZone to data assets hosted in relational databases based on MySQL, PostgreSQL, Oracle or SQL Server engines. Second, the data producer needs to consolidate the data asset’s metadata in the business catalog and enrich it with business metadata.

Metadata 105

Metadata Data Lake Data Processing Data-driven 105

O'Reilly on Data

MARCH 31, 2020

You might establish a baseline by replicating collaborative filtering models published by teams that built recommenders for MovieLens, Netflix, and Amazon. It may even be faster to launch this new recommender system, because the Disney data team has access to published research describing what worked for other teams.

Management 363

Management Machine Learning Experimentation Metrics 363

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. It is crucial in data governance and data management.

Dashboards 123

Dashboards Publishing Data-driven Cost-Benefit 123

Octopai

JANUARY 19, 2020

BCBS 239 is a document published by that committee entitled, Principles for Effective Risk Data Aggregation and Risk Reporting. The document, first published in 2013, outlines best practices for global and domestic banks to identify, manage, and report risks, including credit, market, liquidity, and operational risks. .

Metadata 89

Metadata Risk Management Business Intelligence Data Governance 89

IBM Big Data Hub

MARCH 25, 2022

Automated data enrichment : To create the knowledge catalog, you need automated data stewardship services. These services include the ability to auto-discover and classify data, to detect sensitive information, to analyze data quality, to link business terms to technical metadata and to publish data to the knowledge catalog.

Metadata 104

Metadata Data Quality Data Governance Data Integration 104

Cloudera

MARCH 3, 2022

The medical insurance company wasn’t hacked, but its customers’ data was compromised through a third-party vendor’s employee. In the 2020 O’Reilly Data Quality survey only 20% of respondents say their organizations publish information about data provenance or data lineage internally. From Bad to Worse.

Data Governance 100

Data Governance Enterprise Data Quality Metadata 100

AWS Big Data

DECEMBER 19, 2024

Data has become an invaluable asset for businesses, offering critical insights to drive strategic decision-making and operational optimization. The business end-users were given a tool to discover data assets produced within the mesh and seamlessly self-serve on their data sharing needs.

Data Governance 52

Data Governance Publishing Data-driven Metadata 52

AWS Big Data

JANUARY 18, 2024

Easily and securely prepare, share, and query data – This session shows how you can use Lake Formation and the AWS Glue Data Catalog to share data without copying, transform and prepare data without coding, and query data. This enhancement simplifies many use cases to avoid metadata duplication.

Data Lake 116

Data Lake Metadata Data Governance Statistics 116

erwin

MAY 27, 2020

What, then, should users look for in a data modeling product to support their governance/intelligence requirements in the data-driven enterprise? Nine Steps to Data Modeling. Provide metadata and schema visualization regardless of where data is stored.

Modeling 98

Modeling Metadata Data Governance Visualization 98

Ontotext

JULY 29, 2021

The second one is the Linked Open Data (LOD): a cloud of interlinked structured datasets published without centralized control across thousands of servers. There are more than 80 million pages with semantic, machine interpretable metadata , according to the Schema.org standard. Take this restaurant, for example.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

Smart Data Collective

OCTOBER 13, 2021

Data virtualization is ideal in any situation where the is necessary: Information coming from diverse data sources. Multi-channel publishing of data services. How does Data Virtualization manage data quality requirements? Real-time information. Agile requirements and fast deployment times.

Visualization 145

Visualization Cost-Benefit Big Data Prescriptive Analytics 145

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. Did you just have a spectacular new idea for a data analytics product?

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

IBM Big Data Hub

APRIL 27, 2022

Gartner defines a data fabric as “a design concept that serves as an integrated layer of data and connecting processes. The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale.

Management 79

Management Metadata Data Architecture Data Lake 79

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 97

Analytics Data Warehouse Data Lake Metadata 97

Cloudera

SEPTEMBER 9, 2022

As a data analyst or data scientist, we would all love to be able to do all these things, and much more. This is the promise of the modern data lakehouse architecture. Schema evolution: With fast-moving data and real-time data ingestion, we need new ways to keep up with data quality, consistency, accuracy, and overall integrity.

Metadata 102

Metadata Machine Learning Unstructured Data Data Lake 102

AWS Big Data

AUGUST 15, 2024

Business units can simply share data and collaborate by publishing and subscribing to the data assets. The Central IT team (Spoke N) subscribes the data from individual business units and consumes this data using Redshift Spectrum. Similarly, individual business units produce their own domain-specific data.

Data Lake 120

Data Lake Data Warehouse Data Governance Publishing 120

Ontotext

JANUARY 10, 2024

Limiting growth by (data integration) complexity Most operational IT systems in an enterprise have been developed to serve a single business function and they use the simplest possible model for this. In both cases, semantic metadata is the glue that turns knowledge graphs into hubs of data, metadata, and content.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

erwin

APRIL 16, 2024

In this blog, we’ll delve into the critical role of governance and data modeling tools in supporting a seamless data mesh implementation and explore how erwin tools can be used in that role. erwin also provides data governance, metadata management and data lineage software called erwin Data Intelligence by Quest.

Metadata 52

Metadata Data Quality Data Governance Data Processing 52

AWS Big Data

DECEMBER 9, 2024

Given the importance of data in the world today, organizations face the dual challenges of managing large-scale, continuously incoming data while vetting its quality and reliability. AWS Glue is a serverless data integration service that you can use to effectively monitor and manage data quality through AWS Glue Data Quality.

Data Quality 124

Data Quality Publishing Snapshot Data Lake 124

Ontotext

JULY 15, 2024

It also adds flexibility in accommodating new kinds of data, including metadata about existing data points that lets users infer new relationships and other facts about the data in the graph. Schemas are an example of how the right metadata can add value to the data it describes.

Contextual Data 52

Contextual Data Metadata Data Quality Publishing 52

DataRobot Blog

MAY 10, 2022

It has been over a decade since the Federal Reserve Board (FRB) and the Office of the Comptroller of the Currency (OCC) published its seminal guidance focused on Model Risk Management ( SR 11-7 & OCC Bulletin 2011-12 , respectively). To reference SR 11-7: .

Risk 64

Risk Modeling Machine Learning Data Quality 64

Alation

JUNE 21, 2022

Analysts didn’t just want to catalog data sources, they wanted to include dashboards, reports, and visualizations. Why start with a data source and build a visualization, if you can just find a visualization that already exists, complete with metadata about it? Data engineers want to catalog data pipelines.

Metadata 52

Metadata Data Quality Visualization Data Lake 52