AWS Big Data

JULY 29, 2024

In this blog post, we’ll discuss how the metadata layer of Apache Iceberg can be used to make data lakes more efficient. You will learn about an open-source solution that can collect important metrics from the Iceberg metadata layer. This ensures that each change is tracked and reversible, enhancing data governance and auditability.

Metadata 119

Metadata Snapshot Data Lake Metrics 119

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

O'Reilly on Data

JUNE 14, 2024

And Miso had already built an early LLM-based search engine using the open-source BERT model that delved into research papers—it could take a query in natural language and find a snippet of text in a document that answered that question with surprising reliability and smoothness.

Metadata 298

Metadata Publishing Modeling Data-driven 298

erwin

JUNE 26, 2020

Organizations with particularly deep data stores might need a data catalog with advanced capabilities, such as automated metadata harvesting to speed up the data preparation process. Three Types of Metadata in a Data Catalog. The metadata provides information about the asset that makes it easier to locate, understand and evaluate.

Metadata 132

Metadata Cost-Benefit Measurement Data-driven 132

O'Reilly on Data

MARCH 20, 2019

Like many others, I’ve known for some time that machine learning models themselves could pose security risks. An attacker could use an adversarial example attack to grant themselves a large loan or a low insurance premium or to avoid denial of parole based on a high criminal risk score. Newer types of fair and private models (e.g.,

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

erwin

SEPTEMBER 3, 2020

It documents your data assets from end to end for business understanding and clear data lineage with traceability. Data governance and EA also provide many of the same benefits of enterprise architecture or business process modeling projects: reducing risk, optimizing operations, and increasing the use of trusted data.

Data Governance 144

Data Governance Enterprise Risk Data Lake 144

erwin

OCTOBER 8, 2020

And you can’t risk false starts or delayed ROI that reduces the confidence of the business and taint this transformational initiative. But even with the “need for speed” to market, new applications must be modeled and documented for compliance, transparency and stakeholder literacy. GDPR, CCPA, HIPAA, SOX, PIC DSS).

Data Governance 145

Data Governance Metadata Testing Data Lake 145

erwin

APRIL 9, 2020

If we have not yet ingested and organized both at-rest and in-motion metadata from across our system landscape, then we may be at a disadvantage when it comes to business continuity. Documented Policies and Procedures. Therefore, we need to be aware of our potential risks and put plans in place to mitigate those risks.

Metadata 132

Metadata Risk Data Governance IT 132

Rocket-Powered Data Science

FEBRUARY 15, 2023

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? (2) Why should your organization be doing it and why should your people commit to it? (3) In short, you must be willing and able to answer the seven WWWWWH questions (Who?

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

CIO Business Intelligence

DECEMBER 18, 2024

This ability builds on the deep metadata context that Salesforce has across a variety of tasks. Some examples of such use cases, according to Evans, are answering questions on contracts or large documents, especially in the legal, insurance, and healthcare sectors.

Enterprise 98

Enterprise IT Sales Metadata 98

erwin

JANUARY 30, 2020

However, more than 50 percent say they have deployed metadata management, data analytics, and data quality solutions. erwin Named a Leader in Gartner 2019 Metadata Management Magic Quadrant. And close to 50 percent have deployed data catalogs and business glossaries. Top Five: Benefits of An Automation Framework for Data Governance.

Data Governance 145

Data Governance Metadata Cost-Benefit Digital Transformation 145

DataKitchen

FEBRUARY 2, 2023

This will drive a new consolidated set of tools the data team will leverage to help them govern, manage risk, and increase team productivity. Enterprises are more challenged than ever in their data sprawl , so reducing risk and lowering costs drive software spending decisions. What will exist at the end of 2025?

Metadata 130

Metadata Testing Data Science Risk 130

erwin

SEPTEMBER 17, 2020

For example, automatically importing mappings from developers’ Excel sheets, flat files, Access and ETL tools into a comprehensive mappings inventory, complete with auto generated and meaningful documentation of the mappings, is a powerful way to support overall data governance. Data quality is crucial to every organization.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. They must be accompanied by documentation to support compliance-based and operational auditing requirements.

Data Governance 142

Data Governance Management Metadata Data Quality 142

erwin

SEPTEMBER 10, 2020

Not Documenting End-to-End Data Lineage Is Risky Busines – Understanding your data’s origins is key to successful data governance. Data lineage helps answer questions about the origin of data in key performance indicator (KPI) reports, including: How are the report tables and columns defined in the metadata?

Data Governance 131

Data Governance Key Performance Indicator Metadata Digital Transformation 131

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

CIO Business Intelligence

SEPTEMBER 12, 2024

Enterprises must reimagine their data and document management to meet the increasing regulatory challenges emerging as part of the digitization era. The cost of compliance These challenges are already leading to higher costs and greater operational risk for enterprises. According to figures from the Cato Institute, U.S

Data-driven 106

Data-driven Management Cost-Benefit Risk 106

Cloudera

SEPTEMBER 28, 2020

In order to minimize risk and downtime, the upgrades were performed in that order and the learning from each upgrade was applied to the next upgraded environment. Major upgrades of production environments can require hours to days of downtime, so it is critical that upgrades are efficient and planned to minimize risks. or Ubuntu 18.04.

Testing 132

Testing Metadata Risk Data Science 132

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

Octopai

SEPTEMBER 14, 2020

Modern data processing depends on metadata management to power enhanced business intelligence. Metadata is of course the information about the data, and the process of managing it is mysterious to those not trained in advanced BI. In this article, you will learn: What does metadata management do? What is metadata management?

Metadata 52

Metadata Management Data Processing Machine Learning 52

erwin

JULY 12, 2019

Modern, strategic data governance , which involves both IT and the business, enables organizations to plan and document how they will discover and understand their data within context, track its physical existence and lineage, and maximize its security, quality and value. Five Steps to GDPR/CCPA Compliance. Strengthen data security.

Data Governance 110

Data Governance Management Metadata Risk Management 110

Smart Data Collective

JULY 15, 2021

By using metadata (or short descriptions), data catalogs help companies gather, organize, retrieve, and manage information. A data catalog will usually have a search tool, a separate data discovery tool, a glossary, and a metadata registry. Metadata registries organize various data sets according to categories and fields.

Metadata 133

Metadata IT Data-driven Data Quality 133

IBM Big Data Hub

OCTOBER 16, 2023

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.

Risk 70

Risk Modeling Management Metadata 70

erwin

APRIL 29, 2020

An understanding of the data’s origins and history helps answer questions about the origin of data in a Key Performance Indicator (KPI) reports, including: How the report tables and columns are defined in the metadata? Business terms and data policies should be implemented through standardized and documented business rules.

Key Performance Indicator 111

Key Performance Indicator Metadata Data Governance Data Quality 111

DataRobot Blog

MAY 10, 2022

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States. To reference SR 11-7: .

Risk 64

Risk Modeling Machine Learning Data Quality 64

Alation

JUNE 28, 2022

This is where metadata, or the data about data, comes into play. Your metadata management framework provides the underlying structure that makes your data accessible and manageable. What is a Metadata Management Framework? Your framework should include the following: Global metadata: applies to all information.

Metadata 52

Metadata Management Data Governance Machine Learning 52

erwin

JANUARY 17, 2020

Data models provide visualization, create additional metadata and standardize data design across the enterprise. Thanks to organizations like Amazon, Netflix and Uber, businesses have changed how they leverage their data and are transforming their business models to innovate – or risk becoming obsolete. SQL or NoSQL?

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

erwin

AUGUST 28, 2020

Put simply, DG is about maximizing the potential of an organization’s data and minimizing the risk. Organizations with a effectively governed data enjoy: Better alignment with data regulations: Get a more holistic understanding of your data and any associated risks, plus improve data privacy and security through better data cataloging.

Data Governance 145

Data Governance IT Cost-Benefit Metadata 145

erwin

OCTOBER 31, 2019

While sometimes at rest in databases, data lakes and data warehouses; a large percentage is federated and integrated across the enterprise, introducing governance, manageability and risk issues that must be managed. So being prepared means you can minimize your risk exposure and the damage to your reputation. No Hocus Pocus.

Data Governance 105

Data Governance Metadata Risk Data Lake 105

erwin

JULY 17, 2019

EA and BP modeling squeeze risk out of the digital transformation process by helping organizations really understand their businesses as they are today. Organizations need a real-time, accurate picture of the metadata landscape to: Discover data – Identify and interrogate metadata from various data management silos.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

erwin

NOVEMBER 6, 2020

Metadata Harvesting and Ingestion : Automatically harvest, transform and feed metadata from virtually any source to any target to activate it within the erwin Data Catalog (erwin DC). Data Cataloging: Catalog and sync metadata with data management and governance artifacts according to business requirements in real time.

Data Governance 97

Data Governance Metadata Data-driven Visualization 97

datapine

SEPTEMBER 29, 2022

It involves: Reviewing data in detail Comparing and contrasting the data to its own metadata Running statistical models Data quality reports. Data processes that depended upon the previously defective data will likely need to be re-initiated, especially if their functioning was at risk or compromised by the defected data.

Data Quality 241

Data Quality Metrics Data-driven Management 241

IBM Big Data Hub

OCTOBER 4, 2022

Today, AI presents an enormous opportunity to turn data into insights and actions, to amplify human capabilities, decrease risk and increase ROI by achieving break through innovations. Manual processes that introduce risk and make it hard to scale. Challenges around managing risk. It is an imperative.

Metadata 97

Metadata Risk Management Risk Experimentation 97

erwin

AUGUST 7, 2019

With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects. At each stage, the discovered pieces of lineage are documented.

Metadata 90

Metadata Data Governance Data Quality Data Warehouse 90

O'Reilly on Data

MARCH 23, 2023

What is it, how does it work, what can it do, and what are the risks of using it? But Transformers have some other important advantages: Transformers don’t require training data to be labeled; that is, you don’t need metadata that specifies what each sentence in the training data means. What Are the Risks?

IT 348

IT Modeling Testing Risk 348

erwin

APRIL 2, 2021

With more companies increasingly migrating their data to the cloud to ensure availability and scalability, the risks associated with data management and protection also are growing. Lack of a solid data governance foundation increases the risk of data-security incidents. Is it sensitive data or are there any risks associated with it?

Data Governance 106

Data Governance Cost-Benefit Risk Metadata 106

erwin

JANUARY 24, 2019

In data governance terms, an automation framework refers to a metadata-driven universal code generator that works hand in hand with enterprise data mapping for: Pre-ETL enterprise data mapping. Governing metadata. The 100-percent metadata-driven approach is critical to creating reliable and consistent CATs.

Data Governance 71

Data Governance Metadata Data-driven Cost-Benefit 71