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.

Data Governance 105

Data Governance Metadata Risk Data Lake 105

Smart Data Collective

OCTOBER 28, 2020

Many healthcare organizations also retain data for future research into care improvements or related projects, in which case it’s critical to ensure that when you decommission a data system , you also export and appropriately store any associated metadata. Big Data, Big Costs.

Cost-Benefit 127

Cost-Benefit Risk Metadata Software 127

IBM Big Data Hub

MAY 9, 2023

This solution is designed to include everything needed to develop a consistent transparent model management process. The resulting automation drives scalability and accountability by capturing model development time and metadata, offering post-deployment model monitoring, and allowing for customized workflows.

Metadata 71

Metadata Risk Experimentation Dashboards 71

IBM Big Data Hub

APRIL 20, 2023

This level of visibility also helps ensure that changes made over time don’t introduce new risks into the organization, can make it easier for banks to stay within regulatory guidelines, and helps ensure banks can respond quickly to changing business needs.

Risk 68

Risk Risk Management Reporting Metadata 68

IBM Big Data Hub

DECEMBER 4, 2023

Its toolkit automates risk management, monitors models for bias and drift, captures model metadata and facilitates collaborative, organization-wide compliance. It helps accelerate responsible, transparent and explainable AI workflows.

Consulting 75

Consulting Risk Metadata Risk Management 75

erwin

DECEMBER 20, 2018

The driving factors behind data governance adoption vary. Whether implemented as preventative measures (risk management and regulation) or proactive endeavors (value creation and ROI), the benefits of a data governance initiative is becoming more apparent. Data Governance 2.0 for Financial Services.

Data Governance 40

Data Governance ROI Risk Management Metadata 40

IBM Big Data Hub

SEPTEMBER 20, 2022

Often data scientists aren’t thrilled with the prospect of generating all the documentation necessary to meet ethical and regulatory standards. This is where technology such as IBM FactSheets , can help by reducing the manual labor needed to capture metadata and other facts about a model across stages of the AI lifecycle.

Metadata 56

Metadata Machine Learning Data-driven Modeling 56

AWS Big Data

MARCH 27, 2023

Developers, data scientists, and analysts can work across databases, data warehouses, and data lakes to build reporting and dashboarding applications, perform real-time analytics, share and collaborate on data, and even build and train machine learning (ML) models with Redshift Serverless.

Data Warehouse 86

Data Warehouse Management Snapshot Data Lake 86

erwin

OCTOBER 24, 2019

Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata , or the data about the data. Metadata Is the Heart of Data Intelligence.

Metadata 104

Metadata Management Data-driven Data Architecture 104

Domino Data Lab

APRIL 3, 2019

It includes perspectives about current issues, themes, vendors, and products for data governance. My interest in data governance (DG) began with the recent industry surveys by O’Reilly Media about enterprise adoption of “ABC” (AI, Big Data, Cloud). We keep feeding the monster data. a second priority?at

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Cloudera

JANUARY 22, 2019

In conjunction with the evolving data ecosystem are demands by business for reliable, trustworthy, up-to-date data to enable real-time actionable insights. Big Data Fabric has emerged in response to modern data ecosystem challenges facing today’s enterprises. What is Big Data Fabric? Data access.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44

erwin

FEBRUARY 4, 2021

However, organizations still encounter a number of bottlenecks that may hold them back from fully realizing the value of their data in producing timely and relevant business insights. Impact analysis has equal importance to IT for automatically tracking changes and understanding how data from one system feeds other systems and reports.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

IBM Big Data Hub

FEBRUARY 8, 2024

” European Parliament News The EU AI Act in brief The primary focus of the EU AI Act is to strengthen regulatory compliance in the areas of risk management, data protection, quality management systems, transparency, human oversight, accuracy, robustness and cyber security.

Risk Management 73

Risk Management Risk Metrics Machine Learning 73

TDAN

SEPTEMBER 17, 2019

There have been a couple of different things converged in my media feeds that highlight a key issue that all data management professionals and leaders in information management need to be conscious of – Group Think. This manifests itself in at least two ways in the following examples I’ll use to illustrate my points.

Management 80

Management Risk Management Risk Data Governance 80

IBM Big Data Hub

FEBRUARY 8, 2023

Automate the capture of model metadata and increase predictive accuracy to identify how AI is used and where models need to be reworked. Risk management Automate model facts and workflows for compliance to business standards. identify, manage, monitory and report on risk and compliance at scale.

Risk Management 52

Risk Management Risk Modeling Metadata 52

IBM Big Data Hub

FEBRUARY 22, 2024

Providing interpretable AI model metadata (for example, as factsheets ) specifying accountable persons, performance benchmarks (compared to human), data and methods used, audit records (date and by whom), and audit purpose and results. Capture key metadata to render AI models transparent and keep track of model inventory.

Metadata 93

Metadata Measurement Risk Modeling 93

Smart Data Collective

AUGUST 14, 2022

Evaluate data types of value to the company and identify their relevance; evaluation helps store the data respectful to its sensitivity. Most DLP solutions offer tools to scan file metadata, catalog the results, analyze the files’ content, and estimate the associated risk with each data type.

Risk 123

Risk Cost-Benefit Software Reporting 123

Grooper

OCTOBER 23, 2019

Responsibility for managing your company’s data must be clearly defined and supported. For example, business growth, profits, cost and performance optimization, efficiency, compliance, and risk management. Edwards Deming said, “In God we trust, all others must bring data.”

Metrics 59

Metrics Measurement Strategy Optimization 59

AWS Big Data

AUGUST 14, 2023

Trunk (core data products) – Core data products are those that are central to the organization’s ability to function, and from which the majority of other data products are derived. Organizational governance for these data products typically favors availability and data accuracy over agility.

Management 80

Management Risk Measurement Optimization 80

Andrew White

JANUARY 11, 2021

What are you seeing as the differences between a Chief Analytics Officer and the Chief Data Officer? Value Management or monetization. Risk Management (most likely within context of governance). Product Management. Saul Judah is our main person focusing on D&A risk management. Governance.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52