IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. It encompasses risk management and regulatory compliance and guides how AI is managed within an organization. ” Are foundation models trustworthy?

Risk 70

Risk Modeling Management Metadata 70

IBM Big Data Hub

OCTOBER 4, 2022

Success in delivering scalable enterprise AI necessitates the use of AI tools and processes that are specifically made for building, deploying, monitoring and retraining models. Challenges around managing risk. This includes capturing of the metadata, tracking provenance and documenting the model lifecycle.

Metadata 97

Metadata Risk Risk Management Experimentation 97

Alation

OCTOBER 26, 2021

Because of the criticality of the data they deal with, we think that finance teams should lead the enterprise adoption of data and analytics solutions. And while some might see finance as the most conservative department in an enterprise, we believe that they can become innovators, driving how their business consumes and uses data.

Finance 52

Finance Data Transformation Enterprise Metrics 52

CIO Business Intelligence

JULY 8, 2022

TOGAF is a longstanding, popular, open-source enterprise architecture framework that is widely used by large businesses, government agencies, non-government public organizations, and defense agencies. Offered by The Open Group, TOGAF advises enterprises on how best to implement, deploy, manage, and maintain enterprise architecture.

Risk Management 89

Risk Management Risk Enterprise Metadata 89

erwin

OCTOBER 31, 2019

Data security incidents may be the result of not having a true data governance foundation that makes it possible to understand the context of data – what assets exist and where, the relationship between them and enterprise systems and processes, and how and by what authorized parties data is used. Enterprise architecture.

Data Governance 105

Data Governance Metadata Risk Data Lake 105

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. Providing metadata and value-based analysis: Discovery and classification of sensitive data based on metadata and data value patterns and algorithms.

Data Governance 106

Data Governance Cost-Benefit Risk Metadata 106

Octopai

JANUARY 19, 2020

Here’s a sampling: – Principle 1 covers data governance, including “a firm’s policies on data confidentiality, integrity, and availability, as well as risk-management policies.”. – Principles 7-11 include risk reporting, including the comprehensiveness, timeliness, usefulness, and accuracy of risk management reports. .

Metadata 61

Metadata Risk Management Business Intelligence Data Governance 61

Ontotext

JULY 6, 2023

In today’s fast changing environment, enterprises that have transitioned from being focused on applications to becoming data-driven gain a significant competitive edge. internal metadata, industry ontologies, etc.) The solution also reduces incident response times, optimizes processes and streamlines asset management.

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

IBM Big Data Hub

MAY 9, 2023

Success in delivering scalable enterprise AI necessitates the use of tools and processes that are specifically made for building, deploying, monitoring and retraining AI models. This solution is designed to include everything needed to develop a consistent transparent model management process.

Metadata 72

Metadata Risk Experimentation Dashboards 72

IBM Big Data Hub

DECEMBER 4, 2023

Yet while 79% of executives say AI ethics is important to their enterprise-wide AI approach , less than 25% have operationalized ethics governance principles. Its toolkit automates risk management, monitors models for bias and drift, captures model metadata and facilitates collaborative, organization-wide compliance.

Consulting 74

Consulting Risk Metadata Risk Management 74

erwin

APRIL 29, 2020

These tools range from enterprise service bus (ESB) products, data integration tools; extract, transform and load (ETL) tools, procedural code, application program interfaces (API)s, file transfer protocol (FTP) processes, and even business intelligence (BI) reports that further aggregate and transform data. Who are the data owners?

Metadata 111

Metadata Key Performance Indicator Data Governance Data Quality 111

TDAN

JANUARY 19, 2021

In October 2020, the Office of the Comptroller of the Currency (OCC) announced a $400 million civil monetary penalty against Citibank for deficiencies in enterprise-wide risk management, compliance risk management, data governance, and internal controls.

Data Governance 80

Data Governance Risk Management Risk Management 80

erwin

SEPTEMBER 17, 2020

Critically, it makes it easier to get a clear view of how information is created and flows into, across and outside an enterprise. erwin Data Intelligence (erwin DI) helps bind business terms to technical data assets with a complete data lineage of scanned metadata assets.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

Ontotext

APRIL 21, 2021

Ontotext clients have used enterprise knowledge graphs to ensure inventory control is done in an intelligent and automated way. The problem was the left hand had no way of knowing the systemic issues around data governance, risk management and compliance framework. Many of these organisations have grown through acquisitions.

Metadata 59

Metadata Finance Cost-Benefit Data Quality 59

Octopai

APRIL 8, 2020

While there are many factors that led to this event, one critical dynamic was the inadequacy of the data architectures supporting banks and their risk management systems. Inaccurate Data Management Leads to Financial Collapse. Data Lineage Provides Further Benefits for Enterprise Organizations.

Finance 52

Finance Cost-Benefit Metadata Data Architecture 52

IBM Big Data Hub

SEPTEMBER 20, 2022

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. Your model risk management team, IT operations team and line-of-business employees also need appropriate access.

Metadata 56

Metadata Machine Learning Data-driven Modeling 56

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. Conclusion.

Risk 64

Risk Modeling Machine Learning Data Quality 64

erwin

DECEMBER 20, 2018

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. Its value can be hard to demonstrate to those who don’t work directly with data and metadata on a daily basis.

Data Governance 40

Data Governance ROI Risk Management Metadata 40

Octopai

JULY 12, 2020

Like many BI teams, FCSA’s struggles began with the massive amount of data that existed in their enterprise data warehouse. Automated Data Lineage & Discovery Provides Enterprise-Wide Benefits. With Octopai’s suite of metadata management tools in place, the BI team had a central location for data lineage.

Data Warehouse 58

Data Warehouse Data Quality Metadata Risk Management 58

erwin

OCTOBER 24, 2019

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. And to truly understand it , you need to be able to create and sustain an enterprise-wide view of and easy access to underlying metadata.

Metadata 104

Metadata Management Data-driven Data Architecture 104

Alation

JUNE 14, 2021

Do we know the business outcomes tied to data risk management? To support data security, an effective data catalog should have features, like a business glossary, wiki-like articles, and metadata management. Intelligent systems powered by machine learning are necessary for overcoming the challenges of data management.

Data Governance 97

Data Governance Recreation/Entertainment Data Lake Digital Transformation 97

Ontotext

MAY 5, 2023

They provide practical examples of the requirements an enterprise might have as well as the shortcomings of the traditional warehousing approaches. The following slide summarizes the benefits and the unique solution features that knowledge graphs can contribute to the business needs of an enterprise.

Data Collection 64

Data Collection Risk Data-driven Interactive 64

Cloudera

JANUARY 22, 2019

Big Data Fabric has emerged in response to modern data ecosystem challenges facing today’s enterprises. It enables processing, management, analysis, and storage of virtually any amount of data from a multitude of sources, as well as access to these data by applications and tools employing a variety of interfaces. Source: Cloudera.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44

Domino Data Lab

APRIL 3, 2019

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). Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. for DG adoption in the enterprise.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

AWS Big Data

AUGUST 14, 2023

DPPM does not exist in a vacuum—by its nature, DPPM is closely related to and interdependent with enterprise architecture practices like business capability management and project portfolio management. DPPM itself may therefore also be considered, in part, an enterprise architecture practice.

Management 79

Management Risk Measurement Optimization 79

Grooper

OCTOBER 23, 2019

What to expect in this article: How enterprise information management is the surest path for making data a key asset for business growth and innovation. In our industry, we call this Enterprise Information Management (EIM). And how willing are key people to move away from current data management practices and tools?”.

Metrics 59

Metrics Measurement Strategy Optimization 59

Octopai

FEBRUARY 21, 2022

4 critical elements to enterprise data migration success. But since lucky stars are generally frowned upon as a risk management strategy, we highly recommend you plan out your cloud migration process. 1) Plan it out. If you migrate without a plan, and it actually goes well, you should thank your lucky stars.

Recreation/Entertainment 78

Recreation/Entertainment Cost-Benefit ROI Reporting 78

erwin

OCTOBER 3, 2019

Enterprise architecture (EA) benefits modern organizations in many ways. It provides a holistic, top down view of structure and systems, making it invaluable in managing the complexities of data-driven business. Once considered solely a function of IT, enterprise architecture has historically operated from an ivory tower.

Enterprise 111

Enterprise Cost-Benefit Data-driven Risk 111

erwin

FEBRUARY 4, 2021

While acknowledging that data governance is about more than risk management and regulatory compliance may indicate that companies are more confident in their data, the data governance practice is nonetheless growing in complexity because of more: Data to handle, much of it unstructured. Sources, like IoT. Points of integration.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

Andrew White

JANUARY 11, 2021

What is your vision for D&A for small and medium enterprises? We have specific research for midsize and small enterprises. See 3 Questions That Midsize Enterprises Should Ask About Data and Analytics and have an inquiry with Alan Duncan. CDO Success Factors: Culture Hacks to Create a Data-Driven Enterprise.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

Alation

NOVEMBER 30, 2021

This means data protection and risk mitigation must be promoted and consolidated with other enterprise risk management processes. The first step is recognizing the direct relationship between data governance and risk. All three risk functions should be aligned. Manage by Policy and Contractual Agreements.

Data Governance 52

Data Governance Risk Management Risk Enterprise 52

IBM Big Data Hub

FEBRUARY 22, 2024

Further, according to the IBM Institute of Business Value , 79% of executives say AI ethics is important to their enterprise-wide AI approach, yet less than 25% have operationalized common principles of AI ethics. IBM and its partners can provide AI solutions with comprehensive, auditable content grounding imperative to high-risk use cases.

Metadata 92

Metadata Measurement Risk Modeling 92

Alation

FEBRUARY 8, 2022

As data collection has surged, and demands for data have grown in the enterprise, one single team can no longer meet the data demands of every department. Data mesh supporters argue that this centralized model is no longer tenable in the expanding data universe of the enterprise. Physical products make up the fabric of our lives.

Data Governance 52

Data Governance Data-driven Metadata Enterprise 52

erwin

APRIL 4, 2019

But despite this fact, enterprises often face push back when implementing a new data governance initiative or trying to mature an existing one. In most cases, a new data governance framework requires people – those in IT and across the business, including risk management and information security – to change how they work.

Data Governance 75

Data Governance Data Quality Risk Management Data-driven 75

Smart Data Collective

AUGUST 14, 2022

Enterprises can store Intellectual Property data in unstructured or structured forms. 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 124

Risk Cost-Benefit Software Reporting 124

Alation

DECEMBER 8, 2022

At the risk of introducing yet another data governance definition, here’s how Forrester defines the term: A suite of software and services that help you create, manage, and assess the corporate policies, protocols, and measurements for data acquisition, access, and leverage. An Evaluation of Leading Use Cases.

Data Governance 52

Data Governance Data-driven Strategy Reporting 52