Alation

OCTOBER 5, 2021

What is data governance and how do you measure success? Data governance is a system for answering core questions about data. It begins with establishing key parameters: What is data, who can use it, how can they use it, and why? Why is your data governance strategy failing?

Data Governance 96

Data Governance Strategy Data Quality Metrics 96

IBM Big Data Hub

AUGUST 23, 2023

Because of this, when we look to manage and govern the deployment of AI models, we must first focus on governing the data that the AI models are trained on. This data governance requires us to understand the origin, sensitivity, and lifecycle of all the data that we use. Some of these risks include: 1.

Data Governance 52

Data Governance Enterprise Modeling Risk 52

Octopai

JUNE 26, 2020

In order to figure out why the numbers in the two reports didn’t match, Steve needed to understand everything about the data that made up those reports – when the report was created, who created it, any changes made to it, which system it was created in, etc. Enterprise data governance. Metadata in data governance.

Metadata 84

Metadata Data Governance Enterprise Reporting 84

IBM Big Data Hub

JANUARY 25, 2023

In our last blog , we introduced Data Governance: what it is and why it is so important. In this blog, we will explore the challenges that organizations face as they start their governance journey. Organizations have long struggled with data management and understanding data in a complex and ever-growing data landscape.

Data Governance 52

Data Governance Data-driven Strategy Management 52

erwin

JANUARY 24, 2019

They need their data mappings to fall under governance and audit controls, with instant access to dynamic impact analysis and lineage. With an automation framework, data professionals can meet these needs at a fraction of the cost of the traditional manual way. Benefits of an Automation Framework for Data Governance.

Data Governance 71

Data Governance Metadata Data-driven Cost-Benefit 71

AWS Big Data

APRIL 29, 2024

The construction of big data applications based on open source software has become increasingly uncomplicated since the advent of projects like Data on EKS , an open source project from AWS to provide blueprints for building data and machine learning (ML) applications on Amazon Elastic Kubernetes Service (Amazon EKS).

Management 96

Management Big Data Metadata Data Warehouse 96

erwin

MARCH 8, 2019

GDPR) and to ensure peak business performance, organizations often bring consultants on board to help take stock of their data assets. This sort of data governance “stock check” is important but can be arduous without the right approach and technology. That’s where data governance comes in ….

Data Governance 65

Data Governance Metadata Data Warehouse Data Quality 65

TDAN

APRIL 6, 2021

There is an ever-increasing awareness of concerns about data privacy, corporate data breaches, increasing demands for regulatory compliance. There are also emerging concerns about the ways that big data analytics potentially influence and bias automated decision-making.

Risk Management 64

Risk Management Risk Management Big Data 64

Cloudera

JUNE 6, 2024

Recently, Cloudera, alongside OCBC, were named winners in the“ Best Big Data and Analytics Infrastructure Implementation ” category at The Asian Banker’s Financial Technology Innovation Awards 2024. Lastly, data security is paramount, especially in the finance industry.

Contextual Data 75

Contextual Data Data Lake Data-driven Risk 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. Defining Data Governance. Data Governance 2.0

Data Governance 40

Data Governance ROI Risk Management Metadata 40

erwin

AUGUST 20, 2020

Through effective EA, organizations are better able to identify risks and opportunities, address redundancies and process gaps, and create roadmaps to bridge the gap between the current and desired future state of an organization. Reduced risks and costs. Data Governance. Data Security & Risk Management.

Enterprise 140

Enterprise Cost-Benefit Risk Data Governance 140

Laminar Security

DECEMBER 13, 2023

Close to 70% of respondents in an ISC report indicated that they believe their organization lacks requisite cybersecurity staff to handle cloud data risk effectively. Learn in this article how Laminar harnesses AI for data discovery and classification and reduces public cloud data risks.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

Octopai

JUNE 22, 2023

In the era of big data, organizations are grappling with the challenge of effectively managing and leveraging vast amounts of data. Two prominent approaches have emerged: self-service data management and centralized data management. This approach offers greater control and reduces the risk of data inconsistencies.

Management 52

Management Data-driven Data Quality Data Governance 52

IBM Big Data Hub

APRIL 8, 2024

Gartner has estimated that 80% of organizations fail to scale digital businesses because of outdated governance processes. Data is an asset, but to provide value, it must be organized, standardized and governed.

Data-driven 55

Data-driven Management Data Governance Strategy 55

IBM Big Data Hub

APRIL 24, 2024

Deploying privacy protections: The app uses encryption to protect data from cybercriminals and other prying eyes. Even if the data is stolen in a cyberattack , hackers can’t use it. These access controls reduce the chances that the data is used for unauthorized or illegal purposes.

Risk 78

Risk Measurement Data Governance Insurance 78

IBM Big Data Hub

DECEMBER 14, 2023

To combat these ever-growing risks, the concept of cyber resiliency has gained significant importance. Our approach to cyber resiliency Risk assessment and strategy : IBM emphasizes the importance of conducting a thorough risk assessment to identify vulnerabilities and potential threats.

Risk 78

Risk Measurement Business Objectives Optimization 78

AWS Big Data

JANUARY 31, 2023

Data governance is the collection of policies, processes, and systems that organizations use to ensure the quality and appropriate handling of their data throughout its lifecycle for the purpose of generating business value.

Data Lake 82

Data Lake Data Governance Data Architecture Data Warehouse 82

Smart Data Collective

DECEMBER 21, 2022

Big data technology has helped businesses make more informed decisions. A growing number of companies are developing sophisticated business intelligence models, which wouldn’t be possible without intricate data storage infrastructures. One of the biggest issues pertains to data quality.

Business Analytics 110

Business Analytics Analytics Data Quality Big Data 110

Cloudera

AUGUST 3, 2021

Advanced analytics empower risk reduction . Advanced analytics and enterprise data are empowering several overarching initiatives in supply chain risk reduction – improved visibility and transparency into all aspects of the supply chain balanced with data governance and security. .

Analytics 111

Analytics Digital Transformation Forecasting Risk 111

Cloudera

AUGUST 12, 2021

By using Cloudera’s big data platform to harness IoT data in real-time to drive predictive maintenance and improve operational efficiency, the company has realized about US$25 million annually in new profit resulting from better efficiency of working sites. . Risk Management. Conclusion.

Data-driven 111

Data-driven Manufacturing Insurance Risk Management 111

IBM Big Data Hub

APRIL 20, 2023

When consumers lose trust in a bank’s ability to manage risk, the system stops working. Banks and their employees place trust in their risk models to help ensure the bank maintains liquidity even in the worst of times. We’ve seen what follows—financial crises, bailouts, destruction of capital, and losses of jobs.

Risk 68

Risk Risk Management Reporting Metadata 68

erwin

JANUARY 17, 2020

In the modern context, data modeling is a function of data governance. While data modeling has always been the best way to understand complex data sources and automate design standards, modern data modeling goes well beyond these domains to accelerate and ensure the overall success of data governance in any organization.

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

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

IBM Big Data Hub

JUNE 10, 2024

Cloud-based XaaS solutions provide scalability, flexibility and access to a wide range of AI tools and services, while on-premises XaaS offerings enable greater control over data governance, compliance and security. Embracing a culture of experimentation helps businesses drive innovation while minimizing financial risk.

Experimentation 63

Experimentation Optimization Risk Management Modeling 63

IBM Big Data Hub

APRIL 10, 2023

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

Strategy 86

Strategy Data Architecture Cost-Benefit Reporting 86

IBM Big Data Hub

MAY 28, 2024

A good DLM process can help organize and structure critical data, particularly when organizations rely on diverse types of data storage. It can also help them reduce vulnerabilities and ensure data is efficiently managed, compliant with regulations, and not at risk of misuse or loss.

Strategy 82

Strategy Measurement Risk Cost-Benefit 82

IBM Big Data Hub

JUNE 29, 2023

By integrating financial planning with strategic and operational planning, organizations can evaluate financial profitability, identify potential gaps or risks, and make necessary adjustments to achieve financial targets. These tools enable the extraction, transformation, and loading (ETL) of data from various sources.

Key Performance Indicator 76

Key Performance Indicator Forecasting Sales Data Integration 76

erwin

APRIL 27, 2022

Best Data Governance Solution (erwin Data Intelligence). Best Data Modeling Solution (erwin Data Modeler). Best Data Security Solution (Quest ApexSQL). Best Data Governance Solution (erwin Data Intelligence). Best Data Governance Solution (erwin Data Intelligence).

Data Governance 52

Data Governance Digital Transformation Metadata Data Warehouse 52

IBM Big Data Hub

JUNE 8, 2022

By taking advantage of data, enterprises can shape business decisions, minimize risk for stakeholders, and gain competitive advantage. Ensuring data quality and access within an organization, while establishing and maintaining proper governance processes, is a major struggle for many organizations.

Data-driven 56

Data-driven Enterprise Data Governance Data Lake 56

Alation

MAY 19, 2022

Gartner Data & Analytics Summit 2022: Keynote Highlights. A Gartner survey found that 57% of Boards of Directors have increased their risk appetites, and data & analytics are fueling more risky (and potentially rewarding) projects. Leaders agree: Data needs to drive business results. Leverage small data.

Metadata 52

Metadata Data Analytics Analytics Data Governance 52

erwin

APRIL 11, 2019

Fortunately, whenever the time comes, the first point of call will always be data governance, so organizations can prepare. Effective compliance with new data protection regulations requires a robust understanding of the “what, where and who” in terms of data and the stakeholders with access to it (i.e., employees).

Insurance 100

Insurance Data-driven Data Governance Metadata 100

David Menninger's Analyst Perspectives

MAY 7, 2020

He explains the term “data lake,” describes common use cases and shares his views on some of the latest market trends. He explores the relationship between data warehouses and data lakes and share some of Ventana Research’s findings on the subject.

Data Lake 283

Data Lake Data Warehouse Risk Marketing 283

datapine

SEPTEMBER 29, 2022

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. This is also the point where data quality rules should be reviewed again.

Data Quality 241

Data Quality Metrics Data-driven Management 241

IBM Big Data Hub

DECEMBER 16, 2022

It also serves as a governance tool to drive compliance with data privacy and industry regulations. In other words, a data catalog makes the use of data for insights generation far more efficient across the organization, while helping mitigate risks of regulatory violations. Protected and compliant data.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

IBM Big Data Hub

NOVEMBER 28, 2022

Suppose that a new data asset becomes available but remains hidden from your data consumers because of improper or inadequate tagging. How do you keep pace with growing data volumes and increased demand from data consumers and deliver real-time data governance for trusted outcomes? Improve data discovery.

Metadata 56

Metadata Data Quality Data-driven Data Governance 56

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

erwin

MAY 7, 2020

Those organizations that can find ways to extract data and use it to their advantage will be successful. However, we really need to understand what data we have, what it means, and where it is located. Without this understanding, data can proliferate and become more of a risk to the business than a benefit.

Data Architecture 131

Data Architecture Enterprise Modeling Sales 131