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

Alation

JANUARY 9, 2023

Alation joined with Ortecha , a data management consultancy, to publish a white paper providing insights and guidance to stakeholders and decision-makers charged with implementing or modernising data risk management functions. The Increasing Focus On Data Risk Management. Download the complete white paper now.

Risk Management 52

Risk Management Risk Management Metadata 52

Octopai

OCTOBER 18, 2021

First, what active metadata management isn’t : “Okay, you metadata! Now, what active metadata management is (well, kind of): “Okay, you metadata! Metadata are the details on those tools: what they are, what to use them for, what to use them with. . That takes active metadata management. Quit lounging around!

Metadata 83

Metadata Management IT Data Quality 83

erwin

JANUARY 9, 2020

For decades, data modeling has been the optimal way to design and deploy new relational databases with high-quality data sources and support application development. Today’s data modeling is not your father’s data modeling software. So here’s why data modeling is so critical to data governance.

Data Governance 144

Data Governance Modeling Metadata Unstructured Data 144

O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. We are also beginning to see researchers share sample code written in popular open source libraries, and some even share pre-trained models. Model development. Model governance. Source: Ben Lorica.

Modeling 193

Modeling Machine Learning Testing Metrics 193

erwin

JANUARY 17, 2020

What is Data Modeling? Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface. Data models provide visualization, create additional metadata and standardize data design across the enterprise.

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 52

Metadata Data Governance Digital Transformation Data Quality 52

erwin

FEBRUARY 12, 2021

Managing an organization’s governance, risk and compliance (GRC) via its enterprise and business architectures means managing them against business processes (BP). Governance, risk and compliance are treated as isolated bubbles. Data-related risks are not connected with the data architects/data scientists.

Risk 98

Risk Slice and Dice Management Enterprise 98

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 Risk Data Science 130

erwin

AUGUST 31, 2023

Q: Is data modeling cool again? Amidst the evolving technological landscape, one constant remains despite the ongoing attacks from nay-sayers: the importance of data modeling as a foundational step in the delivery of data to these forward-thinking organizations. A: It always was and is getting cooler!!

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

erwin

SEPTEMBER 25, 2020

Understanding the benefits of data modeling is more important than ever. Data modeling is the process of creating a data model to communicate data requirements, documenting data structures and entity types. In this post: What Is a Data Model? Why Is Data Modeling Important? What’s the Best Data Modeling Tool?

Modeling 111

Modeling Cost-Benefit Visualization Metadata 111

IBM Big Data Hub

FEBRUARY 22, 2024

Earning trust in the outputs of AI models is a sociotechnical challenge that requires a sociotechnical solution. But it’s equally important that they have a deep understanding of the risks and limitations of AI and how to implement the appropriate security measures and ethics guardrails. The CRISP-DM model is useful here.

Metadata 95

Metadata Measurement Risk Modeling 95

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Modeling 219

Modeling Machine Learning Predictive Modeling Consulting 219

erwin

MAY 2, 2022

How to create a solid foundation for data modeling of OLTP systems. As you undertake a cloud database migration , a best practice is to perform data modeling as the foundation for well-designed OLTP databases. This makes mastering basic data modeling techniques and avoiding common pitfalls imperative. Data modeling basics.

Modeling 98

Modeling Insurance Cost-Benefit Data Warehouse 98

CIO Business Intelligence

MARCH 18, 2024

Developers of risky generative AI models are now free to release them without government approval, but are being encouraged to self-regulate by adding labels to the output of their models warning of their potential unreliability. Akshara Bassi, senior analyst at Counterpoint, weighed in on the implications for AI model developers.

Metadata 72

Metadata Visualization Modeling Risk 72

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. Multiple unsupported tools for building and deploying models.

Metadata 99

Metadata Risk Risk Management Experimentation 99

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 Modeling 52

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 75

Risk Management Risk Metrics Machine Learning 75

erwin

MARCH 21, 2019

With business process modeling (BPM) being a key component of data governance , choosing a BPM tool is part of a dilemma many businesses either have or will soon face. The right BPM tool also helps organizations increase productivity, reduce errors and mitigate risks to achieve strategic objectives. Choosing a BPM Tool: An Overview.

Modeling 75

Modeling Metadata IT Data Governance 75

IBM Big Data Hub

MAY 9, 2023

Today, AI presents an enormous opportunity to turn data into insights and actions, to help amplify human capabilities, decrease risk and increase ROI by achieving break through innovations. This solution is designed to include everything needed to develop a consistent transparent model management process. It is an imperative.

Metadata 74

Metadata Risk Experimentation Dashboards 74

AWS Big Data

FEBRUARY 29, 2024

Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. Enterprises are doing this by using proprietary data with approaches like Retrieval Augmented Generation (RAG), fine-tuning, and continued pre-training with foundation models.

Data Governance 97

Data Governance Unstructured Data Metadata Data Lake 97

Rocket-Powered Data Science

FEBRUARY 15, 2023

While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

CIO Business Intelligence

MAY 10, 2023

As companies open this “pandora’s box” of new capabilities, they must be prepared to manage data inputs and outputs in secure ways or risk allowing their private data to be consumed in public AI models. The first step in this process is to ensure the right technical and business metadata is in place.

Metadata 96

Metadata Data Governance Modeling Technology 96

erwin

DECEMBER 3, 2020

The answer is radical transformation, made possible by an intelligent, data-driven approach to: New business models. To manage not only risk but also to grow and compete effectively requires the ability to deal with both planned and unplanned change. New products and services. Hyper-competition. Market expansion.

Metadata 141

Metadata Data Governance Data-driven Digital Transformation 141

Ontotext

OCTOBER 11, 2023

These delved deep into the transformative potential of Large Language Models (LLMs) and the need for semantic technologies to ensure high-quality training data for these models. He also shared key findings on how companies use (not that responsibly) training data to work with models. Are LLMs Knowledgeable? A demonstrator.

Metadata 69

Metadata Cost-Benefit Marketing Modeling 69

IBM Big Data Hub

APRIL 11, 2024

In the context of AI, it can refer to the safety and ethics guardrails of AI tools and systems, policies concerning data access and model usage or the government-mandated regulation itself. They identify international coordination and safety regulation as critical to preventing risks related to an “AI race.”

Risk 76

Risk Consulting Modeling Data Processing 76

O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 307

Enterprise Deep Learning Data Governance Risk 307

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

DataKitchen

OCTOBER 20, 2023

Payload DJs facilitate capturing metadata, lineage, and test results at each phase, enhancing tracking efficiency and reducing the risk of data loss. Example 3: Insurance Card Tracking In the pharmaceutical industry, disjointed business processes can cause data loss as customer information navigates through different systems.

Insurance 169

Insurance Metadata Data-driven Data Quality 169

IBM Big Data Hub

MAY 9, 2023

The answer is that generative AI leverages recent advances in foundation models. Unlike traditional ML, where each new use case requires a new model to be designed and built using specific data, foundation models are trained on large amounts of unlabeled data, which can then be adapted to new scenarios and business applications.

Data Warehouse 107

Data Warehouse Machine Learning Cost-Benefit Metadata 107

Alation

SEPTEMBER 7, 2021

In this blog we will discuss how Alation helps minimize risk with active data governance. Now that you have empowered data scientists and analysts to access the Snowflake Data Cloud and speed their modeling and analysis, you need to bolster the effectiveness of your governance models. Find Trusted Data.

Data Governance 59

Data Governance Risk Data Quality Dashboards 59

erwin

JULY 17, 2019

Part Two of the Digital Transformation Journey … In our last blog on driving digital transformation , we explored how enterprise architecture (EA) and business process (BP) modeling are pivotal factors in a viable digital transformation strategy. Ultimately, data is the foundation of the new digital business model.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

IBM Big Data Hub

DECEMBER 4, 2023

IBM’s governance framework is built around four core roles within the company: The Policy Advisory Committee: senior global leaders who provide oversight of the AI Ethics Board and help to establish the company’s strategy and risk tolerance. It helps accelerate responsible, transparent and explainable AI workflows.

Consulting 77

Consulting Risk Metadata Risk Management 77

CIO Business Intelligence

DECEMBER 13, 2023

and applying and enriching metadata helps organizations take a big step toward innovating with generative AI. Balancing reward and risk Once in the shadows, unstructured data is pivotal in helping generative AI enable human creativity and problem-solving.

Unstructured Data 95

Unstructured Data IoT Metadata Manufacturing 95

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. Put simply, consumers trust banks to keep their money safe and return the money when requested.

Risk 70

Risk Risk Management Reporting Metadata 70

Cloudera

APRIL 1, 2024

Our cutting-edge Shared data experience (SDX) service provides a unified control plane for common security, governance and metadata management on all structured and unstructured data. Organizations manage an increasing variety of single purpose databases, resulting in increased cost, complexity, management overhead, and risk.

Unstructured Data 104

Unstructured Data Cost-Benefit Metadata Machine Learning 104

CIO Business Intelligence

MARCH 24, 2023

The Data Governance Institute defines it as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”

Data Governance 142

Data Governance Management Metadata Data Quality 142