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

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 99

Data Governance Unstructured Data Metadata Data Lake 99

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

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 98

Unstructured Data IoT Metadata Manufacturing 98

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

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

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

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 98

Metadata Data Governance Modeling Technology 98

Ontotext

DECEMBER 16, 2021

Organizations in this sector often deal with multiple repositories of millions of documents on top of proprietary data from labs and other internal sources. Domain experts can document their knowledge of terms and concepts within the graph and then connect the nodes to data stored in any number of external sources. See figure 1.).

Metadata 98

Metadata Reporting Unstructured Data Risk 98

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. Strengthen data security. How erwin Can Help. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

Data Governance 40

Data Governance Risk Metadata Management 40

Ontotext

MAY 2, 2024

Another challenge is the data models that represent how insights and analytics teams see the world. These models are as important to companies as their frontline products and determine how data is managed, consumed, combined, joined, and analyzed. Here we talk about metadata management, catalog of catalogs, and so on.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

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. All of these factors have an impact on a well-defined data integration model. Stop Wasting Your Time.

Data Governance 145

Data Governance Metadata Cost-Benefit Digital Transformation 145

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

JANUARY 24, 2019

Often these enterprises are heavily regulated, so they need a well-defined data integration model that helps avoid data discrepancies and removes barriers to enterprise business intelligence and other meaningful use. 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

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. They are: Data models.

Data Governance 105

Data Governance Metadata Risk Data Lake 105

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

Cloudera

SEPTEMBER 28, 2020

The data lifecycle model ingests data using Kafka, enriches that data with Spark-based batch process, performs deep data analytics using Hive and Impala, and finally uses that data for data science using Cloudera Data Science Workbench to get deep insights. Review the Upgrade document topic for the supported upgrade paths.

Testing 130

Testing Metadata Risk Data Science 130

IBM Big Data Hub

SEPTEMBER 20, 2022

But these organizations need to be able to trust their AI/ML models before they can be operationalized and used in crucial business processes. These days, if an AI model makes a biased, unfair decision involving the health, wealth or well-being of humans, an organization can hit the news for the wrong reasons.

Metadata 56

Metadata Machine Learning Data-driven Modeling 56

DataRobot Blog

SEPTEMBER 1, 2022

For example, a recent IDC study 1 shows that it takes about 290 days on average to deploy a model into production from start to finish. Once you move your model into production, you need to monitor and manage your models to ensure that you can trust predictions and turn them into the right business decisions.

Metrics 145

Metrics Statistics Modeling Data Science 145

erwin

NOVEMBER 13, 2020

Data Security & Risk Management. For example, a COVID response plan will use EA to document if employees work from home, what their roles are, the projects on which they’re working, and what their schedules are. Digital Transformation. Compliance/Legislation. Innovation Management. Artificial Intelligence. Mergers and Acquisitions.

Enterprise 131

Enterprise Digital Transformation Key Performance Indicator Data-driven 131

Alation

MARCH 14, 2023

All critical data elements (CDEs) should be collated and inventoried with relevant metadata, then classified into relevant categories and curated as we further define below. Store Where individual departments have their own databases for metadata management, data will be siloed, meaning it can’t be shared and used business-wide.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

Ontotext

FEBRUARY 12, 2024

This calls for additional planning, documentation, and testing. It’s also critical to advocate a smooth culture change because data mesh involves shifting from thinking about data as tables to data as a combination of multiple elements, such as code, infrastructure, and metadata. Not all organizations are ready to make that investment.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

Octopai

DECEMBER 11, 2022

Banks didn’t accurately assess their credit and operational risk and hold enough capital reserves, leading to the Great Recession of 2008-2009. Data lineage and financial risk data compliance. Even when they are allowed to use internal models, the result still needs to reach a certain threshold ( 72.5% Enter: BCBS 239!

Insurance 52

Insurance Risk Metadata Reporting 52

O'Reilly on Data

JULY 28, 2020

You must detect when the model has become stale, and retrain it as necessary. This isn’t always simple, since it doesn’t just take into account technical risk; it also has to account for social risk and reputational damage. Fault Tolerant Versus Fault Intolerant AI Problems.

Marketing 362

Marketing Experimentation Metrics Testing 362

CIO Business Intelligence

DECEMBER 4, 2023

Tomorrow’s market leaders will recognize how to curate and enrich the value of all assets, which will train AI models, generate insights, and drive automation. With constant advances in intelligent document processing, compute power, DevOps workflows, and AI, the content, context, and value of unstructured data is rapidly increasing.

Unstructured Data 52

Unstructured Data Data Lake Metadata Business Objectives 52

datapine

SEPTEMBER 29, 2022

These needs are then quantified into data models for acquisition and delivery. It involves: Reviewing data in detail Comparing and contrasting the data to its own metadata Running statistical models Data quality reports. The captured data points should be modeled and defined based on specific characteristics (e.g.,

Data Quality 241

Data Quality Metrics Data-driven Management 241

erwin

MARCH 8, 2019

A number of best practices and technology solutions were used to establish the data required for managing the registration and classification of data feeds: The underlying metadata is harvested followed by an initial quality check. Then the metadata is classified against a semantic model held in a business glossary.

Data Governance 65

Data Governance Metadata Data Warehouse Data Quality 65

erwin

APRIL 27, 2022

Best Data Modeling Solution (erwin Data Modeler). Best Data Modeling Solution (erwin Data Modeler). Read more about erwin® Data Modeler by Quest. Extensive automation and collaboration functionality speed development cycles and minimize the risks of bugs and performance issues.

Data Governance 52

Data Governance Digital Transformation Metadata Data Warehouse 52

Ontotext

MARCH 8, 2023

With the size of data and dropping attention spans of online users, digital personalization has become one of the top priorities for companies’ business models. However, this information is typically stored in disparate locations, often hidden within departmental documents or applications. million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Domino Data Lab

OCTOBER 1, 2020

Combining the power of Domino Data Labs with Okera, your data scientists only get access to the columns, rows, and cells allowed, easily removing or redacting sensitive data such as PII and PHI not relevant to training models. So what does this look like? client('s3') obj = s3.get_object(Bucket='clinical-trials',

Enterprise 105

Enterprise Metadata Cost-Benefit Data Processing 105

Cloudera

AUGUST 31, 2021

Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Consideration of both data & metadata in the migration.

Data Warehouse 81

Data Warehouse Cost-Benefit Metadata Data-driven 81

Ontotext

MARCH 18, 2020

The knowledge graph model is one way of doing it and, not surprisingly, it has been in increasing demand in the last decade. Do you want to learn more about how Ontotext Knowledge Graph Platform can help you minimize your technology and architectural risk and save time and effort when delivering your enterprise solutions or PoC?

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

AWS Big Data

MAY 2, 2023

For detailed release documentation with sample code, visit the Apache Airflow v2.4.0 This feature is particularly useful if you want to externally process various files, evaluate multiple machine learning models, or extraneously process a varied amount of data based on a SQL request. Release Notes. which has also been extended in v2.4.

Testing 105

Testing Experimentation Management Metadata 105

erwin

FEBRUARY 19, 2021

Metadata management is key to wringing all the value possible from data assets. What Is Metadata? Analyst firm Gartner defines metadata as “information that describes various facets of an information asset to improve its usability throughout its life cycle. It is metadata that turns information into an asset.”.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

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 222

Modeling Machine Learning Predictive Modeling Consulting 222

Ontotext

JUNE 22, 2023

This will minimize the risk of investing resources in fundamentally wrong assumptions and will give researchers more confidence to choose top targets for validation. On top of that, data is sometimes unreliable , and inaccurate or missing metadata makes it hard to decide which information to trust.

Metrics 52

Metrics Statistics Visualization Metadata 52