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

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.

Modeling 98

Modeling Insurance Cost-Benefit Data Warehouse 98

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

erwin

JANUARY 9, 2020

Then there’s unstructured data with no contextual framework to govern data flows across the enterprise not to mention time-consuming manual data preparation and limited views of data lineage. Today’s data modeling is not your father’s data modeling software.

Data Governance 144

Data Governance Modeling Metadata Unstructured Data 144

Data Virtualization

FEBRUARY 24, 2022

Reading Time: 2 minutes In the software world, we constantly represent real phenomena in abstract terms, and this is no exception in the world of data storage. We say that data storage, like matter, adheres to the Law of Increments, which states that, according.

Modeling 59

Modeling Data Integration Software Management 59

David Menninger's Analyst Perspectives

MAY 9, 2024

Large language models (LLMs) and generative AI (GenAI) have taken the AI world by storm. Our team has also described how AI can help enterprises improve customer experiences , transform human capital management , improve marketing and sales effectiveness , enhance data integration processes and drive automation for enhanced efficiency.

Finance 130

Finance Sales Data Integration Marketing 130

Business Over Broadway

SEPTEMBER 27, 2018

In this scenario, marketing analytics can only be conducted within one data silo at a time, decreasing your model’s predictive power / increasing your model’s error. Data Integration as your Customer Genome Project. Data Integration is an exercise in creating your customer genome.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

erwin

MAY 14, 2021

Accelerating the retrieval and analysis of data —so much of it unstructured—is vital to becoming a data-driven business that can effectively respond in real time to customers, partners, suppliers and other parties, and profit from these efforts. The Newest Release of erwin Data Modeler.

Modeling 80

Modeling Digital Transformation Data-driven Optimization 80

Data Virtualization

MAY 29, 2024

Reading Time: 5 minutes GenAI is driving all kinds of innovations and new ways of working, and we have discovered that large language models (LLMs) are capable of some pretty amazing feats. The post Querying Minds Want to Know: Can a Data Fabric and RAG Clean up LLMs? In my last post I discussed how they can enable users to.

Enterprise 64

Enterprise Data Integration Modeling Management 64

erwin

MAY 13, 2020

The role of data modeling (DM) has expanded to support enterprise data management, including data governance and intelligence efforts. Metadata management is the key to managing and governing your data and drawing intelligence from it. Types of Data Models: Conceptual, Logical and Physical.

Data Governance 93

Data Governance Modeling Enterprise Management 93

DataKitchen

MAY 14, 2024

Chris will overview data at rest and in use, with Eric returning to demonstrate the practical steps in data testing for both states. Session 3: Mastering Data Testing in Development and Migration During our third session, the focus will shift towards regression and impact assessment in development cycles. Reserve Your Spot!

Data Quality 195

Data Quality Testing Metrics Measurement 195

Alation

JUNE 2, 2022

These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. Currently, models are managed by modelers and by the software tools they use, which results in a patchwork of control, but not on an enterprise level. And until recently, such governance processes have been fragmented.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

BI-Survey

OCTOBER 18, 2018

I t is interesting to see that data integration between on-premises and cloud applications is ranked an equally important use case across all company sizes while data integration between cloud applications becomes more important the smaller the company is. BI and Data Management in the Cloud Report.

Data Integration 40

Data Integration Data mining Reporting Management 40

Data Virtualization

MAY 30, 2024

Reading Time: 2 minutes The Denodo Platform’s integration with the Snowflake Arctic large language model (LLM) is paving the way for advanced retrieval augmented generation (RAG) applications that combine the power of LLMs with efficient information retrieval. At the Snowflake Data Cloud Summit 2024.

Enterprise 52

Enterprise Data Integration Modeling Management 52

CIO Business Intelligence

MAY 9, 2024

In today’s data-driven world, the ability to seamlessly integrate structured and unstructured data in a hybrid cloud environment is critical for organizations seeking to harness the full potential of their data assets. However, many enterprises face significant challenges in achieving this seamlessly.

Digital Transformation 52

Digital Transformation Data Integration Unstructured Data Data-driven 52

Data Virtualization

APRIL 8, 2024

Reading Time: 3 minutes Leveraging enterprise data for generative AI and large language models presents significant challenges related to data silos, quality inconsistencies, privacy and security concerns, compliance with data regulations, capturing domain-specific knowledge, and mitigating inherent biases.

Enterprise 52

Enterprise Data Integration Management Modeling 52

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. The problem is even more magnified in the case of structured enterprise data.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Ontotext

FEBRUARY 14, 2024

Data integration If your organization’s idea of data integration is printing out multiple reports and manually cross-referencing them, you might not be ready for a knowledge graph. There’s a famous saying by a statistician, George Box, “All models are wrong, but some are useful.”

Recreation/Entertainment 105

Recreation/Entertainment Data Integration Modeling Data Quality 105

Analytics Vidhya

NOVEMBER 30, 2021

This article was published as a part of the Data Science Blogathon. Introduction to ETL ETL is a type of three-step data integration: Extraction, Transformation, Load are processing, used to combine data from multiple sources. It is commonly used to build Big Data.

Big Data 372

Big Data Data Integration Data Science Publishing 372

erwin

APRIL 4, 2022

If you’re a long-time erwin ® Data Modeler by Quest ® customer, you might be asking yourself, “What happened to the release naming convention of erwin Data Modeler?” In 2021 erwin Data Modeler released 2021R1. What’s new in erwin Data Modeler R12.0? DevOps GitHub integration via Mart.

Modeling 52

Modeling Data Processing Data-driven Big Data 52

CIO Business Intelligence

APRIL 1, 2024

Data preparation, including anonymizing, labeling, and normalizing data across sources, is key. You’ll also institute guardrails for data governance, data quality, data integrity, and data security. Right-size your model(s). Choose a workload location. 2024 Artificial Intelligence

Data Lake 142

Data Lake Consulting Uncertainty Risk 142

DataKitchen

MAY 10, 2024

The Challenge of Data Production Data production encompasses processing and refining raw data into valuable insights, including dashboard production, warehouse constructions, and model refreshes. Are all required data records and values present and accurate? Did I Ensure That Data Does Not Conflict With Itself?

Testing 124

Testing Metrics Data Quality Dashboards 124

Domino Data Lab

JULY 20, 2022

We've blogged before about the importance of model validation, a process that ensures that the model is performing the way it was intended and that it solves the problem it was designed to solve. We've also talked about incorporating tests in your pipeline, which many data scientists find problematic.

Machine Learning 98

Machine Learning Testing Modeling Data Integration 98

Data Virtualization

APRIL 4, 2024

Over the past few decades, we have been storing up data and generating even more of it than we have known what. The post Querying Minds Want to Know: Can a Data Fabric and RAG Clean up LLMs? appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Data Integration 98

Data Integration Management Data-driven IT 98

AWS Big Data

MAY 23, 2024

In today’s data-driven world, organizations often deal with data from multiple sources, leading to challenges in data integration and governance. This process is crucial for maintaining data integrity and avoiding duplication that could skew analytics and insights. b.id )) """).write.mode("overwrite").parquet(f"s3://{bucket_name}/models/1/trainingData/marked/")

Machine Learning 97

Machine Learning Interactive Recreation/Entertainment Data Integration 97

DataKitchen

FEBRUARY 27, 2024

Your LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers The rise of Large Language Models (LLMs) such as GPT-4 marks a transformative era in artificial intelligence, heralding new possibilities and challenges in equal measure. In this context, multi-tool Data Journey observability becomes crucial.

Data Quality 117

Data Quality Unstructured Data Testing Modeling 117

CIO Business Intelligence

MAY 14, 2024

Controlling escalating cloud and AI costs and preventing data leakage are the top reasons why enterprises are eying hybrid infrastructure as their target AI solution. Enterprises need to ensure that private corporate data does not find itself inside a public AI model,” McCarthy says.

IT 143

IT Data Processing Enterprise Modeling 143

O'Reilly on Data

FEBRUARY 4, 2019

In this post, I’ll describe some of the key areas of interest and concern highlighted by respondents from Europe, while describing how some of these topics will be covered at the upcoming Strata Data conference in London (April 29 - May 2, 2019). Data Platforms. Data Integration and Data Pipelines.

Machine Learning 277

Machine Learning Enterprise IoT Big Data 277

Data Virtualization

MAY 20, 2024

The post Querying Minds Want to Know: Can a Data Fabric and RAG Clean up LLMs? – Part 2: On-Demand Enterprise Data Querying appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Enterprise 95

Enterprise Data Integration Management Data Science 95

Cloudera

MAY 9, 2019

The report created a readiness model with five dimensions and various metrics under each dimension. The five dimensions of the readiness model are –. Data readiness – These set of metrics help you measure if your organization is geared up to handle the sheer volume, variety and velocity of IoT data. See you there!

IoT 56

IoT Modeling Metrics Measurement 56

DataKitchen

DECEMBER 9, 2022

DataOps involves close collaboration between data scientists, IT professionals, and business stakeholders, and it often involves the use of automation and other technologies to streamline data-related tasks. One of the key benefits of DataOps is the ability to accelerate the development and deployment of data-driven solutions.

Machine Learning 130

Machine Learning Data-driven Optimization Modeling 130

CIO Business Intelligence

FEBRUARY 16, 2024

Developing a data-led strategy The first, and most critical step in the process is accessing, integrating, and curating the underlying data that will be used to train and power AI models. This is a challenge for many CIOs.

Strategy 138

Strategy Insurance Digital Transformation Enterprise 138

DataKitchen

OCTOBER 20, 2023

It boosts customer satisfaction by providing a self-service mechanism to verify data status, quality, and integrity independently. Additionally, real-time alerts offer an extra layer of assurance by notifying consumers about critical events in their data journey.

Insurance 169

Insurance Metadata Data-driven Data Quality 169

Data Virtualization

MAY 23, 2024

The post Improving the Accuracy of LLM-Based Text-to-SQL Generation with a Semantic Layer in the Denodo Platform appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. Three core ideas, working together, enable that scenario: The.

Data Integration 59

Data Integration Management Enterprise Metadata 59

Data Virtualization

MAY 20, 2022

Reading Time: 3 minutes In recent years, the banking industry has seen the emergence of new modes of financing, new business models, and the development of new technical innovations, all of which relies on the digital exchange of financial data. With open banking and.

Finance 52

Finance Data Integration Modeling Management 52

Ontotext

MARCH 20, 2024

In today’s data-driven world, businesses are drowning in a sea of information. Traditional data integration methods struggle to bridge these gaps, hampered by high costs, data quality concerns, and inconsistencies. We believe in building partnerships with our clients to ensure their success.

Data-driven 52

Data-driven Strategy Sales Data Integration 52

Data Virtualization

JUNE 8, 2023

Disruptive, large language model (LLM) technologies like ChatGPT, OPT, CodeGen, and PaLM 2 are about to transform both the way we communicate with applications and. Reading Time: 3 minutes Artificial intelligence (AI) technologies are poised to bring about a revolution in the business world.

Data Integration 52

Data Integration Technology Modeling Management 52