Smart Data Collective

OCTOBER 20, 2021

The data integration landscape is under a constant metamorphosis. In the current disruptive times, businesses depend heavily on information in real-time and data analysis techniques to make better business decisions, raising the bar for data integration. Why is Data Integration a Challenge for Enterprises?

Data Integration 133

Data Integration Machine Learning Big Data Statistics 133

Data Virtualization

APRIL 10, 2025

Citizens expect efficient services, The post Empowering the Public Sector with Data: A New Model for a Modern Age appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. In this dynamic environment, time is everything.

Modeling 75

Modeling Data Integration Management Data Architecture 75

CIO Business Intelligence

DECEMBER 20, 2024

Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Curate the data.

Data Architecture 122

Data Architecture Management Consulting Internet of Things 122

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

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 279

Machine Learning Data Quality Statistics Modeling 279

CIO Business Intelligence

OCTOBER 9, 2024

How will organizations wield AI to seize greater opportunities, engage employees, and drive secure access without compromising data integrity and compliance? While it may sound simplistic, the first step towards managing high-quality data and right-sizing AI is defining the GenAI use cases for your business.

Strategy 143

Strategy Modeling Management Data Lake 143

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

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 382

Big Data Data Science Data Integration Publishing 382

CIO Business Intelligence

NOVEMBER 19, 2024

The core of their problem is applying AI technology to the data they already have, whether in the cloud, on their premises, or more likely both. Imagine that you’re a data engineer. The data is spread out across your different storage systems, and you don’t know what is where. What does the next generation of AI workloads need?

Management 116

Management Unstructured Data Deep Learning Metadata 116

Data Virtualization

OCTOBER 31, 2024

Reading Time: 2 minutes When making decisions that are critical to national security, governments rely on data, and those that leverage the cutting edge technology of generative AI foundation models will have a distinct advantage over their adversaries. Pros and Cons of generative AI.

Management 52

Management Modeling Data Integration Technology 52

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 279

Machine Learning Enterprise IoT Big Data 279

CIO Business Intelligence

APRIL 30, 2025

As Xerox continues its reinvention, shifting from its traditional print roots to a services-led model, agentic AI fits well into that journey. And around 45% also cite data governance and compliance concerns. Were also exploring agents to handle internal IT support tickets and finance workflows like invoice matching and spend analysis.

IT 139

IT Sales Cost-Benefit Data-driven 139

David Menninger's Analyst Perspectives

MAY 15, 2025

Oracles pricing model does not charge for input/output operations, which can be an important consideration in cloud-based workloads. The software provider also claims to be working on other types of assistants, such as creating data integration and transformation pipelines, test data and generating schemas.

Data Lake 130

Data Lake Data Warehouse Machine Learning Software 130

CIO Business Intelligence

APRIL 25, 2024

“The challenge that a lot of our customers have is that requires you to copy that data, store it in Salesforce; you have to create a place to store it; you have to create an object or field in which to store it; and then you have to maintain that pipeline of data synchronization and make sure that data is updated,” Carlson said.

Data Integration 119

Data Integration Data Lake Data Warehouse Metadata 119

CIO Business Intelligence

FEBRUARY 26, 2025

We actually started our AI journey using agents almost right out of the gate, says Gary Kotovets, chief data and analytics officer at Dun & Bradstreet. The problem is that, before AI agents can be integrated into a companys infrastructure, that infrastructure must be brought up to modern standards. Thats what Cisco is doing.

IT 141

IT Enterprise Interactive Data Quality 141

CIO Business Intelligence

FEBRUARY 27, 2025

Data professionals need to access and work with this information for businesses to run efficiently, and to make strategic forecasting decisions through AI-powered data models. Without integrating mainframe data, it is likely that AI models and analytics initiatives will have blind spots.

Metadata 98

Metadata Data Lake Cost-Benefit Forecasting 98

O'Reilly on Data

OCTOBER 11, 2022

So from the start, we have a data integration problem compounded with a compliance problem. An AI project that doesn’t address data integration and governance (including compliance) is bound to fail, regardless of how good your AI technology might be. Data needs to become the means, a tool for making good decisions.

Data Integration 353

Data Integration Insurance Dashboards Data-driven 353

David Menninger's Analyst Perspectives

NOVEMBER 19, 2024

Bigeye’s anomaly detection capabilities rely on the automated generation of data quality thresholds based on machine learning (ML) models fueled by historical data. The company also offers associated alerts delivered to data owners and data consumers, and reinforcement learning to adapt notifications based on user feedback.

Data Quality 130

Data Quality Dashboards Data-driven Software 130

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in London earlier this year. A typical data pipeline for machine learning.

Machine Learning 279

Machine Learning Technology Deep Learning Data Science 279

AWS Big Data

JANUARY 15, 2025

In addition to real-time analytics and visualization, the data needs to be shared for long-term data analytics and machine learning applications. To achieve this, EUROGATE designed an architecture that uses Amazon DataZone to publish specific digital twin data sets, enabling access to them with SageMaker in a separate AWS account.

IoT 111

IoT Machine Learning Metadata Data-driven 111

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Meta-Orchestration .

Testing 300

Testing Machine Learning Consulting Data Science 300

AWS Big Data

JULY 26, 2023

Many AWS customers have integrated their data across multiple data sources using AWS Glue , a serverless data integration service, in order to make data-driven business decisions. Are there recommended approaches to provisioning components for data integration?

Data Integration 98

Data Integration Snapshot Testing Visualization 98

O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations.

Machine Learning 277

Machine Learning Risk Management Statistics 277

AWS Big Data

MARCH 13, 2025

From within the unified studio, you can discover data and AI assets from across your organization, then work together in projects to securely build and share analytics and AI artifacts, including data, models, and generative AI applications.

Analytics 114

Analytics Data Lake Data Warehouse Data-driven 114

CIO Business Intelligence

APRIL 8, 2025

These strategies, such as investing in AI-powered cleansing tools and adopting federated governance models, not only address the current data quality challenges but also pave the way for improved decision-making, operational efficiency and customer satisfaction. When financial data is inconsistent, reporting becomes unreliable.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102

AWS Big Data

DECEMBER 4, 2024

They’re taking data they’ve historically used for analytics or business reporting and putting it to work in machine learning (ML) models and AI-powered applications. Amazon SageMaker Unified Studio (Preview) solves this challenge by providing an integrated authoring experience to use all your data and tools for analytics and AI.

Data Analytics 105

Data Analytics Analytics Data Lake Data Quality 105

AWS Big Data

JULY 19, 2023

You can structure your data, measure business processes, and get valuable insights quickly can be done by using a dimensional model. Amazon Redshift provides built-in features to accelerate the process of modeling, orchestrating, and reporting from a dimensional model. Declare the grain of your data.

Modeling 97

Modeling Sales Data Warehouse Snapshot 97

AWS Big Data

JULY 26, 2023

AWS Glue provides different authoring experiences for you to build data integration jobs. Data scientists tend to run queries interactively and retrieve results immediately to author data integration jobs. This interactive experience can accelerate building data integration pipelines.

Data Integration 98

Data Integration Interactive Machine Learning Big Data 98

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning 279

Machine Learning Software Metadata Testing 279

CIO Business Intelligence

OCTOBER 24, 2024

The challenge is that these architectures are convoluted, requiring diverse and multiple models, sophisticated retrieval-augmented generation stacks, advanced data architectures, and niche expertise,” they said. They predicted more mature firms will seek help from AI service providers and systems integrators.

ROI 127

ROI Data-driven Enterprise Experimentation 127

KDnuggets

JUNE 19, 2024

Perform data integrity tests and generate model evaluation reports by writing a few lines of code.

Testing 146

Testing Machine Learning Data Integration Reporting 146

erwin

AUGUST 31, 2023

Q: Is data modeling cool again? In today’s fast-paced digital landscape, data reigns supreme. The data-driven enterprise relies on accurate, accessible, and actionable information to make strategic decisions and drive innovation. A: It always was and is getting cooler!!

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

CIO Business Intelligence

AUGUST 16, 2023

ChatGPT is capable of doing many of these tasks, but the custom support chatbot is using another model called text-embedding-ada-002, another generative AI model from OpenAI, specifically designed to work with embeddings—a type of database specifically designed to feed data into large language models (LLM).

Modeling 98

Modeling Enterprise Cost-Benefit Data Science 98

AWS Big Data

OCTOBER 21, 2024

When dealing with third-party data sources, AWS Data Exchange simplifies the discovery, subscription, and utilization of third-party data from a diverse range of producers or providers. As a producer, you can also monetize your data through the subscription model using AWS Data Exchange.

Sales 116

Sales Data-driven Data Processing Key Performance Indicator 116

datapine

NOVEMBER 27, 2019

The development of business intelligence to analyze and extract value from the countless sources of data that we gather at a high scale, brought alongside a bunch of errors and low-quality reports: the disparity of data sources and data types added some more complexity to the data integration process.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

AWS Big Data

DECEMBER 17, 2024

Seamless Lakehouse architectures Lakehouse brings together flexibility and openness of data lakes with the performance and transactional capabilities of data warehouses. Lakehouse allows you to use preferred analytics engines and AI models of your choice with consistent governance across all your data.

Data Lake 98

Data Lake Data Warehouse Data-driven Optimization 98

AWS Big Data

JANUARY 9, 2025

Simplified data corrections and updates Iceberg enhances data management for quants in capital markets through its robust insert, delete, and update capabilities. These features allow efficient data corrections, gap-filling in time series, and historical data updates without disrupting ongoing analyses or compromising data integrity.

Metadata 111

Metadata Snapshot Cost-Benefit Optimization 111