Business Intelligence, Data Integration and Metadata

Business Intelligence

Data Integration

Metadata

Data integrity vs. data quality: Is there a difference?

IBM Journey to AI blog

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.

Data Quality

Data Quality Data Integration Metadata Automation

9 data governance strategies that will unlock the potential of your business data

IBM Journey to AI blog

SEPTEMBER 5, 2024

To maximize the value of their AI initiatives, organizations must maintain data integrity throughout its lifecycle. Every organization aims for up-to-date information, real-time market awareness, and insights to achieve optimal business results. Managing this level of oversight requires adept handling of large volumes of data.

Metadata

Metadata Data Quality Auto-classification DevOps

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How data stores and governance impact your AI initiatives

IBM Journey to AI blog

OCTOBER 12, 2023

Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.)

Data Scientist

Data Scientist Metadata Explainability Responsible AI

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How to Achieve High-Accuracy Results When Using LLMs

Relevance, Reach, Revenue: How to Turn Marketing Trends From Hype to High-Impact

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A Beginner’s Guide to Data Warehousing

Unite.AI

DECEMBER 5, 2023

This article will explore data warehousing, its architecture types, key components, benefits, and challenges. What is Data Warehousing? Data warehousing is a data management system to support Business Intelligence (BI) operations. It can handle vast amounts of data and facilitate complex queries.

Metadata

Metadata Big Data ETL Data Mining

18 Data Profiling Tools Every Developer Must Know

Marktechpost

JUNE 5, 2024

Analytics, management, and business intelligence (BI) procedures, such as data cleansing, transformation, and decision-making, rely on data profiling. Content and quality reviews are becoming more important as data sets grow in size and variety of sources. The 18 best data profiling tools are listed below.

Data Quality

Data Quality Metadata Data Integration ETL

Five benefits of a data catalog

IBM Journey to AI blog

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata

Metadata Data Quality Data Discovery Data Scientist

Data architecture strategy for data quality

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.

Data Quality

Data Quality Metadata ETL Big Data

Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning Blog

MARCH 18, 2025

Extraction of relevant data points for electronic health records (EHRs) and clinical trial databases. Data integration and reporting The extracted insights and recommendations are integrated into the relevant clinical trial management systems, EHRs, and reporting mechanisms.

LLM

LLM NLP Data Integration AI

Data Version Control for Data Lakes: Handling the Changes in Large Scale

ODSC - Open Data Science

SEPTEMBER 27, 2023

Storage Optimization: Data warehouses use columnar storage formats and indexing to enhance query performance and data compression. They excel at managing structured data and supporting ACID (Atomicity, Consistency, Isolation, Durability) transactions.

Big Data

Big Data Metadata ETL Data Science

Fine-tune your data lineage tracking with descriptive lineage

IBM Journey to AI blog

JULY 1, 2024

Irina Steenbeek introduces the concept of descriptive lineage as “a method to record metadata-based data lineage manually in a repository.” Extraction, transformation and loading (ETL) tools dominated the data integration scene at the time, used primarily for data warehousing and business intelligence.

ETL

ETL Automation Metadata Business Intelligence

Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

NOVEMBER 15, 2023

In this post, we demonstrate how data aggregated within the AWS CCI Post Call Analytics solution allowed Principal to gain visibility into their contact center interactions, better understand the customer journey, and improve the overall experience between contact channels while also maintaining data integrity and security.

Data Ingestion

Data Ingestion Metadata NLP Data Scientist

IBM Planning Analytics: The scalable solution for enterprise growth

IBM Journey to AI blog

SEPTEMBER 17, 2024

Its in-memory processing helps to ensure that data is ready for quick analysis and reporting, enabling real-time what-if scenarios and reports without lag. Our solution handles massive multidimensional cubes seamlessly, enabling you to maintain a complete view of your data without sacrificing performance or data integrity.

Big Data

Big Data Metadata Business Intelligence Data Integration

Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning Blog

OCTOBER 2, 2024

However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level. In this post, we discuss how to address these challenges holistically.

Generative AI

Generative AI Data Ingestion AI AI

Accenture creates a Knowledge Assist solution using generative AI services on AWS

AWS Machine Learning Blog

SEPTEMBER 28, 2023

Metadata about the request/response pairings are logged to Amazon CloudWatch. As an Information Technology Leader, Jay specializes in artificial intelligence, data integration, business intelligence, and user interface domains.

Generative AI

Generative AI Artificial Intelligence Artificial Intelligence Chatbots

Introduction to DBMS: A Comprehensive Guide

Pickl AI

JULY 19, 2024

They enhance data integrity, security, and accessibility while providing tools for efficient data management and retrieval. A Database Management System (DBMS) is specialised software designed to efficiently manage and organise data within a computer system. Indices are data structures optimised for rapid data retrieval.

Data Integration

Data Integration Metadata Big Data Business Intelligence

Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world

Pickl AI

NOVEMBER 15, 2023

On the other hand, a Data Warehouse is a structured storage system designed for efficient querying and analysis. It involves the extraction, transformation, and loading (ETL) process to organize data for business intelligence purposes. It often serves as a source for Data Warehouses. What Is Data Lake Architecture?

ETL

ETL Metadata Business Intelligence Data Analysis

A brief history of Data Engineering: From IDS to Real-Time streaming

Artificial Corner

JUNE 6, 2023

This period also saw the development of the first data warehouses, large storage repositories that held data from different sources in a consistent format. The concept of data warehousing was introduced by Bill Inmon, often referred to as the “father of data warehousing.” Morgan Kaufmann. Dean, J., & Ghemawat, S.

Data Mining

Data Mining Big Data ETL Machine Learning

Data democratization: How data architecture can drive business decisions and AI initiatives

IBM Journey to AI blog

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Machine Learning

Machine Learning Metadata Automation AI

Artificial Intelligence Zone

Data integrity vs. data quality: Is there a difference?

9 data governance strategies that will unlock the potential of your business data

Webinars

Trending Sources

How data stores and governance impact your AI initiatives

Webinars

A Beginner’s Guide to Data Warehousing

18 Data Profiling Tools Every Developer Must Know

Five benefits of a data catalog

Data architecture strategy for data quality

Revolutionizing clinical trials with the power of voice and AI

Data Version Control for Data Lakes: Handling the Changes in Large Scale

Fine-tune your data lineage tracking with descriptive lineage

Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

IBM Planning Analytics: The scalable solution for enterprise growth

Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

Accenture creates a Knowledge Assist solution using generative AI services on AWS

Introduction to DBMS: A Comprehensive Guide

Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world

A brief history of Data Engineering: From IDS to Real-Time streaming

Data democratization: How data architecture can drive business decisions and AI initiatives

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