Unite.AI

NOVEMBER 13, 2024

Today, the demand for LLMs in data analysis is so high that the industry is seeing rapid growth, with these models expected to play a significant role in business intelligence. ” The model executes these processes in seconds, ensuring higher data quality and improving downstream analytics.

Large Language Models 261

Large Language Models Data Analysis AI AI 261

IBM Journey to AI blog

OCTOBER 22, 2024

The ability to effectively deploy AI into production rests upon the strength of an organization’s data strategy because AI is only as strong as the data that underpins it. Data must be combined and harmonized from multiple sources into a unified, coherent format before being used with AI models.

Data Integration 256

Data Integration ETL Business Intelligence Data Quality 256

AssemblyAI

JANUARY 22, 2025

For enterprises like CallRail, this accuracy translates directly to revenue: "If the transcriptions are not accurate, then the downstream intelligence our customers depend on will also be subpar—garbage in, garbage out," says Ryan Johnson, Chief Product Officer at CallRail. Here's how each component works together: 1.

Automation 130

Automation AI Business Intelligence AI 130

Pickl AI

MARCH 3, 2025

Summary: Business Intelligence Analysts transform raw data into actionable insights. They use tools and techniques to analyse data, create reports, and support strategic decisions. Key skills include SQL, data visualization, and business acumen. Introduction We are living in an era defined by data.

Business Intelligence 52

Business Intelligence Data Analysis Data Quality Continuous Learning 52

Pickl AI

JANUARY 28, 2025

Summary: Understanding Business Intelligence Architecture is essential for organizations seeking to harness data effectively. This framework includes components like data sources, integration, storage, analysis, visualization, and information delivery. What is Business Intelligence Architecture?

Business Intelligence 52

Business Intelligence ETL Data Integration Big Data 52

IBM Journey to AI blog

APRIL 10, 2023

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

ESG 259

ESG Metadata AI AI 259

IBM Journey to AI blog

SEPTEMBER 5, 2024

Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success. Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality.

Metadata 188

Metadata Data Quality Auto-classification DevOps 188

IBM Journey to AI blog

OCTOBER 14, 2024

“ Gen AI has elevated the importance of unstructured data, namely documents, for RAG as well as LLM fine-tuning and traditional analytics for machine learning, business intelligence and data engineering,” says Edward Calvesbert, Vice President of Product Management at IBM watsonx and one of IBM’s resident data experts.

Business Intelligence 195

Business Intelligence AI AI Data Science 195

IBM Journey to AI blog

JULY 3, 2024

Establishing a foundation of trust: Data quality and governance for enterprise AI As organizations increasingly rely on artificial intelligence (AI) to drive critical decision-making, the importance of data quality and governance cannot be overstated.

Data Quality 112

Data Quality Metadata Business Intelligence AI 112

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 130

Metadata Data Quality Data Discovery Data Scientist 130

Defined.ai blog

AUGUST 10, 2023

For example, if your AI model were designed to predict future sales based on past data, the output would likely be a predictive score. This score represents the predicted sales, and its accuracy would depend on the data quality and the AI model’s efficiency. Maintaining data quality.

Business Intelligence 52

Business Intelligence Data Analysis Neural Network Data Quality 52

Pickl AI

MARCH 19, 2025

It helps data engineers collect, store, and process streams of records in a fault-tolerant way, making it crucial for building reliable data pipelines. Amazon Redshift Amazon Redshift is a cloud-based data warehouse that enables fast query execution for large datasets.

Big Data 52

Big Data Data Science Automation Python 52

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. Data profiling is a crucial tool.

Data Quality 108

Data Quality Metadata Data Integration ETL 108

Flipboard

APRIL 30, 2025

million in seed funding to transform how businesses prepare data for AI, promising to save data scientists from the task that consumes 80% of their time. Brooklyn-based Structify emerges from stealth with $4.1 Read More

Data Scientist 162

Data Scientist Data Quality AI AI 162

AWS Machine Learning Blog

APRIL 3, 2025

Embracing generative AI with Amazon Bedrock The company has identified several use cases where generative AI can significantly impact operations, particularly in analytics and business intelligence (BI). This tool democratizes data access across the organization, enabling even nontechnical users to gain valuable insights.

Generative AI 80

Generative AI Artificial Intelligence Artificial Intelligence Machine Learning 80

AWS Machine Learning Blog

MARCH 18, 2025

Regulatory compliance By integrating the extracted insights and recommendations into clinical trial management systems and EHRs, this approach facilitates compliance with regulatory requirements for data capture, adverse event reporting, and trial monitoring. Solution overview The following diagram illustrates the solution architecture.

LLM 112

LLM NLP Data Integration AI 112

Flipboard

JUNE 17, 2025

This includes working with both structured and unstructured data and employing visualization techniques to communicate findings effectively. Equally important is the ability to manage data responsibly. This involves organizing datasets, making sure data quality, and adhering to privacy regulations such as GDPR or CCPA.

Auto-complete 73

Auto-complete Machine Learning Neural Network Natural Language Processing 73

Pickl AI

JANUARY 19, 2025

Data warehousing involves the systematic collection, storage, and organisation of large volumes of data from various sources into a centralized repository, designed to support efficient querying and reporting for decision-making purposes. It ensures data quality, consistency, and accessibility over time.

Data Mining 52

Data Mining Algorithm Data Quality Data Scientist 52

ODSC - Open Data Science

APRIL 25, 2025

Unfortunately, the current landscape of our consuming systems, especially business intelligence tools, just wont work withAPIs. They offer the ability to specify a schema, expected values, and even such things as data quality expectations. These specifications are kept close to the code and versioned with it.

Software Engineer 52

Software Engineer Business Intelligence Data Quality Machine Learning 52

Mlearning.ai

AUGUST 10, 2023

The project I did to land my business intelligence internship — CAR BRAND SEARCH ETL PROCESS WITH PYTHON, POSTGRESQL & POWER BI 1. It is a data integration process that involves extracting data from various sources, transforming it into a consistent format, and loading it into a target system.

Business Intelligence 52

Business Intelligence ETL Data Analysis Python 52

AWS Machine Learning Blog

OCTOBER 9, 2024

The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch , Amazon CloudWatch , AWS Glue Data Quality , Amazon Redshift ML , and Amazon QuickSight. You can review the recommendations and augment rules from over 25 included data quality rules.

Data Quality 92

Data Quality ML Machine Learning ETL 92

IBM Journey to AI blog

OCTOBER 12, 2023

Solutions such as IBM watsonx.governance are specially designed to help: Streamline model processes and accelerate model deployment Detect risks hiding within models before deployment or while in production Ensure data quality is upheld and protect the reliability of AI-driven business intelligence tools that inform an organization’s business (..)

Data Scientist 234

Data Scientist Metadata Explainability Responsible AI 234

IBM Journey to AI blog

DECEMBER 7, 2023

Automated data preparation and cleansing : AI-powered data preparation tools will automate data cleaning, transformation and normalization, reducing the time and effort required for manual data preparation and improving data quality.

Data Analysis 181

Data Analysis AI AI Automation 181

ODSC - Open Data Science

FEBRUARY 24, 2023

Data Quality Now that you’ve learned more about your data and cleaned it up, it’s time to ensure the quality of your data is up to par. With these data exploration tools, you can determine if your data is accurate, consistent, and reliable.

Data Analysis 126

Data Analysis Data Science Business Intelligence Python 126

IBM Journey to AI blog

AUGUST 4, 2023

Risk of data swamps A data swamp is the result of a poorly managed data lake that lacks appropriate data quality and data governance practices to provide insightful learnings, rendering the data useless.

Machine Learning 183

Machine Learning Metadata Automation AI 183

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 157

Metadata Big Data Data Mining ETL 157

Unite.AI

MAY 21, 2024

As a high-performance analytics database provider, Exasol has remained ahead of the curve when it comes to helping businesses do more with less. We help companies transform business intelligence (BI) into better insights with Exasol Espresso, our versatile query engine that plugs into existing data stacks.

Software Development 157

Software Development Business Intelligence ETL Data Quality 157

IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

ETL 234

ETL Metadata AI AI 234

Pickl AI

APRIL 22, 2025

Information is stored only once, reducing storage needs and improving data consistency. This makes it ideal for analytical queries and business intelligence reporting where speed is critical. Query Performance Star Schema Delivers faster query performance since fewer joins are needed.

Data Integration 52

Data Integration ETL Business Intelligence Data Quality 52

Flipboard

MAY 20, 2025

By combining these strategies, the pipeline becomes increasingly adaptivecontinually improving data quality and enabling scalable, metadata-driven insights across the enterprise. Ultimately, this domainaware approach futureproofs your claims processing workflows by harnessing raw, unstructured data as actionable business intelligence.

Metadata 126

Metadata Auto-classification Auto-complete Categorization 126

Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning.

Data Quality 52

Data Quality ETL Machine Learning Business Intelligence 52

AWS Machine Learning Blog

AUGUST 12, 2024

AWS data engineering pipeline The adaptable approach detailed in this post starts with an automated data engineering pipeline to make data stored in Splunk available to a wide range of personas, including business intelligence (BI) analysts, data scientists, and ML practitioners, through a SQL interface.

ML 121

ML Data Quality AI AI 121

Flipboard

MAY 9, 2025

This pattern creates a comprehensive solution that transforms raw social media data into actionable business intelligence (BI) through advanced AI capabilities. 3B Instruct Amazon Bedrock, the system provides tailored marketing content that adds business value. By integrating LLMs such as Anthropics Claude 3.5

LLM 93

LLM Prompt Engineering Prompt Engineer Large Language Models 93

ODSC - Open Data Science

SEPTEMBER 27, 2023

Storage Optimization: Data warehouses use columnar storage formats and indexing to enhance query performance and data compression.

Big Data 98

Big Data Metadata Data Science ETL 98

Pickl AI

AUGUST 11, 2024

Additionally, it addresses common challenges and offers practical solutions to ensure that fact tables are structured for optimal data quality and analytical performance. Introduction In today’s data-driven landscape, organisations are increasingly reliant on Data Analytics to inform decision-making and drive business strategies.

Data Quality 52

Data Quality Business Intelligence ETL Data Integration 52

Pickl AI

OCTOBER 11, 2024

This process is essential in today’s data-driven environment, where vast amounts of data are generated daily. Here are the key reasons why data transformation is important: Enhancing Data Quality Data transformation improves the quality of data by addressing issues such as missing values, duplicates, and inconsistent formats.

Data Quality 52

Data Quality Data Integration Business Intelligence Artificial Intelligence 52

Pickl AI

AUGUST 9, 2024

Real-world examples illustrate their application, while tools and technologies facilitate effective hierarchical data management in various industries. Data Quality Issues Inconsistent or incomplete data can hinder the effectiveness of hierarchies. What Are Common Challenges When Implementing Hierarchies?

ETL 52

ETL Business Intelligence Data Quality Data Analysis 52