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The importance of data ingestion and integration for enterprise AI

IBM Big Data Hub

Currently, no standardized process exists for overcoming data ingestion’s challenges, but the model’s accuracy depends on it. Increased variance: Variance measures consistency. Insufficient data can lead to varying answers over time, or misleading outliers, particularly impacting smaller data sets.

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Drive Growth with Data-Driven Strategies: Introducing Zenia Graph’s Salesforce Accelerator

Ontotext

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. Zenia Graph’s Salesforce Accelerator makes this a reality.

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How GamesKraft uses Amazon Redshift data sharing to support growing analytics workloads

AWS Big Data

As Gameskraft’s portfolio of gaming products increased, it led to an approximate five-times growth of dedicated data analytics and data science teams. Consequently, there was a fivefold rise in data integrations and a fivefold increase in ad hoc queries submitted to the Redshift cluster.

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Estes Express shifts gears on customer experience by streamlining data operations

CIO Business Intelligence

To fuel self-service analytics and provide the real-time information customers and internal stakeholders need to meet customers’ shipping requirements, the Richmond, VA-based company, which operates a fleet of more than 8,500 tractors and 34,000 trailers, has embarked on a data transformation journey to improve data integration and data management.

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An AI Chat Bot Wrote This Blog Post …

DataKitchen

DataOps observability involves the use of various tools and techniques to monitor the performance of data pipelines, data lakes, and other data-related infrastructure. This can include the use of tools for data integration and transformation, as well as technologies for managing and monitoring data-related systems and processes.

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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. What are the four types of data analytics? It is often used to understand how customers feel about a brand, product, or service.

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Agent Swarms – an evolutionary leap in intelligent automation

CIO Business Intelligence

Gather/Insert data on market trends, customer behavior, inventory levels, or operational efficiency. IoT, Web Scraping, API, IDP, RPA Data Processing Data Pipelines and Analysis Layer Employ data pipelines with algorithms to filter, sort, and interpret data, transforming raw information into actionable insights.

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