Data Analytics Data Integration Data Quality Deep Learning 
Smarten
APRIL 19, 2024
The value of an AI-focused analytics solution can only be fully realized when a business has ensured data quality and integration of data sources, so it will be important for businesses to choose an analytics solution and service provider that can help them achieve these goals.
CIO Business Intelligence
JUNE 13, 2022
In some parts of the world, companies are required to host conversational AI applications and store the related data on self-managed servers rather than subscribing to a cloud-based service. Data integration can also be challenging and should be planned for early in the project. . Intel® Technologies Move Analytics Forward.
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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. OwlDQ — Predictive data quality.
CIO Business Intelligence
MARCH 22, 2022
Then virtualize your data to allow business users to conduct aggregated searches and analyses using the business intelligence or data analytics tools of their choice. . Set up unified data governance rules and processes. With data integration comes a requirement for centralized, unified data governance and security.
Smart Data Collective
OCTOBER 13, 2021
As we have already said, the challenge for companies is to extract value from data, and to do so it is necessary to have the best visualization tools. Over time, it is true that artificial intelligence and deep learning models will be help process these massive amounts of data (in fact, this is already being done in some fields).
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