Dataversity
JULY 21, 2022
Despite its many benefits, the emergence of high-performance machine learning systems for augmented analytics over the last 10 years has led to a growing “plug-and-play” analytical culture, where high volumes of opaque data are thrown arbitrarily at an algorithm until it yields useful business intelligence.
Tableau
APRIL 18, 2022
We’ve infused our values into our platform, which supports data fabric designs with a data management layer right inside our platform, helping you break down silos and streamline support for the entire data and analytics life cycle. . Analytics data catalog. Data quality and lineage.
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Tableau
APRIL 18, 2022
We’ve infused our values into our platform, which supports data fabric designs with a data management layer right inside our platform, helping you break down silos and streamline support for the entire data and analytics life cycle. . Analytics data catalog. Data quality and lineage.
Dataconomy
SEPTEMBER 4, 2023
Leveraging third-party data : Incorporating external data sources, such as weather, social media trends, and market reports, enriches your analysis, providing a more comprehensive understanding of customer behavior and market dynamics. AI and augmented analytics assist users in navigating complex data sets, offering valuable insights.
Dataconomy
AUGUST 24, 2023
In the modern era of data-driven decision-making, business intelligence projects have become the cornerstone for organizations aiming to harness their data for strategic insights. The BI landscape continues to evolve, with innovative projects taking center stage. The year 2023 brings forth a multitude of trends that will shape the BI.
DataRobot Blog
APRIL 1, 2018
In Rita Sallam’s July 27 research, Augmented Analytics , she writes that “the rise of self-service visual-bases data discovery stimulated the first wave of transition from centrally provisioned traditional BI to decentralized data discovery.” Sallam | Cindi Howson | Carlie J.
Alation
AUGUST 26, 2021
As lakes of data become oceans, locating that which is trustworthy and reliable grows more difficult — and important. Indeed, as businesses attempt to scale AI and BI programs, small issues around data quality can transmogrify into massive challenges. Data quality. Data governance. Data profiling.
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