CIO Business Intelligence

DECEMBER 13, 2023

The evolution of AI and the use of structured and unstructured data When discriminative AI rose to prominence in sectors such as banking, healthcare, retail, and manufacturing, it was primarily trained on and used to analyze, classify, or make predictions about unstructured data.

Unstructured Data 98

Unstructured Data IoT Metadata Manufacturing 98

CIO Business Intelligence

APRIL 4, 2024

Like other CIOs, Katrina Redmond has been inundated with opportunities to deploy AI that promise to speed business and operations processes, and optimize workflows. At Eaton, for example, an AI-based sales forecasting tool has the potential to boost productivity dramatically. We want to maintain discipline and go deep.”

Enterprise 134

Enterprise Cost-Benefit Forecasting Sales 134

Sisense

MARCH 14, 2021

Looking at the diagram, we see that Business Intelligence (BI) is a collection of analytical methods applied to big data to surface actionable intelligence by identifying patterns in voluminous data. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data.

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

Cloudera

DECEMBER 15, 2017

Data volumes from both inside as well as outside the manufacturing process continue to grow. The makeup of data changes too, and is more and more unstructured. Manufacturing can move from working with approximations to insight based on actual data, as it happens. A modern platform based on open source.

Manufacturing 40

Manufacturing IoT Machine Learning Data-driven 40

Sisense

MAY 11, 2021

We’re going to nerd out for a minute and dig into the evolving architecture of Sisense to illustrate some elements of the data modeling process: Historically, the data modeling process that Sisense recommended was to structure data mainly to support the BI and analytics capabilities/users.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Jet Global

MAY 9, 2024

Real-Time Analytics Pipelines : These pipelines process and analyze data in real-time or near-real-time to support decision-making in applications such as fraud detection, monitoring IoT devices, and providing personalized recommendations. As data flows into the pipeline, it is processed in real-time or near-real-time.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52