Birst BI

OCTOBER 30, 2018

Fortunately, advances in analytic technology have made the ability to see reliably into the future a reality. This may involve integrating different technologies, like cloud sources, on-premise databases, data warehouses and even spreadsheets. Add the predictive logic to the data model.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

Data Lake 117

Data Lake Unstructured Data Data Warehouse Data Quality 117

CIO Business Intelligence

MARCH 22, 2022

One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Another option is a data warehouse, which stores processed and refined data. Just starting out with analytics?

Analytics 112

Analytics Key Performance Indicator Data Warehouse Data-driven 112

datapine

AUGUST 29, 2022

And shows how big data and the advances in analytical technologies are shaping the way the world is perceived. 2) Designing Data-Intensive Applications by Martin Kleppman. Best for: the seasoned BI professional who is ready to think deep and hard about important issues in data analytics and big data.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

CIO Business Intelligence

JANUARY 20, 2023

It provides rapid, direct access to trusted data for data scientists, business analysts, and others who need data to drive business value. Focus on Outcomes Analytics and AI hold the promise of driving better business insights from data warehouses, streams, and lakes. Just starting out with analytics?

Analytics 136

Analytics Key Performance Indicator Unstructured Data IoT 136

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Introduction.

Data Processing 91

Data Processing Data Warehouse Enterprise Visualization 91

Jet Global

APRIL 18, 2024

Embedded Analytics Challenges – and How to Overcome Them Despite the benefits, it can still be difficult to get buy-in for BI and embedded analytics for your SaaS applications due to challenges like infrastructure costs, safety concerns, as well as uptime and scaling. Here’s how. Infrastructure costs.

Marketing 52

Marketing Analytics Data Processing Cost-Benefit 52

Jet Global

FEBRUARY 21, 2024

However, it’s just as important to demonstrate your application’s ability to support data literacy within leadership, many of whom are not technically skilled. Embedded analytics technology with features like customizable dashboards offer leadership and non-technical users to translating data insights into action.

Analytics 52

Analytics Dashboards Predictive Analytics Software 52

Jet Global

DECEMBER 4, 2023

As analytics technology evolves, so do user needs and expectations. Many customers approach us hoping to boost their application’s analytics capabilities, which are often struggling to meet user demand.

Analytics 52

Analytics Reporting Dashboards Software 52