datapine

JANUARY 31, 2022

Using business intelligence and analytics effectively is the crucial difference between companies that succeed and companies that fail in the modern environment. Experience the power of Business Intelligence with our 14-days free trial! Why Is Business Intelligence So Important?

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

CIO Business Intelligence

NOVEMBER 13, 2023

Inadequate data management and governance Data is at the heart of digital transformation, and companies that don’t have adequate data management processes in place are likely to struggle. Ensuring data quality, privacy, and security is essential.

Digital Transformation 136

Digital Transformation Cost-Benefit Experimentation Measurement 136

CIO Business Intelligence

JUNE 14, 2023

Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications. The number of data analytics certs is expanding rapidly.

Big Data 123

Big Data Data Analytics Analytics Predictive Modeling 123

Ontotext

JULY 6, 2023

Connected Inventory Ontotext’s Connected Inventory integrates data from various sources, which enables efficient reporting. It incorporates the knowledge of Subject Matter Experts and ensures accurate sentiment measurements. Experimentation with different technical analysis services becomes possible.

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

Occam's Razor

AUGUST 13, 2012

The questions reveal a bunch of things we used to worry about, and continue to, like data quality and creating data driven cultures. Yehoshua Coren: Best ways to measure user behavior in a multi-touch, multi-device digital world. What's possible to measure. What's not possible to measure.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66