IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 85

Data Science Data Analytics Prescriptive Analytics Analytics 85

datapine

AUGUST 14, 2019

Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Cloudera

JANUARY 5, 2021

In today’s retail environment, retailers realize that building demand forecasts simply based upon historical transaction, promo, and pricing data alone is not good enough. Data today has a shelf life much like produce and needs to be updated in real-time to be relevant. Including new data sources like demand signals (e.g.

Forecasting 90

Forecasting Cost-Benefit Sales Unstructured Data 90

Smart Data Collective

FEBRUARY 2, 2021

In general, Big Data can help businesses in all fields – it’s not something reserved for tech companies. However, some industries have more to benefit from Big Data than others and have reached impressive milestones because data science and data analytics have helped them streamline their operations.

Big Data 85

Big Data Recreation/Entertainment Cost-Benefit Finance 85

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. There are many types of data pipelines, and all of them include extract, transform, load (ETL) to some extent.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Rita Sallam

JULY 22, 2016

Summary of Differences Between Traditional and Modern Business Intelligence Platforms by Analytic Workflow Component. Q2: Would you consider Sisense better than others in handling big and unstructured data? Again, check out the Critical Capabilities for BI and Analytic Platforms for how each vendor compares.

Analytics 40

Analytics Business Intelligence Metadata Statistics 40