DataKitchen

JANUARY 17, 2022

A large pharmaceutical Business Analytics (BA) team struggled to provide timely analytical insight to its business customers. However, the BA team spent most of its time overcoming error-prone data and managing fragile and unreliable analytics pipelines. . Data is not static. The Challenge.

Experimentation 130

Experimentation Data Lake Predictive Modeling Marketing 130

Cloudera

NOVEMBER 18, 2020

Merck KGaA, Darmstadt, Germany, is a leading science and technology company, operating across healthcare, life science, and performance materials business areas. The Advanced Analytics team supporting the businesses of Merck KGaA, Darmstadt, Germany was able to establish a data governance framework within its enterprise data lake.

Internet Publishing and Broadcasting 123

Internet Publishing and Broadcasting Data-driven Broadcasting Digital Transformation 123

IBM Big Data Hub

SEPTEMBER 19, 2023

This iterative process is known as the data science lifecycle, which usually follows seven phases: Identifying an opportunity or problem Data mining (extracting relevant data from large datasets) Data cleaning (removing duplicates, correcting errors, etc.) Watsonx comprises of three powerful components: the watsonx.ai

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

Cloudera

MAY 12, 2021

Data Security & Governance: Merck KGaA, Darmstadt, Germany — Established a data governance framework with their data lake to discover, analyze, store, mine, and govern relevant data. Industry Transformation: Telkomsel — Ingesting 25TB of data daily to provide advanced customer analytics in real-time .

Digital Transformation 70

Digital Transformation Machine Learning Optimization Data Lake 70

Smart Data Collective

AUGUST 7, 2019

New England College talks in detail about the role of big data in the field of business. They have highlighted some of the biggest applications, as well as some of the precautions businesses need to take, such as navigating the death of data lakes and understanding the role of the GDPR. Creating predictive models.

Data Analytics 111

Data Analytics Analytics Big Data Advertising 111

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52