AWS Big Data

JUNE 28, 2023

Challenges Customers across industries today are looking to increase revenue and customer engagement by implementing near-real time analytics use cases like personalization strategies, fraud detection, inventory monitoring, and many more. He has 17 years of experience architecting, building, leading, and maintaining big data platforms.

Data Warehouse 88

Data Warehouse Analytics Metrics Dashboards 88

Smart Data Collective

OCTOBER 17, 2022

This proliferation of data and the methods we use to safeguard it is accompanied by market changes — economic, technical, and alterations in customer behavior and marketing strategies , to mention a few. zettabytes of data in 2020, a tenfold increase from 6.5 zettabytes in 2012. What’s causing the data explosion?

Big Data 104

Big Data Data-driven Recreation/Entertainment Data Governance 104

Domino Data Lab

AUGUST 8, 2019

Another key point: troubleshooting edge cases for models in production—which is often where ethics and data meet, as far as regulators are concerned—requires much more sophistication in statistics than most data science teams tend to have. It’s a quick way to clear the room. machine learning?

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

AWS Big Data

MARCH 21, 2024

Challenges Customers across industries today are looking to use data to their competitive advantage and increase revenue and customer engagement by implementing near real time analytics use cases like personalization strategies, fraud detection, inventory monitoring, and many more. Choose Create policy.

Data Warehouse 90

Data Warehouse Metrics Statistics Optimization 90

Sisense

DECEMBER 11, 2019

With Itzik’s wisdom fresh in everyone’s minds, Scott Castle, Sisense General Manager, Data Business, shared his view on the role of modern data teams. Scott whisked us through the history of business intelligence from its first definition in 1958 to the current rise of Big Data. The impact on customers. A true unicorn.

Data Lake 80

Data Lake Big Data Sales Data-driven 80

The Unofficial Google Data Science Blog

MARCH 31, 2016

Far from hypothetical, we have encountered these issues in our experiences with "big data" prediction problems. We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all. Cambridge University Press, (2012). [4]

Modeling 40

Modeling Statistics Advertising Testing 40

Domino Data Lab

MARCH 3, 2019

Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. There are essentially four types encountered: image/video, audio, text, and structured data. That represents a significant shift in strategy and cloud providers stand to benefit.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. Data was expensive to gather, and therefore decisions to collect data were generally well-considered. Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data.

Experimentation 61

Experimentation Testing Statistics Metrics 61

datapine

MAY 17, 2021

In the digital age, those who can squeeze every single drop of value from the wealth of data available at their fingertips, discovering fresh insights that foster growth and evolution, will always win on the commercial battlefield. Moreover, 83% of executives have pursued big data projects to gain a competitive edge.

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161