Smart Data Collective

JULY 9, 2023

Collecting Relevant Data for Conversion Rate Optimization Here is some vital data that e-commerce businesses need to collect to improve their conversion rates. Identifying Key Metrics for Conversion Rate Optimization Data collection and analysis are both essential processes for optimizing your conversion rate.

Optimization 74

Optimization Data Analytics Analytics Data-driven 74

CIO Business Intelligence

MAY 16, 2023

It surpasses blockchain and metaverse projects, which are viewed as experimental or in the pilot stage, especially by established enterprises. Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data.

Advertising 100

Advertising Recreation/Entertainment Experimentation Enterprise 100

Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

AWS Big Data

JULY 3, 2023

Look – ahead bias – This is a common challenge in backtesting, which occurs when future information is inadvertently included in historical data used to test a trading strategy, leading to overly optimistic results. To comply with licensing considerations, we cannot provide a sample of the ETF constituents data.

Snapshot 86

Snapshot Data Lake Testing Strategy 86

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Keep in mind that data science is fundamentally interdisciplinary. Let’s look through some antidotes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Cloudera

JUNE 6, 2019

When a mix of batch, interactive, and data serving workloads are added to the mix, the problem becomes nearly intractable. We sometimes refer to this as splitting “dev/test” from “production” workloads, but we can generalize the approach by referring to the overall priority of the workload for the business.

Metadata 85

Metadata Data Lake Optimization Strategy 85