Corinium

JUNE 6, 2019

Are you seeing currently any specific issues in the Insurance industry that should concern Chief Data & Analytics Officers? Lack of clear, unified, and scaled data engineering expertise to enable the power of AI at enterprise scale. The data will enable companies to provide more personalized services and product choices.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

Smart Data Collective

JULY 9, 2023

E-commerce businesses around the world are focusing more heavily on data analytics. There are many ways that data analytics can help e-commerce companies succeed. One benefit is that they can help with conversion rate optimization. One report found that global e-commerce brands spent over $16.7 billion on analytics last year.

Optimization 74

Optimization Data Analytics Analytics Data-driven 74

IBM Big Data Hub

NOVEMBER 3, 2023

As a result, I see access to real-time data as a necessary foundation for building business agility and enhancing decision making. Stream processing is at the core of real-time data. Apache Kafka streams get data to where it needs to go, but these capabilities are not maximized when Apache Kafka is deployed in isolation.

Cost-Benefit 78

Cost-Benefit Experimentation Testing Digital Transformation 78

Smart Data Collective

OCTOBER 13, 2021

AI technology moves innovation forward by boosting tinkering and experimentation, accelerating the innovation process. It also allows companies to experiment with new concepts and ideas in different ways without relying only on lab tests. One of the biggest benefits of AI is that it has led to new breakthroughs in automation.

Technology 122

Technology Cost-Benefit Experimentation Machine Learning 122

Domino Data Lab

OCTOBER 14, 2020

Guest post by Jeff Melching, Distinguished Engineer / Chief Architect Data & Analytics. We’ve developed a model-driven software platform, called Climate FieldView , that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99

Domino Data Lab

AUGUST 6, 2021

An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. With a framework and Enterprise MLOps, organizations can manage data science at scale and realize the benefits of Model Risk Management that are received by a wide range of industry verticals.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

Alation

SEPTEMBER 28, 2022

The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In essence, DataOps is a practice that helps organizations manage and govern data more effectively. What exactly is DataOps ?

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

CIO Business Intelligence

JULY 31, 2023

To allow or not According to various news reports, some big-name companies initially blocked generative AI tools such as ChatGPT for various reasons, including concerns about protecting proprietary data. 1 question now is to allow or not allow,” says Mir Kashifuddin, data risk and privacy leader with the professional services firm PwC US.

Strategy 95

Strategy Risk Enterprise Risk Management 95

Cloudera

JANUARY 30, 2019

It’s official – Cloudera and Hortonworks have merged , and today I’m excited to announce the availability of Cloudera Data Science Workbench (CDSW) for Hortonworks Data Platform (HDP). Trusted by large data science teams across hundreds of enterprises —. Sound familiar? What is CDSW? Install any library or framework (e.g.

Data Science 73

Data Science Machine Learning Experimentation Cost-Benefit 73

datapine

FEBRUARY 4, 2020

Today’s digital data has given the power to an average Internet user a massive amount of information that helps him or her to choose between brands, products or offers, making the market a highly competitive arena for the best ones to survive. First things first – organizing and prioritizing your marketing data.

Reporting 280

Reporting Marketing Advertising Metrics 280

Occam's Razor

OCTOBER 23, 2006

The title of my presentation at the Washington DC Emetrics summit was: Creating a Data Driven Web Decision Making Culture – Lessons, Tips, Insights from a Practitioner. Seven Steps to Creating a Data Driven Decision Making Culture…… Slide 1: Decision Making Landscape. 2 Solve for the Trinity. #

Data-driven 117

Data-driven Metrics KPI Reporting 117

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. Without large amounts of labeled training data solving most AI problems is not possible. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

SEPTEMBER 19, 2011

Then they isolated regions of the country (by city, zip, state, dma pick your fave) into test and control regions. People in the test regions will participate in our hypothesis testing. It is also important to point out that I am keeping the data simple purely to keep communication of the story straightforward.

Measurement 119

Measurement Advertising Testing Marketing 119

Occam's Razor

JULY 6, 2016

Analysts, honestly, make the world go round when it comes to any successful business – yes, data is that important. Today's post is an adjacent mistake: The cardinal sin of spending too much time with data and in reports! But, perhaps I'm at fault for creating the problem of you spending all your time with data.

Testing 86

Testing Marketing Experimentation Cost-Benefit 86

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

CIO Business Intelligence

APRIL 24, 2024

The first use of generative AI in companies tends to be for productivity improvements and cost cutting. But there are only so many costs that can be cut. CIOs are well positioned to cut costs since they’re usually well acquainted with a company’s digital processes, having helped set them up in the first place.

Sales 124

Sales Finance Experimentation Cost-Benefit 124

Domino Data Lab

AUGUST 26, 2019

Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. Data scientists and researchers ascertain predictive accuracy of models using different techniques, methodologies, and settings, including model parameters and hyperparameters. Introduction.

Testing 79

Testing Modeling Machine Learning Metrics 79

Occam's Razor

AUGUST 12, 2013

There are few things more complicated in analytics (all analytics, big data and huge data!) There is lots of missing data. And as if that were not enough, there is lots of unknowable data. And you are telling me that the Cost Per Acquisition for my display campaigns is not $201 but rather a lowly $155? " low.

Modeling 161

Modeling Optimization Marketing Interactive 161

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. This blog post introduces the notions of representational uncertainty and interventional uncertainty to paint a fuller picture of what the practicing data scientist is up against. or "does this product change benefit users?"

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

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. Then you build a massive data store that you can query for data to analyze. They also reveal things that starting to become scary (Privacy! EU Cookies!)

Modeling 124

Modeling Metrics Data Quality Data-driven 124

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

by NIALL CARDIN, OMKAR MURALIDHARAN, and AMIR NAJMI When working with complex systems or phenomena, the data scientist must often operate with incomplete and provisional understanding, even as she works to advance the state of knowledge. There has been debate as to whether the term “data science” is necessary. Some don’t see the point.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Occam's Razor

SEPTEMBER 15, 2014

In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation. When you analyze the data in Google Analytics (or Adobe or WebTrends or Webtrekk), this data will be in your Campaigns folder waiting for you to some pretty magnificent analysis.

Metrics 141

Metrics Reporting Analytics Marketing 141

DataRobot Blog

MARCH 16, 2023

Data scientists run experiments. To work effectively, data scientists need agility in the form of access to enterprise data, streamlined tooling, and infrastructure that just works. We’ve tightened the loop between ML data prep , experimentation and testing all the way through to putting models into production.

Modeling 52

Modeling Experimentation Machine Learning Data-driven 52

The Unofficial Google Data Science Blog

JUNE 30, 2016

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects.

Statistics 44

Statistics Optimization Modeling Experimentation 44

DataRobot Blog

MARCH 7, 2023

DataRobot on Azure accelerates the machine learning lifecycle with advanced capabilities for rapid experimentation across new data sources and multiple problem types. This generates reliable business insights and sustains AI-driven value across the enterprise.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

IBM Big Data Hub

SEPTEMBER 25, 2023

The magic behind Uber’s data-driven success Uber, the ride-hailing giant, is a household name worldwide. But what most people don’t realize is that behind the scenes, Uber is not just a transportation service; it’s a data and analytics powerhouse. Consider the magnitude of Uber’s footprint.

OLAP 93

OLAP Data Lake Data-driven Snapshot 93