DataKitchen

APRIL 13, 2021

This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Companies that implement DataOps find that they are able to reduce cycle times from weeks (or months) to days, virtually eliminate data errors, increase collaboration, and dramatically improve productivity.

Testing 300

Testing Machine Learning Consulting Data Quality 300

CIO Business Intelligence

FEBRUARY 21, 2024

One is knowledge of the emerging mega trends in technology — data, AI, and machine learning — and the other is understanding organizational culture needed to advance the technology goals and to inspire contributors,” he says. We developed a model to predict student outcomes based on metrics from historical evidence,” he says. “We

Recreation/Entertainment 127

Recreation/Entertainment Experimentation Modeling Data-driven 127

DataRobot Blog

OCTOBER 18, 2022

Organizations are looking for AI platforms that drive efficiency, scalability, and best practices, trends that were very clear at Big Data & AI Toronto. DataRobot Booth at Big Data & AI Toronto 2022. Monitoring and Managing AI Projects with Model Observability.

Big Data 96

Big Data Forecasting Experimentation Metrics 96

CIO Business Intelligence

NOVEMBER 21, 2023

After all, 41% of employees acquire, modify, or create technology outside of IT’s visibility , and 52% of respondents to EY’s Global Third-Party Risk Management Survey had an outage — and 38% reported a data breach — caused by third parties over the past two years.

IT 131

IT Risk Cost-Benefit Risk Management 131

Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation.

Metrics 141

Metrics Reporting Analytics Marketing 141

DataRobot Blog

FEBRUARY 28, 2023

Integrating different systems, data sources, and technologies within an ecosystem can be difficult and time-consuming, leading to inefficiencies, data silos, broken machine learning models, and locked ROI. Exploratory Data Analysis After we connect to Snowflake, we can start our ML experiment.

Data Processing 72

Data Processing Experimentation Machine Learning Modeling 72

Occam's Razor

JANUARY 7, 2014

First… it is important to realize that big data's big imperative is driving big action. 7: 25% of all analytical effort is dedicated to data visualization/enhancing data's communicative power. #6: The organization functions off a clearly defined Digital Marketing & Measurement Model. #1.

Data-driven 140

Data-driven Consulting Reporting Marketing 140

datapine

JANUARY 31, 2022

Because things are changing and becoming more competitive in every sector of business, the benefits of business intelligence and proper use of data analytics are key to outperforming the competition. BI software uses algorithms to extract actionable insights from a company’s data and guide its strategic decisions.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Occam's Razor

AUGUST 12, 2013

There are few things more complicated in analytics (all analytics, big data and huge data!) than multi-channel attribution modeling. There is lots of missing data. And as if that were not enough, there is lots of unknowable data. You'll know how to use the good model, even if it is far from perfect.

Modeling 161

Modeling Optimization Marketing Interactive 161

Sisense

MARCH 6, 2019

Every year there’s high anticipation to see what key message Gartner will present in the yearly Data & Analytics Summits. It’s always fun and insightful to be able to talk to so many CDOs, CIOs, data and BI professionals within 2.5 At Sisense we’ve been preaching for BI prototyping and experimentation for quite a while now.

Data Analytics 75

Data Analytics Analytics Experimentation Data-driven 75

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

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

Cloudera

APRIL 9, 2021

This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

O'Reilly on Data

APRIL 19, 2021

During the first weeks of February, we asked recipients of our Data & AI Newsletter to participate in a survey on AI adoption in the enterprise. The second-most significant barrier was the availability of quality data. Relatively few respondents are using version control for data and models. Respondents.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

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. Yehoshua I've covered this topic in detail in this blog post: Multi-Channel Attribution: Definitions, Models and a Reality Check. That means: All of these metrics are off.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

AWS Big Data

NOVEMBER 14, 2023

Data is a key strategic asset for every organization, and every company is a data business at its core. However, in many organizations, data is typically spread across a number of different systems such as software as a service (SaaS) applications, operational databases, and data warehouses.

IoT 56

IoT Data-driven Data Lake Data Strategy 56

Domino Data Lab

JUNE 10, 2020

Today, we announced the latest release of Domino’s data science platform which represents a big step forward for enterprise data science teams. I’m also proud to announce an exciting new product: Domino Model Monitor (DMM). Domino’s best-in-class Workbench is now even more powerful for data scientists.

Data Science 82

Data Science Enterprise Experimentation Modeling 82

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.

Management 110

Management Machine Learning Experimentation Metrics 110

AWS Big Data

JULY 20, 2023

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

Data Lake 85

Data Lake Analytics Snapshot Optimization 85

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. The inherent capabilities of AI–to process vast amounts of data and use learned intelligence to make decisions with extraordinary speed–enable opportunities uncovered through digital listening.

Advertising 87

Advertising Recreation/Entertainment Experimentation Enterprise 87

Octopai

OCTOBER 26, 2022

Data-driven organizations are a bad idea. Using data to drive your organization is wonderful. But data, at best, can only be a powerful vehicle, or a reliable GPS system. Except sometimes we call organizations “data-driven” when really the data is driving them up the wall. And it should be.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

Occam's Razor

AUGUST 22, 2011

Be data driven?" " ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. Key To Your Digital Success: Web Analytics Measurement Model. Six Rules For Creating A Data Driven Boss! Be data driven?" How can my company become great?" How to focus?"

KPI 124

KPI Metrics Measurement ROI 124

Occam's Razor

JULY 24, 2018

work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps , metrics ladders of awesomeness , and… so… much… more. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more. The Impact Matrix.

Analytics 93

Analytics Metrics Strategy Measurement 93

Corinium

APRIL 25, 2019

Ahead of the Chief Data Analytics Officers & Influencers, Insurance event we caught up with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity to discuss how the industry is evolving. The last 10+ years or so have seen Insurance become as data-driven as any vertical industry.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science. Introduction.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Occam's Razor

DECEMBER 12, 2016

There is, almost literally, an unlimited number of things you could focus on to create a high impact data-influenced organization. I was asked a few weeks back: " What companies should we proactively help with analytics, for free, so that they can make smarter data-influenced decisions ?" Data quality plays a role into this.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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.

Testing 79

Testing Modeling Machine Learning Metrics 79

O'Reilly on Data

MAY 14, 2020

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

Management 363

Management Experimentation B2B Machine Learning 363

Occam's Razor

MARCH 23, 2010

Javascript tag driven click data processed in the cloud provided through a web based front end that allows you to segment and create meaningful views of the data unique to you. Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. This instant.

Analytics 118

Analytics Consulting Metrics Experimentation 118

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”.

Management 244

Management Metrics Machine Learning Modeling 244

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. Vignette: Data Science at fluff.ai

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

DataRobot Blog

MARCH 16, 2023

To deliver on this new approach, one that we are calling Value-Driven AI , we set out to design new and enhanced platform capabilities that enable customers to realize value faster. Best-Practice Compliance and Governance: Businesses need to know that their Data Scientists are delivering models that they can trust and defend over time.

Experimentation 98

Experimentation Data-driven Modeling Metadata 98

Occam's Razor

JULY 26, 2010

Suggestions that might increase the probability that you'll bump into things that might be insightful, and communicate data more effectively. The data is easily available in the web analytics tool so why not. Get the data out of the crm / erp / "backend" system. PALM: People Against Lonely Metrics].

Metrics 96

Metrics Measurement Dashboards Reporting 96

Occam's Razor

DECEMBER 9, 2009

It is rare for me to work with a organization where the root cause for their faith based decision making (rather than data driven) was not the org structure. Surprisingly it is often not their will to use data, that is there in many cases. Chapter 14: HiPPOs, Ninjas, and the Masses: Creating a Data-Driven Culture.

Analytics 95

Analytics Data-driven Marketing Sales 95

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

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