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. Understanding E-commerce Conversion Rates There are a number of metrics that data-driven e-commerce companies need to focus on. billion on analytics last year.

Optimization 74

Optimization Data Analytics Analytics Data-driven 74

DataKitchen

MARCH 8, 2023

These three objectives are interconnected and essential to the success of any data team. Delivering insight to customers without error is critical to the success of any data team. The team must ensure that the data they are working with is clean and accurate and that the analysis created from it is rigorous and reliable.

Analytics 130

Analytics Manufacturing Data-driven Data Analytics 130

CIO Business Intelligence

JUNE 14, 2023

Data and big data analytics are the lifeblood of any successful business. Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications.

Big Data 127

Big Data Data Analytics Analytics Predictive Modeling 127

O'Reilly on Data

MARCH 31, 2020

AI products are automated systems that collect and learn from data to make user-facing decisions. All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. Why AI software development is different.

Management 332

Management Machine Learning Experimentation Metrics 332

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

Occam's Razor

SEPTEMBER 19, 2011

This: You understand all the environmental variables currently in play, you carefully choose more than one group of "like type" subjects, you expose them to a different mix of media, measure differences in outcomes, prove / disprove your hypothesis (DO FACEBOOK NOW!!!), Measuring Incrementality: Controlled Experiments to the Rescue!

Measurement 119

Measurement Advertising Testing Marketing 119

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

Ontotext

JULY 6, 2023

In today’s fast changing environment, enterprises that have transitioned from being focused on applications to becoming data-driven gain a significant competitive edge. There are four groups of data that are naturally siloed: Structured data (e.g., Transaction and pricing data (e.g.,

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

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 307

Testing Machine Learning Consulting Data Quality 307

Rocket-Powered Data Science

JULY 7, 2019

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics).

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Ontotext

NOVEMBER 2, 2023

Achieving this advantage is dependent on their ability to capture, connect, integrate, and convert data into insight for business decisions and processes. This is the goal of a “data-driven” organization. We call this the “ Bad Data Tax ”. This is partly because integrating and moving data is not the only problem.

IT 69

IT Cost-Benefit Data-driven Technology 69

DataRobot Blog

AUGUST 10, 2022

Change is happening fast across the NHS with the focus squarely on harnessing the huge amount of data the NHS generates — to drive forward the transformation programmes needed to address the backlog for elective care and growing demands for services. Resetting urgent care performance and delivery.

Forecasting 52

Forecasting Experimentation Data-driven Risk 52

CIO Business Intelligence

FEBRUARY 21, 2023

The firm’s connected brewery IoT platform, for instance, is being used for data ingestion and edge computing in breweries, enabling local teams to analyze, adjust, test and optimize production processes, with this in-turn allowing operations to leverage real-time and historical data to support the workers on the shop floor.

Digital Transformation 102

Digital Transformation Sales Experimentation Optimization 102

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 95

Advertising Recreation/Entertainment Experimentation Enterprise 95

CIO Business Intelligence

JUNE 28, 2023

For all of generative AI’s allure, large enterprises are taking their time, many outright banning tools like ChatGPT over concerns of accuracy, data protection, and the risk of regulatory backlash. Experimentation with a use case driven approach. By that measure, you will indeed have done better than you thought.

Enterprise 52

Enterprise Experimentation Risk Data-driven 52

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

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 55

IoT Data-driven Data Lake Data Strategy 55

AWS Big Data

NOVEMBER 2, 2023

GoDaddy is a data-driven company, and getting meaningful insights from data helps us drive business decisions to delight our customers. As a part of this vision, GoDaddy’s Data & ML Platform team plans to use EMR Serverless as one of the compute solutions under the hood.

Cost-Benefit 84

Cost-Benefit Big Data Testing Optimization 84

Ontotext

MAY 18, 2020

Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

Sisense

NOVEMBER 23, 2020

The global pandemic has driven home the fact that data is vital to the success of every organization. Sisense recently surveyed over 460 companies across Australia and New Zealand to dig into their data and analytics usage and future plans. Who is leading the way?

Analytics 98

Analytics Data-driven Marketing Reporting 98

Data Science and Beyond

DECEMBER 23, 2018

In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. Hernán and Robins are both epidemiologists, which means they often have to deal with data with strong limitations on sample size and feasibility of experiments. Hence, the book is full of practical examples.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

Occam's Razor

AUGUST 22, 2011

Be data driven?" Key To Your Digital Success: Web Analytics Measurement Model. Six Rules For Creating A Data Driven Boss! Be data driven?" " Measuring Incrementality: Controlled Experiments to the Rescue! Barriers To An Effective Web Measurement Strategy [+ Solutions!].

KPI 124

KPI Metrics Measurement ROI 124

Sisense

JANUARY 21, 2020

Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. How can good data lead to faulty conclusions? How does this happen? 9/10 = 90%.

Testing 104

Testing Risk Data-driven Statistics 104

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

Ontotext

APRIL 3, 2024

Joseph Hilger : People are starting to understand that knowledge graphs are not just a tool for storing data and information. I need something that defines what those entities are and can align them with the data.” The other use case where graphs are exploding is what Gartner calls a data fabric. What are your thoughts about it?

Technology 59

Technology Risk Digital Transformation Measurement 59

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

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

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

Domino Data Lab

NOVEMBER 14, 2019

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. Yet, despite the complexity (or because of it), data scientists and researchers curate and use different languages, tools, packages, techniques, and frameworks to tackle the problem they are trying to solve.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

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. First, you figure out what you want to improve; then you create an experiment; then you run the experiment; then you measure the results and decide what to do.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

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

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

Sisense

JULY 15, 2020

Healthy Data is your window into how data can help organizations address this crisis. COVID-19 required a worldwide coordinated response of medical professionals, data teams, logistics organizations, and a whole host of other experts to try to flatten the curve, improve treatments, and ultimately develop lasting remedies.

Analytics 90

Analytics Modeling Visualization Dashboards 90

Domino Data Lab

MAY 8, 2019

For example, common practices for collecting data to build training datasets tend to throw away valuable information along the way. The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. ML model interpretability and data visualization. back to the structure of the dataset.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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

JULY 24, 2018

The result is analytical strategies that are uninformed by reality, and driven new tool features, random expert recommendations and shiny objects ( OMG we have to get offline attribution! ). To name a few: Digital Marketing & Measurement Model | Analytics Ecosystem | Web Analytics 2.0. Then one day, a magic moment.

Analytics 93

Analytics Metrics Strategy Measurement 93

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