IBM Big Data Hub

MARCH 27, 2024

Some companies attempt to estimate Scope 3 emissions by collecting data from suppliers and manually categorizing data, but progress is hindered by challenges such as large supplier base, depth of supply chains, complex data collection processes and substantial resource requirements.

Experimentation 77

Experimentation Modeling Deep Learning Data-driven 77

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

Cloudera

APRIL 9, 2021

The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Data Collection – streaming data.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

CIO Business Intelligence

MARCH 21, 2022

Data scientists are becoming increasingly important in business, as organizations rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies. Data scientist job description. Semi-structured data falls between the two.

Unstructured Data 136

Unstructured Data Data Analytics Analytics Structured Data 136

CIO Business Intelligence

AUGUST 16, 2023

In order to do that, a digital transformation was required, and when it comes to information provision, there wasn’t much, so we put in place basic platforms to handle data, and developed a cloud architecture for infrastructure and applications.” “Often a business area, a service, or a product is digitized but not the entire company.

Risk 94

Risk Cost-Benefit Digital Transformation Internet of Things 94

CIO Business Intelligence

MAY 31, 2022

Beyond the fundamentals of cross-device interactions, privacy, and security, becoming a leader in the programmable world will require wide-ranging exploration, experimentation, and development. There are endless avenues to enable new ways to augment, customize, and otherwise “program” our physical environments. billion by 2030.

Technology 116

Technology Experimentation Interactive IoT 116

Data Science 101

JANUARY 15, 2020

Bias ( syatematic unfairness in data collection ) can be a potential problem in experiments and we need to take it into account while designing experiments. Some pitfalls of this type of experimentation include: Suppose an experiment is performed to observe the relationship between the snack habit of a person while watching TV.

Experimentation 96

Experimentation Statistics Measurement Testing 96

Smart Data Collective

JUNE 21, 2023

This frees up time for experimentation and achieving superior results. Alongside capturing precious memories, Snappic’s software doubles as a data collection tool, providing valuable insights about your guests through features like surveys and competitions. The market for AI software is booming.

Software 77

Software Technology Experimentation Data-driven 77

Data Science and Beyond

APRIL 5, 2021

The initial covid-19 lockdown provided me with extra free time to make the measurement and offsetting of Automattic’s emissions from data centre power use happen. I previously posted about my experiences with RLS offline data collection and visualisation of the collected data , and have since helped with quite a few RLS surveys.

Experimentation 61

Experimentation Testing Publishing Measurement 61

Rocket-Powered Data Science

JULY 7, 2019

Computer Vision: Data Mining: Data Science: Application of scientific method to discovery from data (including Statistics, Machine Learning, data visualization, exploratory data analysis, experimentation, and more). NLG is a software process that transforms structured data into human-language content.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Dataiku

APRIL 22, 2022

Enter Frugal AI , a technique that promises the use of less data and less compute power while guaranteeing robustness within the intended field of use for a given AI model.It In this blog series, we will explore Frugal AI from the perspectives of data, design, trust, and sustainability. It's All in a Name.

Experimentation 59

Experimentation Modeling Business Objectives Manufacturing 59

Cloudera

FEBRUARY 16, 2022

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle. classification problem. Figure 01: Is this a chihuahua or a muffin?

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

Depict Data Studio

JULY 13, 2021

It was also just nice to talk to a group of smart, creative, data-minded professionals who also (yes – that’s right – also) feel overwhelmed by the prospect of having to use statistics. Your Data Team is the litmus test for determining which things are important and meaningful and which things are not. 6 Months to 40 Years).

Statistics 98

Statistics Testing Recreation/Entertainment Experimentation 98

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

Insight

MARCH 27, 2019

DeepAugment is an AutoML tool focusing on data augmentation. It utilizes Bayesian optimization for discovering data augmentation strategies tailored to your image dataset. Introduction Data is the most critical piece of AI applications. Collecting additional data is not always possible and can be expensive.

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

Insight

MARCH 12, 2020

In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. The latter is any type of external data that has been attached to a book?—?for finished his first novel, Cuckoo’s Calling.

Modeling 59

Modeling Metadata Publishing Sales 59

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. It helps you to amplify what’s proven to work, throw away what isn’t, and tweak the goal-posts when data indicates that they may be in the wrong place.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Rocket-Powered Data Science

JULY 6, 2021

In at least one way, it was not different, and that was in the continued development of innovations that are inspired by data. This steady march of data-driven innovation has been a consistent characteristic of each year for at least the past decade. 2) MLOps became the expected norm in machine learning and data science projects.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

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

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

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.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

JUNE 6, 2007

Experimentation & Testing (A/B, Multivariate, you name it). Benchmarking (exactly how you can do it), impactful actionable executive dashboards (what they should contain), creating a data driven organization. I am thrilled to say that my book Web Analytics: An Hour A Day has been published and is now widely available.

Analytics 60

Analytics Metrics Marketing Dashboards 60

AWS Big Data

JULY 3, 2023

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. Amazon Simple Storage Service (Amazon S3) is a popular cloud-based object storage service that can be used as the foundation for building a data lake.

Snapshot 87

Snapshot Data Lake Testing Strategy 87

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. They’ve become “embedded institutions” in engineering. back to the structure of the dataset. intelligibility.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

DataRobot Blog

OCTOBER 4, 2022

This automation drastically reduces model building, testing, evaluation and deployment time, promotes creativity, and enables rapid experimentation for time-sensitive use cases. The dataset consist of sales data collected for multiple retail stores across North America. Improved Productivity. The Dataset.

Forecasting 59

Forecasting Sales Data-driven Modeling 59

Occam's Razor

DECEMBER 12, 2016

Data quality plays a role into this. But, at the end of the day presence of a Tag Manager communicates to me that the company is serious about data collection and data quality. You'll be surprised, there's only one tool in that mix. It is not even an analytics tool. " That lead to this post.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Cloudera

JUNE 6, 2019

Over time, workloads start processing more data, tenants start onboarding more workloads, and administrators (admins) start onboarding more tenants. While this approach provides isolation, it creates another significant challenge: duplication of data, metadata, and security policies, or ‘split-brain’ data lake.

Metadata 85

Metadata Data Lake Optimization Strategy 85

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. Mobile devices (phones, tablets, wearables) are transforming how we behave, how we buy, how we consume content, and dare I say how we become happy or we become sad. Many reasons.

Metrics 141

Metrics Reporting Analytics Marketing 141

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. There are more mistruths and F U D about Web analytics out there than I think is reasonable. Part of it fueled by Vendors. What a competitive bunch!

Analytics 118

Analytics Consulting Metrics Experimentation 118

Occam's Razor

MAY 27, 2015

In a recent set of keynotes and consulting engagements in the US, UK and Canada, I've had an overwhelming feeling that in very fundamental ways some companies make imprecise choices when it comes to their digital strategy. Not because they don't have enough money or opportunity or people. Let's go! Can you do RM+RP+RT using TV?

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

Occam's Razor

JULY 18, 2011

Most email programs now have preview panes that typically block images and scripts (Outlook, Thunderbird, Gmail, everyone), and default settings prevent data collection due to concerns about viruses. This should drive aggressive experimentation of email content / offers / targeting / every facet by your team. That is okay.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

Occam's Razor

AUGUST 13, 2012

Jeroen Hesterman: My biggest challenge is this: I've created a data platform which captures all campaign (paid) traffic and can attribute a conversion to each based on whatever model I choose. That is the solution. The only challenge is that it is not easy, and everyone wants easy. What you are asking for is impossible.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Occam's Razor

OCTOBER 19, 2010

An expansive view of what it means to use data online, both from the type of data perspective and the kind of desired impact perspective. They'll simply puke data faster and, if you implement them right, more efficiently. What is the first thing you want when you think about web analytics? Of course tools. spectrum.

Analytics 135

Analytics Testing Measurement Optimization 135

datapine

JANUARY 31, 2022

The main use of business intelligence is to help business units, managers, top executives, and other operational workers make better-informed decisions backed up with accurate data. BI software uses algorithms to extract actionable insights from a company’s data and guide its strategic decisions. Let’s see it with a real-world example.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation. We ought not dredge our data.

Experimentation 61

Experimentation Testing Statistics Metrics 61