IBM Big Data Hub

SEPTEMBER 6, 2023

AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. What is AI marketing?

Marketing 85

Marketing Technology Advertising ROI 85

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Products based on deep learning can be difficult (or even impossible) to develop; it’s a classic “high return versus high risk” situation, in which it is inherently difficult to calculate return on investment.

Marketing 362

Marketing Experimentation Metrics Testing 362

Smart Data Collective

MAY 8, 2022

Let’s not forget that big data and AI can also automate about 80% of the physical work required from human beings, 70% of the data processing, and more than 60% of the data collection tasks. From the statistics shown, this means that both AI and big data have the potential to affect how we work in the workplace.

Big Data 107

Big Data Data Analytics Analytics Cost-Benefit 107

IBM Big Data Hub

FEBRUARY 14, 2024

Using machine learning (ML), AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. These systems can evaluate vast amounts of data to uncover trends and patterns, and to make decisions.

Cost-Benefit 87

Cost-Benefit Insurance Unstructured Data Machine Learning 87

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. Marketing and sales: Conversational AI has become an invaluable tool for data collection. This data can be used to better understand customer preferences and tailor marketing strategies accordingly.

Enterprise 104

Enterprise Interactive Data-driven Key Performance Indicator 104

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 166

Analytics Machine Learning Data Science Deep Learning 166

Cloudera

OCTOBER 1, 2022

While the word “data” has been common since the 1940s, managing data’s growth, current use, and regulation is a relatively new frontier. . Governments and enterprises are working hard today to figure out the structures and regulations needed around data collection and use.

Data Governance 74

Data Governance Strategy Enterprise Machine Learning 74

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. This is critical in our massively data-sharing world and enterprises.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

IBM Big Data Hub

NOVEMBER 14, 2023

Data monetization strategy: Managing data as a product Every organization has the potential to monetize their data; for many organizations, it is an untapped resource for new capabilities. Each data product has its own lifecycle environment where its data and AI assets are managed in their product-specific data lakehouse.

Strategy 112

Strategy Cost-Benefit Data-driven Measurement 112

Cloudera

NOVEMBER 19, 2020

In financial services, another highly regulated, data-intensive industry, some 80 percent of industry experts say artificial intelligence is helping to reduce fraud. Machine learning algorithms enable fraud detection systems to distinguish between legitimate and fraudulent behaviors. Fraudulent Activity Detection.

Machine Learning 117

Machine Learning Data-driven Modeling Deep Learning 117

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

Insight

JANUARY 14, 2020

In short, I was faced with two major difficulties regarding data collection: I didn’t have nearly enough images, and the images I did have were not representative of a realistic gym environment. Let’s train a CNN with fake data! On top of this issue, I only had between 40–50 unique images for each class. What the heck!

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

OCTOBER 9, 2019

We’ll examine National Oceanic and Atmospheric Administration (NOAA) data management practices which I learned about at their workshop, as a case study in how to handle data collection, dataset stewardship, quality control, analytics, and accountability when the stakes are especially high. How cool is that?!

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Occam's Razor

OCTOBER 12, 2017

One sample strategy: Expect that I'll ask open-ended questions (If a company has 90% Reach on TV, why the heck do they need digital?). Hence, a personal strategy of mine is to look well outside my area of expertise to help me improve my analytical thinking capabilities. Or, maybe I'm just too deep into this stuff. :).

Marketing 136

Marketing Analytics Machine Learning Strategy 136