O'Reilly on Data

MARCH 18, 2020

The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. This year, about 15% of respondent organizations are not doing anything with AI, down ~20% from our 2019 survey. It seems as if the experimental AI projects of 2019 have borne fruit. But what kind? Bottlenecks to AI adoption.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Jet Global

AUGUST 23, 2021

Manage data from diverse systems. Data quality is a central point for producing quality reports that can be used effectively in decision-making. Besides simply presenting data, the audience must understand what the figures mean and see the trends. Improve collaborative measures.

Finance 52

Finance Reporting Data Collection Data Quality 52

O'Reilly on Data

MARCH 31, 2020

That foundation means that you have already shifted the culture and data infrastructure of your company. Although machine learning projects differ in subtle ways from traditional projects, they tend to require similar infrastructure, similar data collection processes, and similar developer habits.

Management 332

Management Machine Learning Experimentation Metrics 332

Domino Data Lab

MARCH 3, 2019

Then, when we received 11,400 responses, the next step became obvious to a duo of data scientists on the receiving end of that data collection. Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Timo Elliott

JANUARY 22, 2019

2019 is the year that analytics technology starts delivering what users have been dreaming about for over forty years — easy, natural access to reliable business information. We’ve reached the third great wave of analytics, after semantic-layer business intelligence platforms in the 90s and data discovery in the 2000s.

Analytics 89

Analytics Machine Learning Unstructured Data Digital Transformation 89

Domino Data Lab

FEBRUARY 4, 2019

This edition of the conference will be held May 23–24, 2019 in NYC, where we’ll focus on data science as a team sport: leadership , practices , how teams work. For example, one of the questions which has provided a guide throughout our planning stages is: “ What can data science teams learn from each other? ethics in AI.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

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. It can’t do that anymore.

Data Governance 72

Data Governance Strategy Enterprise Machine Learning 72