Smart Data Collective

DECEMBER 17, 2021

The process of Marketing Analytics consists of data collection, data analysis, and action plan development. Understanding your marketing data to make more informed and successful marketing strategy decisions is a systematic process. Types of Data Used in Marketing Analytics. Source: [link].

Marketing Analytics 95

Marketing Analytics Marketing Data-driven Analytics 95

CIO Business Intelligence

JANUARY 17, 2024

The first was becoming one of the first research companies to move its panels and surveys online, reducing costs and increasing the speed and scope of data collection. Additionally, it continuously explores reams of data and modern tools to improve its capabilities and adapt to the changing data landscape.

Digital Transformation 119

Digital Transformation Dashboards Reporting Data Science 119

FineReport

MARCH 24, 2024

Given the critical role they play, employers actively seek data analysts to enhance efficiency and stimulate growth. This article explores the data analyst job description, covering essential skills, tools, education, certifications, and experience. SQL manages and retrieves data from databases, handling larger datasets.

Statistics 52

Statistics Data mining Visualization Reporting 52

datapine

JANUARY 6, 2022

Interval: a measurement scale where data is grouped into categories with orderly and equal distances between the categories. For a more in-depth review of scales of measurement, read our article on data analysis questions. Quantitative analysis refers to a set of processes by which numerical data is analyzed.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

O'Reilly on Data

JULY 28, 2020

In this article, we turn our attention to the process itself: how do you bring a product to market? It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Product managers for AI must satisfy these same responsibilities, tuned for the AI lifecycle.

Marketing 362

Marketing Experimentation Metrics Testing 362

O'Reilly on Data

JULY 28, 2020

A recent article in The Verge discussed PULSE , an algorithm for “upsampling” digital images. When looked at this way, it’s largely a problem of mathematics and statistics. There is no such thing as “raw data,” and hence, no pure, unadulterated, unbiased data. It’s an issue of harms and of power.

Testing 135

Testing Statistics Recreation/Entertainment Software 135

Smart Data Collective

MAY 8, 2022

Brands are closely working to solve this as they dive deep into the world of big data analytics. Well, don’t go anywhere because, in this article, we will show you how you can use big data analytics combined with AI to achieve the best performance possible. What is the relationship between big data analytics and AI?

Big Data 106

Big Data Data Analytics Analytics Cost-Benefit 106

Smart Data Collective

NOVEMBER 14, 2019

As Andrew Pearson wrote in his article “ Predictive Analytics in the Gaming Industry ,” the gaming industry has used some form of predictive analytics for decades. Older statistical modeling methodologies only used three or four variables, so gaming companies can make much more nuanced insights these days.

Predictive Analytics 79

Predictive Analytics Analytics Forecasting Modeling 79

Smart Data Collective

NOVEMBER 24, 2020

In this article, you’ll discover: upcoming trends in business intelligence what benefits will BI provide for businesses in 2020 and on? The strategic decision-making in the future of business intelligence will be shaped by faster reports, deeper data insights, broader areas of data collection. Identify Opportunities.

Business Intelligence 131

Business Intelligence Visualization Dashboards Prescriptive Analytics 131

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. These visualizations are useful for helping people visualize and understand trends , outliers, and patterns in data. We’ll actually do this later in this article. R: Analytics powerhouse. R libraries.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Occam's Razor

MARCH 30, 2017

Machine Learning | Marketing. Machine Learning | Analytics. People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Most Deep Learning methods involve artificial neural networks, modeling how our bran works.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

IBM Big Data Hub

AUGUST 7, 2023

This article, part of the IBM and Pfizer’s series on the application of AI techniques to improve clinical trial performance, focuses on enrollment and real-time forecasting. AI can also empower trial managers and executives with the data to make strategic decisions.

Optimization 61

Optimization Forecasting Data-driven Strategy 61

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

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. A complementary Domino project is available. . Introduction. Chapter Introduction: Causal Inference.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Using bad data, or the incorrect data can generate devastating results. between 2022 and 2029.

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

Sisense

OCTOBER 15, 2020

As real-time analytics and machine learning stream processing are growing rapidly, they introduce a new set of technological and conceptual challenges. Every data professional knows that ensuring data quality is vital to producing usable query results. The best architecture for that is called “event sourcing.”

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

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? Instead, I built the AIgent. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

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

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

Domino Data Lab

OCTOBER 23, 2019

This article provides insight on the mindset, approach, and tools to consider when solving a real-world ML problem. It covers questions to consider as well as collecting, prepping and plotting data. Collecting and prepping data are core research tasks. A complementary Domino project is available. Introduction.

Statistics 59

Statistics Machine Learning Testing Modeling 59

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

The Unofficial Google Data Science Blog

JULY 18, 2023

Editor's note : The relationship between reliability and validity are somewhat analogous to that between the notions of statistical uncertainty and representational uncertainty introduced in an earlier post. Measurement challenges Assessing reliability is essentially a process of data collection and analysis.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Domino Data Lab

AUGUST 20, 2019

Many thanks to AWP Pearson for the permission to excerpt “Manual Feature Engineering: Manipulating Data for Fun and Profit” from the book, Machine Learning with Python for Everyone by Mark E. Missing values can be filled in based on expert knowledge, heuristics, or by some machine learning techniques.

Testing 68

Testing Modeling Interactive Measurement 68

Jet Global

MAY 1, 2023

Let’s just give our customers access to the data. You’ve settled for becoming a data collection tool rather than adding value to your product. While data exports may satisfy a portion of your customers, there will be many who simply want reports and insights that are available “out of the box.”

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52