CIO Business Intelligence

JANUARY 14, 2025

Experimentation: The innovation zone Progressive cities designate innovation districts where new ideas can be tested safely. This shift from traditional SOA (where services align with technical functions) to domain-oriented services represents a fundamental change in how we structure systems.

Enterprise 119

Enterprise Technology Metrics Measurement 119

O'Reilly on Data

OCTOBER 13, 2020

Our previous articles in this series introduce our own take on AI product management , discuss the skills that AI product managers need , and detail how to bring an AI product to market. From a technical perspective, it is entirely possible for ML systems to function on wildly different data. I/O validation.

Management 314

Management Machine Learning Metrics Modeling 314

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

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

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 97

Optimization Data Analytics Analytics Data-driven 97

CIO Business Intelligence

MARCH 7, 2023

Some IT organizations elected to lift and shift apps to the cloud and get out of the data center faster, hoping that a second phase of funding for modernization would come. I’ll be covering more examples of force multipliers in upcoming articles, and here are three to start that should apply to most CIOs and their IT organizations.

Digital Transformation 145

Digital Transformation Data Governance Machine Learning Experimentation 145

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

CIO Business Intelligence

OCTOBER 15, 2024

Driven by the development community’s desire for more capabilities and controls when deploying applications, DevOps gained momentum in 2011 in the enterprise with a positive outlook from Gartner and in 2015 when the Scaled Agile Framework (SAFe) incorporated DevOps. It may surprise you, but DevOps has been around for nearly two decades.

Enterprise 143

Enterprise Testing Business Objectives Experimentation 143

CIO Business Intelligence

DECEMBER 19, 2023

After all, every department is pressured to drive efficiencies and is clamoring for automation, data capabilities, and improvements in employee experiences, some of which could be addressed with generative AI. As every CIO can attest, the aggregate demand for IT and data capabilities is straining their IT leadership teams.

Digital Transformation 143

Digital Transformation Unstructured Data Machine Learning Risk Management 143

Insight

APRIL 28, 2020

Originally posted on Open Data Science (ODSC). In this article, we share some data-driven advice on how to get started on the right foot with an effective and appropriate screening process. Designing a Data Science Interview Onsite interviews are indispensable, but they are time-consuming.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

Domino Data Lab

OCTOBER 14, 2020

Guest post by Jeff Melching, Distinguished Engineer / Chief Architect Data & Analytics. We’ve developed a model-driven software platform, called Climate FieldView , that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield.

Experimentation 99

Experimentation Deep Learning Modeling Testing 99

CIO Business Intelligence

JULY 24, 2024

In this article, we’ll dive into each phase, offering actionable strategies to help you master the art of adaptive technology portfolio management. Key strategies for exploration: Experimentation: Conduct small-scale experiments. Data-driven decisions: Leverage data and analytics to assess new technologies’ potential impact and ROI.

Technology 98

Technology Experimentation Cost-Benefit Digital Transformation 98

CIO Business Intelligence

DECEMBER 10, 2024

As a data-driven company, InnoGames GmbH has been exploring the opportunities (but also the legal and ethical issues) that the technology brings with it for some time. Both were created to address a fundamental problem in two respects: Data that remains unused: InnoGames collects more than 1.7

Data-driven 98

Data-driven Metadata Interactive KPI 98

CIO Business Intelligence

JUNE 11, 2024

A 1958 Harvard Business Review article coined the term information technology, focusing their definition on rapidly processing large amounts of information, using statistical and mathematical methods in decision-making, and simulating higher order thinking through applications.

IT 136

IT Digital Transformation Broadcasting Business Objectives 136

O'Reilly on Data

MARCH 23, 2023

But Transformers have some other important advantages: Transformers don’t require training data to be labeled; that is, you don’t need metadata that specifies what each sentence in the training data means. Unlike labels, embeddings are learned from the training data, not produced by humans.

IT 348

IT Modeling Testing Risk 348

Smart Data Collective

JUNE 21, 2023

Last summer, we wrote an article about the ways that artificial intelligence is changing video editing software. We also talked about some of the best AI-driven video editing applications. This frees up time for experimentation and achieving superior results.

Software 104

Software Technology Experimentation Data-driven 104

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. In other words, using metadata about data science work to generate code. Introduction. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Data Science Machine Learning Data-driven 105

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

Occam's Razor

SEPTEMBER 19, 2011

It is also important to point out that I am keeping the data simple purely to keep communication of the story straightforward. Here's the outcomes data for the control version of the experiment. Having read this post what might be the biggest downside to experimentation? For people in the control region, nothing changes.

Measurement 120

Measurement Advertising Testing Marketing 120

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

AUGUST 5, 2019

This Domino Data Science Field Note covers Chris Wiggins ‘s recent data ethics seminar at Berkeley. Data Scientists, Tradeoffs, and Data Ethics. As more companies become model-driven , data scientists are uniquely positioned to drive innovation and to help their companies remain economically strong.

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

IBM Big Data Hub

MARCH 27, 2024

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Why are Scope 3 emissions difficult to calculate?

Experimentation 73

Experimentation Deep Learning Modeling Data-driven 73

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

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

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. Years and years of practice with R or "Big Data." I read articles they write. This reality powers my impostor syndrome, and (yet?)

Marketing 138

Marketing Analytics Machine Learning Strategy 138

Sisense

JUNE 30, 2019

It doesn’t matter what you think your company does, it’s going to have to turn into a data company soon, if it hasn’t started already, in addition to continuing to provide your core product or service. Data Strategies for the Uninitiated. First off, “So, what even is a data strategy anyway?” Is that my job?”

Data Strategy 71

Data Strategy Strategy Consulting Experimentation 71

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. Likely, you’re doing better than you think. Caution is king. Looking forward.

Enterprise 52

Enterprise Experimentation Risk Data-driven 52

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

Cloudera

NOVEMBER 25, 2020

The challenges Matthew and his team are facing are mainly about access to a multitude of data sets, of various types and sources, with ease and ad-hoc, and their ability to deliver data-driven and confident outcomes. . Most of their research data is unstructured and has a lot of variety. Challenges Ahead.

Data Warehouse 59

Data Warehouse Unstructured Data Analytics Visualization 59

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

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. Without clarity in metrics, it’s impossible to do meaningful experimentation. Identifying the problem.

Marketing 364

Marketing Experimentation Metrics Testing 364

The Unofficial Google Data Science Blog

JUNE 30, 2016

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects.

Statistics 45

Statistics Optimization Modeling Experimentation 45

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

CIO Business Intelligence

APRIL 9, 2025

In Prioritizing AI investments: Balancing short-term gains with long-term vision , I addressed the foundational role of data trust in crafting a viable AI investment strategy. So why would any organization that considers a decision critical use business intelligence data to make that decision?

Enterprise 102

Enterprise Analytics Experimentation Statistics 102

O'Reilly on Data

MARCH 19, 2020

We use it as a data source for our annual platform analysis , and we’re using it as the basis for this report, where we take a close look at the most-used and most-searched topics in machine learning (ML) and artificial intelligence (AI) on O’Reilly [1]. The chatbot was one of the first applications of AI in experimental and production usage.

Deep Learning 354

Deep Learning Machine Learning Data-driven Modeling 354

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 Machine Learning Data-driven 66

Sisense

MARCH 26, 2021

Companies surveyed by Harvard Business Review Analytic Services (HBR) report that two of the most important strategic benefits of using data analytics are (1) identifying new revenue and business models and (2) becoming more innovative. 39% of companies want to identify new revenue and business opportunities with data analytics.

Analytics 98

Analytics Data-driven Dashboards Experimentation 98