CIO Business Intelligence

NOVEMBER 7, 2022

CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects. Are data science teams set up for success? Have business leaders defined realistic success criteria and areas of low-risk experimentation?

Machine Learning 128

Machine Learning Data Science Experimentation IoT 128

Domino Data Lab

JANUARY 24, 2019

In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. Over the last couple years, it would be hard to blame anyone for being overwhelmed looking at the data science platform market landscape. Proprietary (often GUI-driven) data science platforms.

Data Science 65

Data Science Marketing Data-driven Statistics 65

CIO Business Intelligence

NOVEMBER 21, 2023

That’s where an IT strategy that frames shadow IT as an opportunity for improved collaboration can have a profound impact. Catalog risks, prioritize opportunities, promote financial controls Shadow IT gives IT leaders an opportunity to reassess their strategies around departmental technology solutions.

IT 137

IT Risk Cost-Benefit Risk Management 137

CIO Business Intelligence

DECEMBER 19, 2023

This year’s technology darling and other machine learning investments have already impacted digital transformation strategies in 2023 , and boards will expect CIOs to update their AI transformation strategies frequently. Luckily, many are expanding budgets to do so. “94%

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Digital Transformation Unstructured Data Machine Learning Risk Management 143

CIO Business Intelligence

APRIL 2, 2024

Over the past decade, CIOs have invested significantly in digital transformation initiatives in an effort to improve customer experiences, build data analytics capabilities, and deliver productivity enhancements with automation. It’s like trying to get a jazz quartet, a rock band, a classical orchestra, and a DJ to play in harmony.”

Management 140

Management Digital Transformation Testing Experimentation 140

DataRobot Blog

NOVEMBER 15, 2022

Organizations are looking to deliver more business value from their AI investments, a hot topic at Big Data & AI World Asia. At the well-attended data science event, a DataRobot customer panel highlighted innovation with AI that challenges the status quo.

Big Data 52

Big Data Experimentation Data-driven Machine Learning 52

CIO Business Intelligence

MARCH 21, 2022

What is a data scientist? Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Data scientist job description. Data scientist salary.

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Unstructured Data Data Analytics Analytics Structured Data 137

CIO Business Intelligence

JULY 6, 2022

Laying the foundation for its cloud strategy, BNY Mellon undertook a multi-year application modernization effort. “In On BNY Mellon’s cloud strategy: First and foremost, we view cloud as a journey, not a destination. Today, our strategy is to have a multi-cloud approach. What follows are edited excerpts of that conversation.

Experimentation 98

Experimentation Strategy Testing Risk 98

DataRobot Blog

FEBRUARY 28, 2023

While this can be an excellent strategy for a future-oriented company, it can prove futile if you don’t maximize the value of your investment. According to Flexera 1 , 92% of enterprises have a multi-cloud strategy, while 80% have a hybrid cloud strategy. Learn more about DataRobot hosted notebooks.

Data Processing 72

Data Processing Experimentation Machine Learning Modeling 72

The Unofficial Google Data Science Blog

APRIL 23, 2024

It is also a sound strategy when experimenting with several parameters at the same time. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. (And sometimes even if it is not[1].)

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

CIO Business Intelligence

MAY 12, 2022

The goal of Alteryx’s Analytic Process Automation (APA) platform is to help you build a pipeline that cleans data before applying the best data science and machine learning algorithms. A free plan allows experimentation. More focused options are available for particular data sets.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

CIO Business Intelligence

OCTOBER 1, 2022

It has moved past what Cretella calls the “experimentation phase” with scaled solutions and increasingly sophisticated AI applications. Data and AI have since become central to the company’s digital strategy. “We P&G took the first steps in its AI journey more than five years ago.

Manufacturing 145

Manufacturing Digital Transformation IoT Internet of Things 145

Domino Data Lab

AUGUST 6, 2021

An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. The complexity of models, and general limitations of expertise with data science among business leaders, creates an environment ripe for risk. Types of Model Risk.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

FineReport

DECEMBER 11, 2019

In the past few years, the term “data science” has been widely used, and people seem to see it in every field. Big Data”, “Business Intelligence”, “ Data Analysis ” and “ Artificial Intelligence ” came into being. For a while, everyone seems to have begun to learn data analysis. Big data is changing our world.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

Data Science 101

OCTOBER 15, 2023

And we are still in the science to tech phase, not just battling incredibly challenging lab work, but finding things we can solve. This problem can be witnessed in the business strategies of QC companies. That’s going to use the various existing computing technology in and around the experimentation of this new capability.

Risk 107

Risk Marketing Consulting Technology 107

O'Reilly on Data

MARCH 19, 2020

Aggregating artificial intelligence and machine learning topics accounts for nearly 5% of all usage activity on the platform, a touch less than, and growing 50% faster than, the well-established “data science” topic (see Figure 2). The chatbot was one of the first applications of AI in experimental and production usage.

Deep Learning 276

Deep Learning Machine Learning Data-driven Modeling 276

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production.

Cost-Benefit 100

Cost-Benefit Modeling Manufacturing Interactive 100

Ontotext

NOVEMBER 2, 2023

This shift from relational to graph approach has been well-documented by Gartner who advise that “using graph techniques at scale will form the foundation of modern data and analytics” and “graph technologies will be used in 80% of data and analytics innovations by 2025.”

IT 69

IT Cost-Benefit Data-driven Technology 69

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

The Unofficial Google Data Science Blog

JULY 22, 2020

by ALEXANDER WAKIM Ramp-up and multi-armed bandits (MAB) are common strategies in online controlled experiments (OCE). These strategies involve changing assignment weights during an experiment. The first is a strategy called ramp-up and is advised by many experts in the field [1].

Experimentation 95

Experimentation Statistics Testing Strategy 95

bridgei2i

APRIL 28, 2020

My name is Aruna Babu and I’m a transformation consultant who spent a good part of the last decade crafting strategy that marries business, technology and user needs. And so that process with curation or identifying which data potentially is a leading indicator and then test those leading indicators. Aruna: Got it.

Enterprise 103

Enterprise Data Analytics Analytics Forecasting 103

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. Discovering the proper method requires time-consuming experimentation. Want to advance your career in Data Science and Artificial Intelligence?

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

Alation

SEPTEMBER 28, 2022

DataOps, at its core, is about collaboration between data professionals and other IT roles to help increase the speed, quality, and frequency of data analytics deployments. In order to make DataOps successful in an organization, there needs to be a shift in culture and mindset around how data is managed. It’s a Team Sport.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

Insight

MARCH 12, 2020

With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. of users tagged Cuckoo’s Calling with ‘science-fiction’, but 10% of users tagged it with ‘mystery’, we should be more confident in the latter label.

Modeling 59

Modeling Metadata Publishing Sales 59

Rocket-Powered Data Science

OCTOBER 16, 2020

Be sure to listen to the full recording of our lively conversation, which covered Data Literacy, Data Strategy, Data Leadership, and more. The data age has been marked by numerous “hype cycles.” How To Build A Successful Enterprise Data Strategy. The Age of Hype Cycles.

Digital Transformation 142

Digital Transformation Experimentation Data Strategy Strategy 142

Domino Data Lab

JUNE 10, 2020

Today, we announced the latest release of Domino’s data science platform which represents a big step forward for enterprise data science teams. Domino’s best-in-class Workbench is now even more powerful for data scientists.

Data Science 82

Data Science Enterprise Experimentation Modeling 82

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.

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Data-driven Machine Learning IoT Deep Learning 279

DataKitchen

APRIL 13, 2021

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix.

Testing 307

Testing Machine Learning Consulting Data Quality 307

Cloudera

APRIL 13, 2021

Unfortunately, most organizations run into trouble when it comes to bridging the gap that exists between experimentation and full-scale ML production. Before ML can become a catalyst for change, it must first be treated as an integral part of your data strategy. Still, at its core, ML is about science.

Machine Learning 97

Machine Learning Enterprise Experimentation Modeling 97

IBM Big Data Hub

JANUARY 17, 2023

With IBM’s data integration portfolio, you are not locked into just a single integration style. You can select a hybrid integration strategy that aligns with your organization’s business strategy to meet the needs of your data consumers wanting to access and utilize the data. Data science and MLOps.

Data-driven 58

Data-driven Enterprise Data Governance Prescriptive Analytics 58

DataRobot Blog

OCTOBER 27, 2022

Granular time splits show you the true picture,” emphasized Brain Bell, Senior Director, Product Management who leads the DataRobot MLOps strategy. DataRobot offers fast and intuitive drift drill down, as we focus on analyzing your data across different dimensions in real-time to answer data science questions.

Data Science 52

Data Science Experimentation Visualization Modeling 52

IBM Big Data Hub

OCTOBER 20, 2023

AI platforms offer a wide range of capabilities that can help organizations streamline operations, make data-driven decisions, deploy AI applications effectively and achieve competitive advantages. Visual modeling: Combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI studio.

Machine Learning 84

Machine Learning Manufacturing Cost-Benefit Deep Learning 84

Decision Management Solutions

JUNE 25, 2019

Robinson , Chief Strategy Officer, Kinaxis discussed Searching for Unicorns: Attracting and Retaining Analytics Talent. She emphasized the importance to data science teams of business translation and of partnering with domain experts. Great advice. DecisionsFirst indeed! Finally, Shingai Manjengwa , CEO, Fireside Analytics Inc.

Predictive Analytics 75

Predictive Analytics Analytics Data Science Experimentation 75

Domino Data Lab

FEBRUARY 12, 2020

This post is for people making technology decisions, by which I mean data science team leads, architects, dev team leads, even managers who are involved in strategic decisions about the technology used in their organizations. If your team has started using ? and you’re wondering what it is, this post is for you. multiprocessing , ?

Experimentation 137

Experimentation Modeling Data Science Management 137

CIO Business Intelligence

OCTOBER 4, 2023

“Generative AI is just more far-reaching than traditional AI or machine learning, so the opportunities for disasters have grown,” says Bret Greenstein, partner and leader of the gen AI go-to-market strategy at PricewaterhouseCoopers. Artificial Intelligence, Data Management, Data Science, Machine Learning

Cost-Benefit 141

Cost-Benefit Manufacturing Machine Learning Experimentation 141

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. The approach they’ve used applies to other popular data science APIs such as NumPy , Tensorflow , and so on.

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Metadata Machine Learning Data Science Data-driven 105

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.

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Statistics Optimization Modeling Experimentation 44