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.

Unstructured Data 132

Unstructured Data Data Analytics Analytics Structured Data 132

CIO Business Intelligence

MAY 12, 2022

Most tools offer visual programming interfaces that enable users to drag and drop various icons optimized for data analysis. 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.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

CIO Business Intelligence

OCTOBER 1, 2022

The digital transformation of P&G’s manufacturing platform will enable the company to check product quality in real-time directly on the production line, maximize the resiliency of equipment while avoiding waste, and optimize the use of energy and water in manufacturing plants.

Manufacturing 144

Manufacturing Digital Transformation IoT Internet of Things 144

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. AGI wouldn’t just perceive its surroundings; it would understand them.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

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

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. Be modular and user-friendly.

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

Ontotext

NOVEMBER 2, 2023

This is partly because integrating and moving data is not the only problem. The data itself is stored in a way that is not optimal for extracting insight. Unlocking additional value from data requires context, relationships, and structure, none of which are present in the way most organizations store their data today.

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

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

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. Data breaks.

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. Optimize later.

Machine Learning 97

Machine Learning Enterprise Experimentation Modeling 97

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. This unified experience optimizes the process of developing and deploying ML models by streamlining workflows for increased efficiency.

Machine Learning 78

Machine Learning Manufacturing Cost-Benefit Deep Learning 78

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 44

Statistics Optimization Modeling Experimentation 44

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 57

Data-driven Enterprise Data Governance Prescriptive Analytics 57

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.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

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

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. But these are not usually amenable to A/B experimentation.

Experimentation 68

Experimentation Sales Metrics Measurement 68

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. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

AUGUST 26, 2019

This control prevents overfitting and reduction in predictive accuracy on new test data. When hyperparameters are also used for optimization and tuned for a specific dataset, they also impact predictive accuracy. Models are at the heart of of data science and increasing predictive accuracy of models is a core expectation upon industry.

Testing 79

Testing Modeling Machine Learning Metrics 79

datapine

JANUARY 31, 2022

It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

CIO Business Intelligence

AUGUST 1, 2023

As many CIOs prepare their 2024 budgets and digital transformation priorities, developing a strategy that seeks opportunities to evolve business models, targets near-term operational impacts, prioritizes where employees should experiment, and defines AI-related risk-mitigating plans is imperative.

Digital Transformation 97

Digital Transformation Unstructured Data Strategy Experimentation 97

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Although these difficulties are more pronounced when we deal with observational data, the proliferation of hypotheses and lack of intentionality in data collection can even impact designed experiments. Be sure to have a deep, thorough understanding of how data under consideration was collected and what it actually means.

Experimentation 61

Experimentation Testing Statistics Metrics 61

IBM Big Data Hub

SEPTEMBER 25, 2023

With a few taps on a mobile device, riders request a ride; then, Uber’s algorithms work to match them with the nearest available driver and calculate the optimal price. Uber’s prowess as a transportation, logistics and analytics company hinges on their ability to leverage data effectively. But the simplicity ends there.

OLAP 92

OLAP Data Lake Data-driven Snapshot 92

Domino Data Lab

OCTOBER 14, 2020

It incorporates public and third-party data sets, such as weather, soil, satellite, elevation data and proprietary data, such as genetic information of seed hybrids that we acquire from our parent company, Bayer. With our platform, farmers can understand the impact of a particular fertilization strategy and adjust as needed.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99