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. 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

CIO Business Intelligence

JUNE 14, 2023

Data and big data analytics are the lifeblood of any successful business. Getting the technology right can be challenging but building the right team with the right skills to undertake data initiatives can be even harder — a challenge reflected in the rising demand for big data and analytics skills and certifications.

Big Data 127

Big Data Data Analytics Analytics Predictive Modeling 127

Cloudera

OCTOBER 18, 2023

Historically, firms have relied on high-cost, third-party solutions to help identify savings opportunities, however, the landscape is rapidly changing, and the emergence of AI and machine learning (ML) has ushered in a new era of possibilities. Quick wins!

Cost-Benefit 80

Cost-Benefit Testing Machine Learning Optimization 80

IBM Big Data Hub

DECEMBER 5, 2023

Generative AI excels at handling diverse data sources such as emails, images, videos, audio files and social media content. This unstructured data forms the backbone for creating models and the ongoing training of generative AI, so it can stay effective over time. trillion in that year.

Unstructured Data 95

Unstructured Data Cost-Benefit Experimentation Machine Learning 95

The Unofficial Google Data Science Blog

APRIL 23, 2024

If the relationship of $X$ to $Y$ can be approximated as quadratic (or any polynomial), the objective and constraints as linear in $Y$, then there is a way to express the optimization as a quadratically constrained quadratic program (QCQP). However, joint optimization is possible by increasing both $x_1$ and $x_2$ at the same time.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

CIO Business Intelligence

JANUARY 9, 2024

Many organizations know that commercially available, “off-the-shelf” generative AI models don’t work well in enterprise settings because of significant data access and security risks. Lesson 1: Don’t start from scratch to train your LLM model Massive amounts of data and computational resources are needed to train an LLM.

Enterprise 119

Enterprise Experimentation Modeling Data-driven 119

CIO Business Intelligence

OCTOBER 1, 2022

The partners say they will create the future of digital manufacturing by leveraging the industrial internet of things (IIoT), digital twin , data, and AI to bring products to consumers faster and increase customer satisfaction, all while improving productivity and reducing costs. Smart manufacturing at scale is a challenge.

Manufacturing 145

Manufacturing Digital Transformation IoT Internet of Things 145

CIO Business Intelligence

FEBRUARY 14, 2024

Fundamentals like security, cost control, identity management, container sprawl, data management, and hardware refreshes remain key strategic areas for CIOs to deal with. Data due diligence Generative AI especially has particular implications for data security, Mann says.

IT 143

IT Metadata Data-driven 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. Length: Highly Variable.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

DataRobot

JANUARY 13, 2022

Many companies find that they have a treasure trove of data but lack the expertise to use it to improve ROI. AI can help them wrangle the data that’s collected and quickly answer important questions. But they often don’t have enough data scientists with the full range of domain expertise to realize its full value.

ROI 52

ROI Machine Learning Experimentation Data Science 52

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

DataRobot

JUNE 8, 2021

The proposed cognitive benefit of personas is that humans are better at thinking in narratives than abstract data about customers. Unlike personas, however, cluster analysis is data-driven. But despite the allure of a data-driven approach, cluster analysis is arbitrary. Personas are subjectively constructed.

Data-driven 98

Data-driven Testing Marketing Experimentation 98

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. Automation Tools.

Data Science 65

Data Science Marketing Data-driven Statistics 65

Smart Data Collective

DECEMBER 30, 2020

Big data is playing an important role in many facets of modern business. One of the most important applications of big data technology lies with inventory management and optimization. Understanding the Best Data-Driven Inventory Optimization Applications for the Coming Year.

Data-driven 94

Data-driven Software Optimization Big Data 94

CIO Business Intelligence

MAY 9, 2024

Its ability to automate routine processes and provide data-driven insights helps create a conducive environment for deep work. Experimentation drives momentum: How do we maximize the value of a given technology? Via experimentation. Learn how DataStax enables production -ready GenAI applications.

Enterprise 119

Enterprise Experimentation Sales Optimization 119

O'Reilly on Data

MAY 14, 2020

This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. 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

Occam's Razor

JULY 24, 2018

The result is analytical strategies that are uninformed by reality, and driven new tool features, random expert recommendations and shiny objects ( OMG we have to get offline attribution! ). Diving a bit deeper into the x-axis… While most data can be collected in real-time now, not all metrics are useful in real-time.

Analytics 93

Analytics Metrics Strategy Measurement 93

DataKitchen

APRIL 13, 2021

This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Companies that implement DataOps find that they are able to reduce cycle times from weeks (or months) to days, virtually eliminate data errors, increase collaboration, and dramatically improve productivity.

Testing 307

Testing Machine Learning Consulting Data Quality 307

O'Reilly on Data

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. Why: Data Makes It Different.

IT 346

IT Testing Experimentation Software 346

CIO Business Intelligence

APRIL 24, 2024

Sales and marketing departments have long been at the forefront of embracing new technologies, and according to data provided by the Alexander Group, a revenue consultancy, 80% of hundreds of survey responses detailed that CROs have formally invested in AI for their marketing teams.

Sales 128

Sales Finance Experimentation Cost-Benefit 128

IBM Big Data Hub

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

Machine Learning 80

Machine Learning Manufacturing Cost-Benefit Deep Learning 80

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. There are similar concerns for CIOs looking to build data and analytics capabilities.

Digital Transformation 145

Digital Transformation Data Governance Experimentation Machine Learning 145

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

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

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 Data-driven Machine Learning 66

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 this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Corinium

APRIL 25, 2019

Ahead of the Chief Data Analytics Officers & Influencers, Insurance event we caught up with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity to discuss how the industry is evolving. The last 10+ years or so have seen Insurance become as data-driven as any vertical industry.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Domino Data Lab

MAY 8, 2019

I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. For example, common practices for collecting data to build training datasets tend to throw away valuable information along the way. Machine learning model interpretability.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

AWS Big Data

NOVEMBER 14, 2023

Data is a key strategic asset for every organization, and every company is a data business at its core. However, in many organizations, data is typically spread across a number of different systems such as software as a service (SaaS) applications, operational databases, and data warehouses.

IoT 57

IoT Data-driven Data Lake Data Strategy 57

AWS Big Data

JULY 20, 2023

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

Data Lake 86

Data Lake Analytics Snapshot Optimization 86

AWS Big Data

OCTOBER 4, 2023

Amazon DataZone enables customers to discover, access, share, and govern data at scale across organizational boundaries, reducing the undifferentiated heavy lifting of making data and analytics tools accessible to everyone in the organization. This is challenging because access to data is managed differently by each of the tools.

Metadata 103

Metadata Data Lake Publishing Data Governance 103

AWS Big Data

MARCH 20, 2023

We discuss how to create such a solution using Amazon Kinesis Data Streams , Amazon Managed Streaming for Kafka (Amazon MSK), Amazon Kinesis Data Analytics for Apache Flink ; the design decisions that went into the architecture; and the observed business benefits by Poshmark.

Analytics 52

Analytics Slice and Dice Data Processing Data Lake 52

AWS Big Data

JUNE 20, 2023

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) and data sources residing in AWS, on-premises, or other cloud systems using SQL or Python. Solution overview Data scientists are generally accustomed to working with large datasets.

Data Lake 73

Data Lake Data Science Recreation/Entertainment Experimentation 73

Domino Data Lab

FEBRUARY 12, 2020

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. Ray is an open-source system for scaling Python applications from single machines to large clusters. for reinforcement learning (RL), ? Introduction. If your team has started using ?

Experimentation 137

Experimentation Modeling Data Science Management 137

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 58

Data Warehouse Unstructured Data Analytics Visualization 58

Occam's Razor

FEBRUARY 28, 2017

A story where data is the hero, followed by two mind-challenging business-shifting ideas. Additionally, I passionately believe that optimal metrics help solve for more than an individual’s behavior. While not optimal, it was understandable given the evolutionary stage they were at. You are what you measure. Truly omg coolness.

Key Performance Indicator 106

Key Performance Indicator Metrics Measurement Advertising 106

Domino Data Lab

AUGUST 26, 2019

This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. Introduction.

Testing 79

Testing Modeling Machine Learning Metrics 79