CIO Business Intelligence

JUNE 1, 2022

For businesses like the McLaren Group, these two trends are at the core of the conglomerate’s digital transformation and competitive strategy, on and off the track. . Like professional basketball, industrial-scale farming, national politics, and global merchandising, auto racing has become a data science. A Competitive Differentiator.

Manufacturing 119

Manufacturing Digital Transformation Predictive Analytics Strategy 119

CIO Business Intelligence

JUNE 7, 2022

But only in recent years, with the growth of the web, cloud computing, hyperscale data centers, machine learning, neural networks, deep learning, and powerful servers with blazing fast processors, has it been possible for NLP algorithms to thrive in business environments. Just starting out with analytics?

Unstructured Data 98

Unstructured Data Deep Learning Insurance Interactive 98

datapine

AUGUST 14, 2019

To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations. But being an inquisitive Sherlock Holmes of data is no easy task. In the past, data scientists had to rely on powerful computers to manage large volumes of data.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

CIO Business Intelligence

APRIL 11, 2022

strategy, which will focus even more on enhancing customer service on the city’s digital infrastructure. The smart city solution incorporates video and sound data inputs from the area, integrated with publicly available, historical data sources, such as crime, weather and social media data.

IoT 145

IoT Measurement Cost-Benefit Deep Learning 145

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Products based on deep learning can be difficult (or even impossible) to develop; it’s a classic “high return versus high risk” situation, in which it is inherently difficult to calculate return on investment.

Marketing 362

Marketing Experimentation Metrics Testing 362

CIO Business Intelligence

APRIL 6, 2022

By giving leadership at all levels more timely access to critical data about business operations, businesses can see more of their options, make and automate better decisions, and further improve efficiency to drive higher customer satisfaction and profitability. Ready to evolve your analytics strategy or improve your data quality?

Data mining 72

Data mining Deep Learning Insurance Digital Transformation 72

CIO Business Intelligence

MARCH 14, 2022

It is also the foundation of predictive analysis, artificial intelligence (AI), and machine learning (ML). Real-time Data Scaling Challenges. Several factors make such scaling difficult: Massive Data Growth: Global data creation is projected to exceed 180 zettabytes by 2025. Just starting out with analytics?

Data Processing 116

Data Processing Optimization Deep Learning Software 116

CIO Business Intelligence

APRIL 11, 2022

While buying everyday necessities like groceries online has been a safety strategy for many consumers, the convenience of online shopping is compelling enough for many to continue. Using artificial intelligence (AI) and machine learning, more than 1.9 Ready to evolve your analytics strategy or improve your data quality?

Deep Learning 59

Deep Learning Strategy Risk Data Quality 59

CIO Business Intelligence

JANUARY 4, 2023

Deployed successfully around the globe, liquid cooling is becoming essential to future proofing data centers. Scaling out and developing large-scale systems : To meet demand, the HPC industry is developing and honing strategies to effectively scale and deploy large systems that are both efficient and reliable. IT Leadership.

Cost-Benefit 59

Cost-Benefit Machine Learning Modeling Uncertainty 59

CIO Business Intelligence

APRIL 4, 2022

Their legacy databases could not analyze the volumes of data fast enough to block fraudulent activities without negatively impacting their responsiveness. . Deep link analytics combined with real-time analysis and machine learning provide a robust platform for detecting and preventing fraud.

Technology 52

Technology Deep Learning Cost-Benefit Data Quality 52

CIO Business Intelligence

MAY 16, 2022

The data gathered from cameras and sensors as part of a computer vision system, along with machine learning, make it easier to find missing persons and to identify people who are not allowed to be in a venue. Ready to evolve your analytics strategy or improve your data quality?

Recreation/Entertainment 104

Recreation/Entertainment Broadcasting Cost-Benefit Advertising 104

Smart Data Collective

AUGUST 26, 2021

Deep learning technology is changing the future of small businesses around the world. A growing number of small businesses are using deep learning technology to address some of their most pressing challenges. New advances in deep learning are integrated into various accounting algorithms.

Deep Learning 97

Deep Learning Finance Cost-Benefit Advertising 97

Cloudera

NOVEMBER 15, 2021

Here we briefly describe some of the challenges that data poses to AI. Data annotation. Abundance of data has been one of the main facilitators of the AI boom of the last decade. Deep Learning, a subset of AI algorithms, typically requires large amounts of human annotated data to be useful. Data curation.

Unstructured Data 91

Unstructured Data Recreation/Entertainment Structured Data Reporting 91

Cloudera

OCTOBER 1, 2022

These companies face a unique set of data governance challenges regarding infrastructure and compliance on local, national, and international levels. Some organizations are choosing to confront these challenges with the help of tools like machine learning (ML) and artificial intelligence (AI) to automate, streamline, and scale compliance. .

Data Governance 76

Data Governance Strategy Enterprise Machine Learning 76

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. In this post, we will dive deeper into how members from both the first and second line of defense within a financial institution can adapt their model validation strategies in the context of modern ML methods. Validating Machine Learning Models.

Risk 52

Risk Modeling Metrics Business Objectives 52

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. .

Testing 307

Testing Machine Learning Consulting Data Quality 307

Smarten

APRIL 19, 2024

Assisted Predictive Modeling and Auto Insights to create predictive models using self-guiding UI wizard and auto-recommendations The Future of AI in Analytics The C=suite executive survey revealed that 93% felt that data strategy is critical to getting value from generative AI, but a full 57% had made no changes to their data.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

CIO Business Intelligence

MARCH 9, 2022

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. Each ETL step risks introducing failures or bugs that reduce data quality. .

Data Lake 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

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

CIO Business Intelligence

MARCH 22, 2022

To companies entrenched in decades-old business and IT processes, data fiefdoms, and legacy systems, the task may seem insurmountable. Develop a strategy to liberate data . Business units can bring in their own data, and access the superset of data aggregated from all the other different sources. .

Analytics 115

Analytics Key Performance Indicator Data Warehouse Data-driven 115

CIO Business Intelligence

JANUARY 31, 2023

These changes bring new challenges, but advancements in IT automation, artificial intelligence (AI) and machine learning (ML), and edge-computing capabilities will play a key role. Modern data analytics spans a range of technologies, from dedicated analytics platforms and databases to deep learning and artificial intelligence (AI).

Deep Learning 111

Deep Learning Cost-Benefit Data-driven Manufacturing 111

CIO Business Intelligence

APRIL 12, 2022

In addition, safety is a key requirement across rail, water, air, and roadways, often requiring split-second decisions that can often be enhanced by machine learning. To create a productive, cost-effective analytics strategy that gets results, you need high performance hardware that’s optimized to work with the software you use.

IT 72

IT Cost-Benefit IoT Machine Learning 72

DataRobot Blog

DECEMBER 20, 2022

Automating and optimizing their investment strategy. DataRobot combines these datasets and data types into one training dataset used to build machine learning models. You can understand the data and model’s behavior at any time. Estimating the growth potential of a city and neighborhood.

Management 59

Management Machine Learning Optimization 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

CIO Business Intelligence

JANUARY 20, 2023

Blocking the move to a more AI-centric infrastructure, the survey noted, are concerns about cost and strategy plus overly complex existing data environments and infrastructure. 2] Foundational considerations include compute power, memory architecture as well as data processing, storage, and security. 1] [link]. [2]

Analytics 137

Analytics Key Performance Indicator Unstructured Data IoT 137