CIO Business Intelligence

DECEMBER 10, 2024

At its core, that process involves extracting key information about the individual customer, unstructured data from medical records and financial data and then analyzing that data to make an underwriting decision. To learn more, visit us here.

Modeling 116

Modeling Insurance Unstructured Data Experimentation 116

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 304

Testing Machine Learning Consulting Data Science 304

CIO Business Intelligence

FEBRUARY 20, 2025

Decades-old apps designed to retain a limited amount of data due to storage costs at the time are also unlikely to integrate easily with AI tools, says Brian Klingbeil, chief strategy officer at managed services provider Ensono. The aim is to create integration pipelines that seamlessly connect different systems and data sources.

Insurance 108

Insurance Cost-Benefit Unstructured Data Data Lake 108

CIO Business Intelligence

JANUARY 9, 2025

Before LLMs and diffusion models, organizations had to invest a significant amount of time, effort, and resources into developing custom machine-learning models to solve difficult problems. In many cases, this eliminates the need for specialized teams, extensive data labeling, and complex machine-learning pipelines.

Software 128

Software Enterprise Key Performance Indicator Machine Learning 128

O'Reilly on Data

SEPTEMBER 19, 2023

But the grouping and summarizing just wasn’t exciting enough for the data addicts. They’d grown tired of learning what is; now they wanted to know what’s next. Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. Those algorithms packaged with scikit-learn?

Machine Learning 341

Machine Learning Testing Modeling Cost-Benefit 341

David Menninger's Analyst Perspectives

APRIL 24, 2025

There are multiple challenges to delivering AI-ready data, including combining structured and unstructured data, ensuring that the combined data can be trusted, and validating that the connections between data and entities are captured and exposed to relevant applications.

Unstructured Data 130

Unstructured Data Machine Learning Software Data Processing 130

O'Reilly on Data

FEBRUARY 11, 2020

Just 20% of organizations publish data provenance and data lineage. Adopting AI can help data quality. Almost half (48%) of respondents say they use data analysis, machine learning, or AI tools to address data quality issues. Can AI be a catalyst for improved data quality?

Data Quality 363

Data Quality Metadata Data Governance Publishing 363

AWS Big Data

NOVEMBER 21, 2024

Amazon EMR is a cloud big data platform for petabyte-scale data processing, interactive analysis, streaming, and machine learning (ML) using open source frameworks such as Apache Spark , Presto and Trino , and Apache Flink. Under Allocation strategy , select Apply allocation strategy.

Metrics 84

Metrics Machine Learning Strategy Big Data 84

O'Reilly on Data

MARCH 25, 2025

Two big things: They bring the messiness of the real world into your system through unstructured data. People have been building data products and machine learning products for the past couple of decades. They used some local embeddings and played around with different chunking strategies.

Testing 177

Testing Data-driven Software Measurement 177

CIO Business Intelligence

APRIL 8, 2025

I aim to outline pragmatic strategies to elevate data quality into an enterprise-wide capability. However, even the most sophisticated models and platforms can be undone by a single point of failure: poor data quality. This challenge remains deceptively overlooked despite its profound impact on strategy and execution.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102

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. Geet our bite-sized free summary and start building your data skills! What Is A Data Science Tool?

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Cloudera

JANUARY 7, 2025

In todays dynamic digital landscape, multi-cloud strategies have become vital for organizations aiming to leverage the best of both cloud and on-premises environments. As enterprises navigate complex data-driven transformations, hybrid and multi-cloud models offer unmatched flexibility and resilience.

Cost-Benefit 82

Cost-Benefit Optimization Data-driven Strategy 82

Smart Data Collective

MAY 13, 2022

AI and machine learning. Before you can have AI-driven apps, you need to train a machine learning model to do the work. This means feeding the machine with vast amounts of data, from structured to unstructured data, which will help the device learn how to think, process information, and act like humans.

Business Analytics 141

Business Analytics Analytics Unstructured Data Data-driven 141

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%

Digital Transformation 143

Digital Transformation Unstructured Data Machine Learning Risk Management 143

Smart Data Collective

JUNE 10, 2022

The average data scientist earns over $108,000 a year. The interdisciplinary field of data science involves using processes, algorithms, and systems to extract knowledge and insights from both structured and unstructured data and then applying the knowledge gained from that data across a wide range of applications.

Data Science 144

Data Science Machine Learning Statistics Business Intelligence 144

CIO Business Intelligence

MARCH 21, 2022

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. Semi-structured data falls between the two.

Unstructured Data 137

Unstructured Data Data Analytics Analytics Data Science 137

Smart Data Collective

FEBRUARY 3, 2021

A good example of this, an investment strategy like Fibonacci trading uses the Fibonacci sequence. The strategy is a reflection of nature since it orders the structures in line with the Fibonacci sequence. Traders have been using this strategy for quite some time. What Impact Is Big Data Having Towards Investing?

Big Data 140

Big Data Marketing Data Analytics Analytics 140

CIO Business Intelligence

MARCH 27, 2023

When he’s not immersed in cybersecurity, hybrid cloud strategy, or app modernization, David Reis, CIO at the University of Miami Health System and the Miller School of Medicine, spends his time working with the board of directors and top leadership to reimagine healthcare and take the lead driving digital transformation.

Strategy 110

Strategy Digital Transformation Insurance Technology 110

datapine

DECEMBER 4, 2019

Considered a new big buzz in the computing and BI industry, it enables the digestion of massive volumes of structured and unstructured data that transform into manageable content. It is the combination of several data processes that, instead of just giving back data, but provides a valuable, strategy-changing recommendation.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Cloudera

AUGUST 20, 2021

How CDP Enables and Accelerates Data Product Ecosystems. A multi-purpose platform focused on diverse value propositions for data products. Data Types and Sources: The multitude of data experiences enable efficient processing of different data types, such as structured and unstructured data collected from any potential source.

Strategy 98

Strategy Visualization Cost-Benefit Data Warehouse 98

Analytics Vidhya

MAY 29, 2023

The two pillars of data analytics include data mining and warehousing. They are essential for data collection, management, storage, and analysis. Providing insights into the trends, prediction, and appropriate strategy for the company and serving numerous other uses are distinct.

Data mining 307

Data mining Data Collection Strategy Data Analytics 307

CIO Business Intelligence

SEPTEMBER 12, 2024

For most organizations, the effective use of AI is essential for future viability and, in turn, requires large amounts of accurate and accessible data. Across industries, 78 % of executives rank scaling AI and machine learning (ML) use cases to create business value as their top priority over the next three years.

Unstructured Data 120

Unstructured Data Data Quality Structured Data Machine Learning 120

CIO Business Intelligence

AUGUST 9, 2024

At Atlanta’s Hartsfield-Jackson International Airport, an IT pilot has led to a wholesale data journey destined to transform operations at the world’s busiest airport, fueled by machine learning and generative AI. They’re trying to get a handle on their data estate right now.

Data Transformation 127

Data Transformation Machine Learning Data Lake Dashboards 127

Timo Elliott

JANUARY 3, 2022

Today’s data volumes have long since exceeded the capacities of straightforward human analysis, and so-called “unstructured” data, not stored in simple tables and columns, has required new tools and techniques. Improving data quality. Data augmentation. Learn More.

IT 143

IT Unstructured Data Data Quality Machine Learning 143

CIO Business Intelligence

MARCH 29, 2024

That’s why Rocket Mortgage has been a vigorous implementor of machine learning and AI technologies — and why CIO Brian Woodring emphasizes a “human in the loop” AI strategy that will not be pinned down to any one generative AI model. It’s a powerful strategy.” The rest are on premises. That’s really valuable.”

Data Lake 134

Data Lake Machine Learning Data Warehouse Unstructured Data 134

CIO Business Intelligence

MARCH 9, 2022

As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio.

Data Lake 119

Data Lake Unstructured Data Data Warehouse Big Data 119

CIO Business Intelligence

OCTOBER 8, 2024

By Bryan Kirschner, Vice President, Strategy at DataStax Data scientists have long struggled with silos and cycle time. That’s partly because of an underlying structural tension between the traditional data science mission of turning “data into insights” versus the on-the-ground game of turning “context into action.”

Unstructured Data 118

Unstructured Data Data Science Uncertainty Sales 118

AWS Big Data

FEBRUARY 29, 2024

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

Data Governance 123

Data Governance Unstructured Data Metadata Data Lake 123

AWS Big Data

SEPTEMBER 12, 2024

As it relates to the use case in the post, ZS is a global leader in integrated evidence and strategy planning (IESP), a set of services that help pharmaceutical companies to deliver a complete and differentiated evidence package for new medicines. We use various chunking strategies to enhance text comprehension.

Unstructured Data 110

Unstructured Data Metadata Machine Learning Consulting 110

CIO Business Intelligence

AUGUST 11, 2023

How natural language processing works NLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning.

Unstructured Data 142

Unstructured Data Data Science Machine Learning Data mining 142

CIO Business Intelligence

FEBRUARY 16, 2023

My vision is that I can give the keys to my businesses to manage their data and run their data on their own, as opposed to the Data & Tech team being at the center and helping them out,” says Iyengar, director of Data & Tech at Straumann Group North America. The offensive side? The company’s Findability.ai

Unstructured Data 106

Unstructured Data Data Lake Prescriptive Analytics Data Warehouse 106

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.

Analytics 137

Analytics Key Performance Indicator Unstructured Data Deep Learning 137

AWS Big Data

MAY 28, 2024

Large language models (LLMs) such as Anthropic Claude and Amazon Titan have the potential to drive automation across various business processes by processing both structured and unstructured data. Redshift Serverless is a fully functional data warehouse holding data tables maintained in real time.

Unstructured Data 118

Unstructured Data Data Warehouse Structured Data Testing 118

CIO Business Intelligence

FEBRUARY 10, 2023

Deploying new data types for machine learning Mai-Lan Tomsen-Bukovec, vice president of foundational data services at AWS, sees the cloud giant’s enterprise customers deploying more unstructured data, as well as wider varieties of data sets, to inform the accuracy and training of ML models of late.

Unstructured Data 106

Unstructured Data Data Lake Machine Learning Enterprise 106

CIO Business Intelligence

FEBRUARY 23, 2024

Generative AI takes a front seat As for that AI strategy, American Honda’s deep experience with machine learning positions it well to capitalize on the next wave: generative AI. The key to a successful AI strategy, in part, is the quality and cleanliness of both structured and unstructured data, he says.

IT 122

IT Manufacturing Unstructured Data Strategy 122

CIO Business Intelligence

SEPTEMBER 30, 2024

Like many organizations, Indeed has been using AI — and more specifically, conventional machine learning models — for more than a decade to bring improvements to a host of processes. “So one tiny little sentence is better for job seekers and employers,” she says. Everyone is looking at AI to optimize and gain efficiencies, for sure.

Machine Learning 141

Machine Learning ROI Data Processing Optimization 141

Cloudera

DECEMBER 11, 2020

Insurance and finance are two industries that rely on measuring risk with historical data models. They have traditionally been slower-moving to adopt new structured and unstructured data inputs as regulatory considerations are always top of mind. This can be done at speed, and at scale. What if 2020 is an anomaly?

Insurance 95

Insurance Digital Transformation Unstructured Data Machine Learning 95