O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. We are also beginning to see researchers share sample code written in popular open source libraries, and some even share pre-trained models. Model development. Model governance. Source: Ben Lorica.

Modeling 235

Modeling Machine Learning Testing Metrics 235

CIO Business Intelligence

NOVEMBER 27, 2024

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

Enterprise 141

Enterprise Data Quality Structured Data Modeling 141

CIO Business Intelligence

APRIL 8, 2025

As technology and business leaders, your strategic initiatives, from AI-powered decision-making to predictive insights and personalized experiences, are all fueled by data. Yet, despite growing investments in advanced analytics and AI, organizations continue to grapple with a persistent and often underestimated challenge: poor data quality.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102

CIO Business Intelligence

DECEMBER 12, 2024

Confidence from business leaders is often focused on the AI models or algorithms, Erolin adds, not the messy groundwork like data quality, integration, or even legacy systems. Data quality is a problem that is going to limit the usefulness of AI technologies for the foreseeable future, Brown adds.

IT 134

IT Data Quality Experimentation Machine Learning 134

David Menninger's Analyst Perspectives

NOVEMBER 19, 2024

To improve data reliability, enterprises were largely dependent on data-quality tools that required manual effort by data engineers, data architects, data scientists and data analysts.  With the aim of rectifying that situation, Bigeye’s founders set out to build a business around data observability.

Data Quality 130

Data Quality Dashboards Data-driven Software 130

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 278

Machine Learning Modeling Testing Risk Management 278

Analytics Vidhya

MAY 30, 2023

Introduction In the realm of machine learning, the veracity of data holds utmost significance in the triumph of models. Inadequate data quality can give rise to erroneous predictions, unreliable insights, and overall performance.

Machine Learning 325

Machine Learning Data Quality Modeling Analytics 325

KDnuggets

MARCH 10, 2022

Good quality data becomes imperative and a basic building block of an ML pipeline. The ML model can only be as good as its training data.

Machine Learning 158

Machine Learning Data Quality Modeling IT 158

Analytics Vidhya

MARCH 13, 2024

Introduction Whether you’re a fresher or an experienced professional in the Data industry, did you know that ML models can experience up to a 20% performance drop in their first year? Monitoring these models is crucial, yet it poses challenges such as data changes, concept alterations, and data quality issues.

Data Quality 319

Data Quality Modeling Analytics IT 319

AWS Big Data

AUGUST 8, 2024

They establish data quality rules to ensure the extracted data is of high quality for accurate business decisions. These rules commonly assess the data based on fixed criteria reflecting the current business state. In this post, we demonstrate how this feature works with an example.

Data Quality 113

Data Quality Statistics Visualization Metrics 113

O'Reilly on Data

NOVEMBER 19, 2024

Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. Also, in place of expensive retraining or fine-tuning for an LLM, this approach allows for quick data updates at low cost. at Facebook—both from 2020.

Unstructured Data 318

Unstructured Data Structured Data Statistics Modeling 318

Analytics Vidhya

JANUARY 2, 2024

Microsoft researchers have pioneered a groundbreaking approach in the realm of code language models, introducing CodeOcean and WaveCoder to redefine instruction tuning.

Data Quality 305

Data Quality Modeling Analytics 305

Smart Data Collective

APRIL 20, 2022

Companies that utilize data analytics to make the most of their business model will have an easier time succeeding with Amazon. One of the best ways to create a profitable business model with Amazon involves using data analytics to optimize your PPC marketing strategy.

Data Quality 128

Data Quality Analytics Testing Sales 128

datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

DataKitchen

JULY 12, 2023

The Syntax, Semantics, and Pragmatics Gap in Data Quality Validate Testing Data Teams often have too many things on their ‘to-do’ list. Each unit will have unique data sets with specific data quality test requirements. One of the standout features of DataOps TestGen is the power to auto-generate data tests.

Data Quality 130

Data Quality Testing Manufacturing Finance 130

Analytics Vidhya

FEBRUARY 20, 2023

Introduction Whenever we talk about building better forecasting models, the first and foremost step starts with detecting.

Forecasting 291

Forecasting Data Quality Modeling Analytics 291

CIO Business Intelligence

NOVEMBER 4, 2024

Over the next one to three years, 84% of businesses plan to increase investments in their data science and engineering teams, with a focus on generative AI, prompt engineering (45%), and data science/data analytics (44%), identified as the top areas requiring more AI expertise. Cost, by comparison, ranks a distant 10th.

Marketing 128

Marketing Data Quality Data Governance Digital Transformation 128

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. The model and the data specification become more important than the code.

Machine Learning 363

Machine Learning Statistics Data Quality Data Collection 363

TDAN

DECEMBER 4, 2024

We have lots of data conferences here. I’ve taken to asking a question at these conferences: What does data quality mean for unstructured data? Over the years, I’ve seen a trend — more and more emphasis on AI. This is my version of […]

Unstructured Data 52

Unstructured Data Data Quality Data Architecture Modeling 52

CIO Business Intelligence

OCTOBER 30, 2024

Whether it’s a financial services firm looking to build a personalized virtual assistant or an insurance company in need of ML models capable of identifying potential fraud, artificial intelligence (AI) is primed to transform nearly every industry. But adoption isn’t always straightforward.

Enterprise 139

Enterprise Data Architecture Unstructured Data Insurance 139

CIO Business Intelligence

MARCH 25, 2025

Data debt that undermines decision-making In Digital Trailblazer , I share a story of a private company that reported a profitable year to the board, only to return after the holiday to find that data quality issues and calculation mistakes turned it into an unprofitable one.

Risk 140

Risk Cost-Benefit Data-driven Digital Transformation 140

O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 349

Enterprise Deep Learning Data Governance Risk 349

Analytics Vidhya

DECEMBER 28, 2022

Introduction In deep learning, the activation functions are one of the essential parameters in training and building a deep learning model that makes accurate predictions. Choosing the best appropriate activation function can help one get better results with even reduced data quality; hence, […].

Deep Learning 277

Deep Learning Data Quality Data Science Publishing 277

CIO Business Intelligence

MARCH 13, 2024

But hearing those voices, and how to effectively respond, is dictated by the quality of data available, and understanding how to properly utilize it. “We We know in financial services and in a lot of verticals, we have a whole slew of data quality challenges,” he says. Traditionally, AI data quality has been a challenge.”

Data Quality 105

Data Quality Digital Transformation Enterprise Marketing 105

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. OwlDQ — Predictive data quality.

Testing 300

Testing Machine Learning Consulting Data Science 300

AWS Big Data

JUNE 6, 2023

We are excited to announce the General Availability of AWS Glue Data Quality. Our journey started by working backward from our customers who create, manage, and operate data lakes and data warehouses for analytics and machine learning. It takes days for data engineers to identify and implement data quality rules.

Data Quality 97

Data Quality Statistics Data Lake Visualization 97

AWS Big Data

DECEMBER 4, 2024

They’re taking data they’ve historically used for analytics or business reporting and putting it to work in machine learning (ML) models and AI-powered applications. Amazon SageMaker Unified Studio (Preview) solves this challenge by providing an integrated authoring experience to use all your data and tools for analytics and AI.

Data Analytics 86

Data Analytics Analytics Data Lake Data Quality 86

CIO Business Intelligence

NOVEMBER 18, 2024

If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless. By partnering with industry leaders, businesses can acquire the resources needed for efficient data discovery, multi-environment management, and strong data protection.

Data-driven 111

Data-driven Machine Learning ROI Uncertainty 111

CIO Business Intelligence

JANUARY 7, 2025

Transformational CIOs continuously invest in their operating model by developing product management, design thinking, agile, DevOps, change management, and data-driven practices. For AI to deliver safe and reliable results, data teams must classify data properly before feeding it to those hungry LLMs.

Digital Transformation 143

Digital Transformation Experimentation Cost-Benefit Strategy 143

CIO Business Intelligence

DECEMBER 10, 2024

Align data strategies to unlock gen AI value for marketing initiatives Using AI to improve sales metrics is a good starting point for ensuring productivity improvements have near-term financial impact. When considering the breadth of martech available today, data is key to modern marketing, says Michelle Suzuki, CMO of Glassbox.

Sales 143

Sales Metrics Data-driven Unstructured Data 143

AWS Big Data

OCTOBER 9, 2024

Some customers build custom in-house data parity frameworks to validate data during migration. Others use open source data quality products for data parity use cases. This takes away important person hours from the actual migration effort into building and maintaining a data parity framework.

Data Quality 96

Data Quality Data Lake Data Warehouse Metrics 96

CIO Business Intelligence

JANUARY 5, 2025

Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] Reliability and security is paramount.

Enterprise 124

Enterprise Cost-Benefit Unstructured Data Data Quality 124

AWS Big Data

OCTOBER 10, 2023

Due to the volume, velocity, and variety of data being ingested in data lakes, it can get challenging to develop and maintain policies and procedures to ensure data governance at scale for your data lake. Data confidentiality and data quality are the two essential themes for data governance.

Data Quality 104

Data Quality Data Governance Data Lake Testing 104

CIO Business Intelligence

FEBRUARY 26, 2025

We actually started our AI journey using agents almost right out of the gate, says Gary Kotovets, chief data and analytics officer at Dun & Bradstreet. The knowledge management systems are up to date and support API calls, but gen AI models communicate in plain English. Thats what Cisco is doing.

IT 141

IT Enterprise Interactive Data Quality 141

CIO Business Intelligence

NOVEMBER 13, 2024

One is going through the big areas where we have operational services and look at every process to be optimized using artificial intelligence and large language models. But a substantial 23% of respondents say the AI has underperformed expectations as models can prove to be unreliable and projects fail to scale.

IT 141

IT Consulting ROI Cost-Benefit 141

Analytics Vidhya

DECEMBER 30, 2022

This article was published as a part of the Data Science Blogathon. Introduction In machine learning, the data is an essential part of the training of machine learning algorithms. The amount of data and the data quality highly affect the results from the machine learning algorithms.

Machine Learning 291

Machine Learning Data Quality Data Science Publishing 291