Smarten

APRIL 25, 2024

How Can I Ensure Data Quality and Gain Data Insight Using Augmented Analytics? There are many business issues surrounding the use of data to make decisions. One such issue is the inability of an organization to gather and analyze data.

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Data Quality Analytics Machine Learning Visualization 62

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.

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Data Quality Statistics Data Lake Visualization 95

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.

Data Quality 144

Data Quality Machine Learning Modeling IT 144

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.

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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.

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Data Quality Analytics Testing Sales 114

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.

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Data Quality Data Governance Data Lake Testing 92

IBM Big Data Hub

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Data Quality Data Architecture Strategy Data Lake 52

Dataiku

APRIL 19, 2021

Data needs to be valuable, thus of high quality , to drive machine learning model success.

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Data Quality Machine Learning Modeling Management 105

TDAN

MAY 1, 2024

If you look at Google Trends, you’ll see that the explosion of searches for generative AI (GenAI) and large language models correlates with the introduction of ChatGPT back in November 2022.

Data Quality 52

Data Quality Modeling Data Governance Management 52

CIO Business Intelligence

AUGUST 2, 2022

In the two or three years since then we have models that have significantly helped our business in such areas as predictive maintenance by combining data from different sources. We are also combining that with data from different sources as a pilot to see if it makes sense and tests out a hypothesis. That’s one.

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Data Quality Digital Transformation Machine Learning Predictive Analytics 82

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.

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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.

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Octopai

MARCH 23, 2022

If today, federal inspections and authorizations of meatpacking plants are made with some level of reliance on data systems (and they almost certainly are), then dirty data could even lead directly to dirty meat. Cleaning Up Dirty Data. Data Supervision. Everyone agrees that data quality is important.

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Data Quality Reporting Modeling Interactive 91

Alation

JANUARY 20, 2022

Several weeks ago (prior to the Omicron wave), I got to attend my first conference in roughly two years: Dataversity’s Data Quality and Information Quality Conference. Ryan Doupe, Chief Data Officer of American Fidelity, held a thought-provoking session that resonated with me. Step 2: Data Definitions.

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Data Quality Data Governance Metrics Statistics 94

AWS Big Data

JUNE 6, 2023

Poor-quality data can lead to incorrect insights, bad decisions, and lost opportunities. AWS Glue Data Quality measures and monitors the quality of your dataset. It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines.

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Data Quality Data Lake Visualization Data-driven 70

Dataiku

AUGUST 10, 2021

Using machine learning (ML) to predict customer growth, churn, and to find insights in the data is not only a trendy topic, but also something that can bring a lot of value.

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Data Quality Machine Learning Modeling Management 105

Dataiku

OCTOBER 8, 2020

Data needs to be valuable (high quality, labeled, and organized) to drive machine learning model success. This infographic reveals some of the challenges data leaders face when it comes to data quality as well as a specific focus on the need for data labeling through active learning.

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Data Quality Machine Learning Modeling IT 66

IBM Big Data Hub

NOVEMBER 4, 2022

Data is the new oil and organizations of all stripes are tapping this resource to fuel growth. However, data quality and consistency are one of the top barriers faced by organizations in their quest to become more data-driven. Unlock quality data with IBM. and its leading data observability offerings.

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Data Quality Metadata Data Governance Data-driven 56

DataKitchen

MAY 7, 2024

DataKitchen Training And Certification Offerings For Individual contributors with a background in Data Analytics/Science/Engineering Overall Ideas and Principles of DataOps DataOps Cookbook (200 page book over 30,000 readers, free): DataOps Certificatio n (3 hours, online, free, signup online): DataOps Manifesto (over 30,000 signatures) One (..)

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Data Quality Testing Consulting Metrics 161

DataRobot Blog

MAY 10, 2022

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States.

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Analytics Vidhya

FEBRUARY 20, 2023

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

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CIO Business Intelligence

MAY 3, 2022

On creating a data hub: We began looking at the need for a new approach into data quality and data governance for the company in late 2020. But we rely on the business, functional, and geographic spokes to perform the cleansing and the long-term maintenance of our data.

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Data Quality Machine Learning Business Objectives Business Intelligence 77

Smarten

DECEMBER 11, 2019

Data Quality vs. Data Agility – A Balanced Approach! If you want to create an environment with a culture and processes that are balanced to accommodate data agility and data quality, you can start here: Benefits of Augmented Analytics Balance.

Data Quality 52

Data Quality Predictive Modeling Data-driven Visualization 52

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 191

Machine Learning Modeling Testing Risk Management 191

DataKitchen

MAY 10, 2024

The Third of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production. This process is critical as it ensures data quality from the onset. Examples include regular loading of CRM data and anomaly detection. Is My Model Still Accurate?

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Testing Metrics Data Quality Dashboards 124

DataKitchen

FEBRUARY 27, 2024

Your LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers The rise of Large Language Models (LLMs) such as GPT-4 marks a transformative era in artificial intelligence, heralding new possibilities and challenges in equal measure. Without this, LLMs cannot reliably interpret or generate meaningful outputs.

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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, […].

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Deep Learning Data Quality Data Science Publishing 216

Anmut

MARCH 22, 2023

Data maturity models are a crucial step for any organisation looking to improve their data, informing if your current data practices are helping, or holding back, your business. ? Click the links below to navigate to different sections What are data maturity models? Why do we need data maturity models?

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Measurement Modeling Data Quality Data Governance 52

Ontotext

MAY 26, 2021

If these sample sets are not of high quality, clean and representative, we cannot hope to train the algorithms to get useful results. Beyond training, each and every AI system, be it based on symbolic rules or statistical models, has to be evaluated, in the same way children take exams to graduate.

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Data Quality Machine Learning Measurement Metadata 87

Octopai

OCTOBER 26, 2022

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. The same goes for data analytics products. Let’s take a look.

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Data Quality Data Analytics Analytics Manufacturing 52

DataRobot Blog

MAY 26, 2022

Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52

Risk Modeling Metrics Business Objectives 52

O'Reilly on Data

FEBRUARY 11, 2020

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Data quality might get worse before it gets better.

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Data Quality Metadata Data Governance Publishing 305

David Menninger's Analyst Perspectives

MARCH 17, 2022

Organizations face various challenges with analytics and business intelligence processes, including data curation and modeling across disparate sources and data warehouses, maintaining data quality and ensuring security and governance.

Business Intelligence 204

Business Intelligence Data Warehouse Data Quality Dashboards 204

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.

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Data Governance Unstructured Data Metadata Data Lake 96

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 259

Machine Learning Data Quality Statistics Modeling 259

Alation

JUNE 2, 2022

These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. Currently, models are managed by modelers and by the software tools they use, which results in a patchwork of control, but not on an enterprise level. And until recently, such governance processes have been fragmented.

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Modeling Data Governance Statistics Unstructured Data 52

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.

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