Analytics Vidhya

NOVEMBER 8, 2023

In the data-driven world […] The post Monitoring Data Quality for Your Big Data Pipelines Made Easy appeared first on Analytics Vidhya. Determine success by the precision of your charts, the equipment’s dependability, and your crew’s expertise. A single mistake, glitch, or slip-up could endanger the trip.

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Data Science Blog

OCTOBER 1, 2024

Just like a skyscraper’s stability depends on a solid foundation, the accuracy and reliability of your insights rely on top-notch data quality. Enter Generative AI – a game-changing technology revolutionizing data management and utilization. Businesses must ensure their data is clean, structured, and reliable.

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insideBIGDATA

JULY 24, 2024

In this contributed article, Subbiah Muthiah, CTO of Emerging Technologies at Qualitest, takes a deep dive into how raw data can throw specialized AI into disarray. While raw data has its uses, properly processed data is vital to the success of niche AI.

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KDnuggets

JANUARY 12, 2023

Bad data costs organizations money, reputation, and time. Hence it is very important to monitor and validate data quality continuously.

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KDnuggets

JANUARY 17, 2022

Incorrect or unclean data leads to false conclusions. The time you take to understand and clean the data is vital to the outcome and quality of the results. Data Quality always takes the win against complex fancy algorithms.

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Towards AI

OCTOBER 31, 2024

Data analytics has become a key driver of commercial success in recent years. The ability to turn large data sets into actionable insights can mean the difference between a successful campaign and missed opportunities. Flipping the paradigm: Using AI to enhance data quality What if we could change the way we think about data quality?

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KDnuggets

NOVEMBER 22, 2022

Ensuring data quality guarantees more data-informed decisions. Hence, this article highlights the common data quality issues and ways to overcome them.

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insideBIGDATA

JUNE 6, 2023

In this contributed article, Peter Nagel, VP of Engineering at Noyo, addresses the benefits/insurance industry’s roadblocks and opportunities — and why some of the most interesting data innovations will soon be happening in benefits.

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KDnuggets

OCTOBER 6, 2022

Join Lior Gavish, co-author and Monte Carlo co-founder, Oct 12 @ 1 PM ET, as he explores the latest in data quality techniques with a panel of some of the foremost experts.

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The Data Administration Newsletter

NOVEMBER 20, 2024

Data quality issues continue to plague financial services organizations, resulting in costly fines, operational inefficiencies, and damage to reputations. Key Examples of Data Quality Failures — […]

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Precisely

JULY 12, 2024

When companies work with data that is untrustworthy for any reason, it can result in incorrect insights, skewed analysis, and reckless recommendations to become data integrity vs data quality. Two terms can be used to describe the condition of data: data integrity and data quality.

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Flipboard

NOVEMBER 14, 2023

Modern data quality practices leverage advanced technologies, automation, and machine learning to handle diverse data sources, ensure real-time processing, and foster collaboration across stakeholders.

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Flipboard

NOVEMBER 16, 2023

Learn how to perform data quality checks using pandas. From detecting missing records to outliers, inconsistent data entry and more.

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KDnuggets

MARCH 23, 2023

This article highlights the significance of ensuring high-quality data and presents six key dimensions for measuring it. These dimensions include Completeness, Consistency, Integrity, Timelessness, Uniqueness, and Validity.

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insideBIGDATA

MAY 1, 2024

In this contributed article, Stephany Lapierre, Founder and CEO of Tealbook, discusses how AI can help streamline procurement processes, reduce costs and improve supplier management, while also addressing common concerns and challenges related to AI implementation like data privacy, ethical considerations and the need for human oversight.

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Dataversity

APRIL 22, 2024

So why are many technology leaders attempting to adopt GenAI technologies before ensuring their data quality can be trusted? Reliable and consistent data is the bedrock of a successful AI strategy.

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Dataconomy

APRIL 12, 2022

Did you know that common data quality difficulties affect 91% of businesses? Incorrect data, out-of-date contacts, incomplete records, and duplicates are the most prevalent.

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Precisely

NOVEMBER 5, 2024

Key Takeaways: Data quality is the top challenge impacting data integrity – cited as such by 64% of organizations. Data trust is impacted by data quality issues, with 67% of organizations saying they don’t completely trust their data used for decision-making.

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Pickl AI

JULY 24, 2024

Summary: Data quality is a fundamental aspect of Machine Learning. Poor-quality data leads to biased and unreliable models, while high-quality data enables accurate predictions and insights. What is Data Quality in Machine Learning? Bias in data can result in unfair and discriminatory outcomes.

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Precisely

SEPTEMBER 9, 2024

Key Takeaways: • Implement effective data quality management (DQM) to support the data accuracy, trustworthiness, and reliability you need for stronger analytics and decision-making. Embrace automation to streamline data quality processes like profiling and standardization. What is Data Quality Management (DQM)?

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insideBIGDATA

MAY 28, 2023

iMerit, a leading artificial intelligence (AI) data solutions company, released its 2023 State of ML Ops report, which includes a study outlining the impact of data on wide-scale commercial-ready AI projects.

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KDnuggets

NOVEMBER 22, 2023

This week on KDnuggets: Learn how to perform data quality checks using pandas, from detecting missing records to outliers, inconsistent data entry and more • The top vector databases are known for their versatility, performance, scalability, consistency, and efficient algorithms in storing, indexing, and querying vector embeddings for AI applications (..)

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Precisely

JANUARY 9, 2024

Data can only deliver business value if it has high levels of data integrity. That starts with good data quality, contextual richness, integration, and sound data governance tools and processes. This article focuses primarily on data quality. How can you assess your data quality?

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Dataversity

NOVEMBER 6, 2023

This is my monthly check-in to share with you the people and ideas I encounter as a data evangelist with DATAVERSITY. This month we’re talking about Data Quality (DQ). Read last month’s column here.)

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KDnuggets

AUGUST 18, 2022

Just as Maslow identified a hierarchy of needs for people, data teams have a hierarchy of needs, beginning with data freshness; including volumes, schemas, and values; and culminating with lineage.

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DagsHub

AUGUST 23, 2024

As such, the quality of their data can make or break the success of the company. This article will guide you through the concept of a data quality framework, its essential components, and how to implement it effectively within your organization. What is a data quality framework?

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databricks

AUGUST 1, 2024

At Data and AI Summit, we announced the general availability of Databricks Lakehouse Monitoring. Our unified approach to monitoring data and AI.

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Dataversity

OCTOBER 25, 2023

In fact, it’s been more than three decades of innovation in this market, resulting in the development of thousands of data tools and a global data preparation tools market size that’s set […] The post Why Is Data Quality Still So Hard to Achieve? appeared first on DATAVERSITY.

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Dataversity

AUGUST 1, 2024

Welcome to the latest edition of Mind the Gap, a monthly column exploring practical approaches for improving data understanding and data utilization (and whatever else seems interesting enough to share). Last month, we explored the rise of the data product. This month, we’ll look at data quality vs. data fitness.

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SAS Software

APRIL 27, 2023

Data quality: The foundation for trustworthy AI was published on SAS Voices by Reggie Townsend After a pandemic, global supply chain disruptions and resulting economic fragility, executives understandably consider adaptability, durability and how best to operate with a strength of character – all attributes that define resilience.

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phData

OCTOBER 25, 2024

“Quality over Quantity” is a phrase we hear regularly in life, but when it comes to the world of data, we often fail to adhere to this rule. Data Quality Monitoring implements quality checks in operational data processes to ensure that the data meets pre-defined standards and business rules.

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The Data Administration Newsletter

MARCH 6, 2024

Data quality is measured across dimensions, but why? Data quality metrics exist to support the business. The value of a data quality program resides in the ability to take action to improve data to make it more correct and therefore more valuable.

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Flipboard

JUNE 5, 2023

True data quality simplification requires transformation of both code and data, because the two are inextricably linked. Code sprawl and data siloing both imply bad habits that should be the exception, rather than the norm.

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The Data Administration Newsletter

MAY 4, 2022

Getting to great data quality need not be a blood sport! This article aims to provide some practical insights gained from enterprise master data quality projects undertaken within the past […].

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insideBIGDATA

JUNE 15, 2023

Jason Smith, Chief Technology Officer, AI & Analytics at Within3, highlights how many life science data sets contain unclean, unstructured, or highly-regulated data that reduces the effectiveness of AI models. Life science companies must first clean and harmonize their data for effective AI adoption.

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Dataversity

JULY 12, 2024

At their core, LLMs are trained on large amounts of content and data, and the architecture […] The post RAG (Retrieval Augmented Generation) Architecture for Data Quality Assessment appeared first on DATAVERSITY. It is estimated that by 2025, 50% of digital work will be automated through these LLM models.

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Dataversity

AUGUST 22, 2022

They have the data they need, but due to the presence of intolerable defects, they cannot use it as needed. These defects – also called Data Quality issues – must be fetched and fixed so that data can be used for successful business […].

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