Dataversity

APRIL 18, 2022

Since the data from such processes is growing, data controls may not be strong enough to ensure the data is qualitative. That’s where Data Quality dimensions come into play. […]. The post Data Quality Dimensions Are Crucial for AI appeared first on DATAVERSITY.

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Dataversity

MAY 31, 2024

Data: Data is number, characters, images, audio, video, symbols, or any digital repository on which operations can be performed by a computer. Algorithm: An algorithm […] The post 12 Key AI Patterns for Improving Data Quality (DQ) appeared first on DATAVERSITY.

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Smart Data Collective

SEPTEMBER 29, 2022

Find out in this article how your company can benefit from the use of OCR. This article reveals all! The post Data-Driven Companies Leverage OCR for Optimal Data Quality appeared first on SmartData Collective. Even so, it takes time and can quickly become an obstacle to the smooth running of your business.

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Dataversity

APRIL 1, 2024

This reliance has spurred a significant shift across industries, driven by advancements in artificial intelligence (AI) and machine learning (ML), which thrive on comprehensive, high-quality data.

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Dataversity

FEBRUARY 13, 2023

The key to being truly data-driven is having access to accurate, complete, and reliable data. In fact, Gartner recently found that organizations believe […] The post How to Assess Data Quality Readiness for Modern Data Pipelines appeared first on DATAVERSITY.

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Dataversity

AUGUST 10, 2023

We’ve all generally heard that data quality issues can be catastrophic. But what does that look like for data teams, in terms of dollars and cents? And who is responsible for dealing with data quality issues?

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Dataversity

FEBRUARY 10, 2021

In this blog, we will take a look at: The impact poor Data Quality has on organizations and practical advice for how to overcome this challenge through the use of feedback loops. Poor Data Quality can cost organizations millions each year. It can lead to incorrect decisions, […].

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Analysts Corner

JANUARY 12, 2023

In this article, we present a brief overview of compliance and regulations, discuss the cost of non-compliance and some related statistics, and the role data quality and data governance play in achieving compliance. The average cost of a data breach among organizations surveyed reached $4.24

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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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Dataversity

MARCH 30, 2023

These initiatives may provide companies with short-term reprieve during times of disruption, […] The post Optimizing Data Quality to Navigate Economic Turbulence appeared first on DATAVERSITY.

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Dataversity

JULY 3, 2023

What are the most common causes of Data Quality issues? The conventional answer to that question includes problems like inaccurate data, duplicate data, or data containing missing values.

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Dataversity

DECEMBER 3, 2021

Three big shifts came this year, namely in the realms of consumer data privacy, the use of third-party cookies vs. first-party data, and the regulations and expectations […]. The post What to Expect in 2022: Data Privacy, Data Quality, and More appeared first on DATAVERSITY.

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Dataversity

MARCH 14, 2022

The post When It Comes to Data Quality, Businesses Get Out What They Put In appeared first on DATAVERSITY. The stakes are high, so you search the web and find the most revered chicken parmesan recipe around. At the grocery store, it is immediately clear that some ingredients are much more […].

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Dataversity

FEBRUARY 15, 2023

Project Open Data is a place where fostering collaboration and promoting improvement in open data policy is intended. This […] The post How Can Project Open Data Improve Data Quality? appeared first on DATAVERSITY.

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Dataversity

JANUARY 13, 2023

When we talk to prospective customers, their first questions are usually around the fundamentals of data quality, including what it is, how we measure it, what happens when it goes south, and how data quality issues can be prevented.

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Dataversity

MARCH 2, 2022

With the accelerating adoption of Snowflake as the cloud data warehouse of choice, the need for autonomously validating data has become critical. While existing Data Quality solutions provide the ability to validate Snowflake data, these solutions rely on a rule-based approach that is […].

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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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Dataversity

JANUARY 10, 2022

The road to better Data Quality is a path most data-driven organizations are already on. The path becomes bumpy for organizations when stakeholders are constantly dealing with data that is either incomplete or inaccurate. That scenario is far too familiar for most organizations and creates a lack of trust in Data Quality.

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Dataversity

AUGUST 15, 2022

If data is the new oil, then high-quality data is the new black gold. Just like with oil, if you don’t have good data quality, you will not get very far. So, what can you do to ensure your data is up to par and […]. You might not even make it out of the starting gate.

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Dataversity

MARCH 3, 2021

The post Being Data-Driven Means Embracing Data Quality and Consistency Through Data Governance appeared first on DATAVERSITY. They want to improve their decision making, shifting the process to be more quantitative and less based on gut and experience.

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Dataversity

FEBRUARY 4, 2022

I suspect the failure rates are even higher, as more and more organizations today are trying to utilize the power of data to improve their services or create new revenue streams. Not having the “right” data continues to prevent […]. The post Why Synthetic Data Still Has a Data Quality Problem appeared first on DATAVERSITY.

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The Data Administration Newsletter

MARCH 15, 2022

1 In this article, I will apply it to the topic of data quality. I will do so by comparing two butterflies, each that represent a common use of data quality: firstly and most commonly in situ for existing systems, and secondly for use […]. We know the phrase, “Beauty is in the eye of the beholder.”1

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Dataversity

AUGUST 2, 2022

Unfortunately, most organizations – across all industries – have Data Quality problems that are directly impacting their company’s performance. The post Why Data Quality Problems Plague Most Organizations (and What to Do About It) appeared first on DATAVERSITY.

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Dataversity

JANUARY 24, 2022

Business users want to know where that data lives, understand if people are accessing the right data at the right time, and be assured that the data is of high quality. But they are not always out shopping for Data Quality […].

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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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Dataversity

DECEMBER 20, 2022

The post Data Quality Best Practices to Discover the Hidden Potential of Dirty Data in Health Care appeared first on DATAVERSITY. Health plans will […].

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The Data Administration Newsletter

SEPTEMBER 18, 2024

As organizations rush headlong into digital transformation initiatives, a critical factor often gets overlooked: data quality.

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Dataversity

JANUARY 8, 2021

The post Good AI in 2021 Starts with Great Data Quality appeared first on DATAVERSITY. Achieving good AI is a whole other story. AI initiatives can take a lot of time and effort to get up and running, often exceeding initial budget and […].

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Data Pine

SEPTEMBER 29, 2022

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

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Cprime

JANUARY 19, 2021

To prepare plenty of food boxes, you’ll want your data scientist to analyze historical selling data to predict the number of food boxes you will likely sell during the Christmas period. This article explores data quality, the link between data quality and data analysis, why data quality matters, and how you can improve it.

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The Data Administration Newsletter

NOVEMBER 20, 2024

In my last article, “The Shift from Syntactic to Semantic Data Curation and What It Means for Data Quality” published in the August 2024 issue of this newsletter, I argued how the adoption of generative AI will change the focus and scope of data quality management (DQM).

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The Data Administration Newsletter

OCTOBER 18, 2022

Last week, we presented a webinar in our Data Governance — Best Practices series on data quality. Obviously, we’re a […].

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Tableau

APRIL 12, 2021

With data discovery as an important part of the cataloging experience, we want you to get the most relevant search results when looking for databases and tables in Tableau Server or Online. Our customers love data quality warnings, so we’ve also added a new feature based on a popular request! Starting with Tableau 2021.1,

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Analysts Corner

DECEMBER 6, 2022

It is important to assess the data to understand their fitness for use and shortcomings, before using the same to derive insights and make decisions. Hope you found this article useful! Future articles on data will focus on data quality dimensions, data quality assessment, and other aspects of data quality and data governance.

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Dataversity

NOVEMBER 1, 2024

In a recent conversation with one of our customers, my team uncovered a troubling reality: Poor data quality wasn’t just impacting their bottom line but also causing friction between departments.

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The Data Administration Newsletter

DECEMBER 2, 2020

Ensuring data quality is an important aspect of data management and these days, DBAs are increasingly being called upon to deal with the quality of the data in their database systems more than ever before. The importance of quality data cannot be overstated.

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Analysts Corner

OCTOBER 30, 2024

This section explores four main challenges: data quality, interpretability, generalizability, and ethical considerations, and discusses strategies for addressing each issue. Download end-to-end articles with codes 1.

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