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

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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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

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

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

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 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

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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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

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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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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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

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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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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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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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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The Data Administration Newsletter

NOVEMBER 14, 2023

Data empowers businesses to gain valuable insights into industry trends and fosters profitable decision-making for long-term growth. No wonder businesses of all sizes are switching to data-driven culture from conventional practices.

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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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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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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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The Data Administration Newsletter

DECEMBER 19, 2023

One of the reasons why there’s always excess production in the textile sector is the stringent requirement of meeting set quality standards. As far as healthcare is concerned, surprisingly, only two out of five health executives believe they receive healthy data through […]

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The Data Administration Newsletter

AUGUST 31, 2021

Data is everywhere! But can you find the data you need? What can be done to ensure the quality of the data? How can you show the value of investing in data? Can you trust it when you get it? These are not new questions, but many people still do not know how to practically […].

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The Data Administration Newsletter

OCTOBER 3, 2023

My recent columns have focused on actionable initiatives that can both deliver business value, providing a tangible achievement, and raise the profile of the data management organization data management organization (DMO).(For In that light, let’s […]

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Dataversity

JANUARY 19, 2024

Do you know the costs of poor data quality? Below, I explore the significance of data observability, how it can mitigate the risks of bad data, and ways to measure its ROI. Data has become […] The post Putting a Number on Bad Data appeared first on DATAVERSITY.

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The Data Administration Newsletter

APRIL 5, 2022

Regardless of how accurate a data system is, it yields poor results if the quality of data is bad. As part of their data strategy, a number of companies have begun to deploy machine learning solutions. In a recent study, AI and machine learning were named as the top data priorities for 2021, by 61% […].

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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 21, 2022

A lack of awareness has led to less competition in the data science field. In this article, we present some interesting statistics around the following, that should help you decide for yourself as to where you are headed for with job in data science : Big Data, Data Science, and Analytics Market; Data Scientist — Job and Salary.

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The Data Administration Newsletter

OCTOBER 17, 2023

Where exactly within an organization does the primary responsibility lie for ensuring that a data pipeline project generates data of high quality, and who exactly holds that responsibility? Who is accountable for ensuring that the data is accurate? Is it the data engineers? The data scientists?

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Dataversity

SEPTEMBER 25, 2023

In an era where large language models (LLMs) are redefining AI digital interactions, the criticality of accurate, high-quality, and pertinent data labeling emerges as paramount. That means data labelers and the vendors overseeing them must seamlessly blend data quality with human expertise and ethical work practices.

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Analysts Corner

FEBRUARY 5, 2023

Forrester reports suggest that between 60% and 73% of the total data is never used for analytics. An IBM study states 80% of data scientists utilize their time finding, organizing, and cleansing data (that is improving data quality), and only 20% on data analysis. Food for thought and the way ahead!

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