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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Data Quality Data Governance Database Analytics 105

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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Data Quality AI AI Data Governance 114

Smart Data Collective

SEPTEMBER 29, 2022

Every company deals with a certain number of documents on a daily basis: invoices, receipts, logistics, or HR documents… You have to keep these documents, extract the useful information for your business, and then integrate them manually into your database. The software extracts all the information in plain text in a TXT format.

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Data Quality Big Data Big Data Artificial Intelligence 145

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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Data Quality Data Governance 119

Dataversity

APRIL 3, 2025

This is the first in a two-part series exploring Data Quality and the ISO 25000 standard. Despite efforts to recall the bombers, one plane successfully drops a […] The post Mind the Gap: Did You Know About the ISO 25000 Series Data Quality Standards? Ripper orders a nuclear strike on the USSR.

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Data Quality Data Governance 52

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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Data Quality Data Silos Big Data Big Data 121

Flipboard

MARCH 13, 2023

Presented by BMC Poor data quality costs organizations an average $12.9 Organizations are beginning to recognize that not only does it have a direct impact on revenue over the long term, but poor data quality also increases the complexity of data ecosystems, and directly impacts the … million a year.

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Data Quality Big Data Big Data Analytics 123

Dataconomy

NOVEMBER 12, 2024

“Some researchers at the company believe Orion isn’t reliably better than its predecessor in handling certain tasks,” reported The Information. Researchers have found that relying heavily on synthetic data can cause models to degrade over time.

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Data Quality AI AI Artificial Intelligence 194

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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Data Quality Data Preparation Algorithm Data Silos 104

Data Science Dojo

JANUARY 25, 2024

The data points in the three-dimensional space can capture the semantic relationships and contextual information associated with them. With the advent of generative AI, the complexity of data makes vector embeddings a crucial aspect of modern-day processing and handling of information.

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AI AI Algorithm Natural Language Processing 412

Flipboard

JUNE 15, 2023

Data Engineers: We look into Data Engineering, which combines three core practices around Data Management, Software Engineering, and I&O. This focuses …

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Data Quality Data Engineering Data Engineering Data Engineering 147

Dataconomy

APRIL 17, 2025

Enhancing model accuracy and decision making Effectively using PSI not only refines predictive accuracy but also informs strategic business decisions. Data quality assurance PSI acts as a validation measure for data quality, particularly beneficial in environments reliant on automated data collection processes.

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Data Quality Analytics Analytics 91

Dataconomy

APRIL 21, 2023

Data fidelity, the degree to which data can be trusted to be accurate and reliable, is a critical factor in the success of any data-driven business. Companies are collecting and analyzing vast amounts of data to gain insights into customer behavior, identify trends, and make informed decisions.

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Data Quality Data Governance Data Visualization Power BI 182

Dataconomy

MARCH 17, 2025

Augmented analytics is revolutionizing how organizations interact with their data. By harnessing the power of machine learning (ML) and natural language processing (NLP), businesses can streamline their data analysis processes and make more informed decisions. This leads to better business planning and resource allocation.

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Precisely

JANUARY 9, 2025

Each source system had their own proprietary rules and standards around data capture and maintenance, so when trying to bring different versions of similar data together such as customer, address, product, or financial data, for example there was no clear way to reconcile these discrepancies. A data lake!

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Data Warehouse Hadoop Data Governance Data Lakes 119

Dataconomy

NOVEMBER 11, 2024

This approach is ideal for use cases requiring accuracy and up-to-date information, like providing technical product documentation or customer support. For instance, prompts like “Provide a detailed but informal explanation” can shape the output significantly without requiring the model itself to be fine-tuned.

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Data Preparation Database Data Quality Machine Learning 203

Precisely

JANUARY 30, 2025

Recognize that artificial intelligence is a data governance accelerator and a process that must be governed to monitor ethical considerations and risk. Integrate data governance and data quality practices to create a seamless user experience and build trust in your data.

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Data Science Dojo

MARCH 18, 2024

Yet, despite these impressive capabilities, their limitations became more apparent when tasked with providing up-to-date information on global events or expert knowledge in specialized fields. Revisit the best large language models of 2023 Enter RAG and finetuning RAG revolutionizes the way language models access and use information.

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Database Natural Language Processing AI AI 367

Data Science Dojo

SEPTEMBER 15, 2023

While there’s no denying that large language models can generate false information, we can take action to reduce the risk. Large Language Models (LLMs), such as OpenAI’s ChatGPT, often face a challenge: the possibility of producing inaccurate information. AI hallucinations: When language models dream in algorithms.

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AI AI Data Quality Algorithm 359

Dataconomy

MARCH 29, 2025

You need to provide the user with information within a short time frame without compromising the user experience. He cited delivery time prediction as an example, where each user’s data is unique and depends on numerous factors, precluding pre-caching. Data management is another critical area.

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Data Warehouse AI AI Data Preparation 103

Dataconomy

APRIL 21, 2023

Data fidelity, the degree to which data can be trusted to be accurate and reliable, is a critical factor in the success of any data-driven business. Companies are collecting and analyzing vast amounts of data to gain insights into customer behavior, identify trends, and make informed decisions.

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Data Quality Data Governance Data Visualization Power BI 149

Flipboard

NOVEMBER 22, 2024

It serves as the hub for defining and enforcing data governance policies, data cataloging, data lineage tracking, and managing data access controls across the organization. Data lake account (producer) – There can be one or more data lake accounts within the organization.

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Data Governance ML ML Data Lakes 131

Dataversity

JANUARY 31, 2025

Data Sips is a new video miniseries presented by Ippon Technologies and DATAVERSITY that showcases quick conversations with industry experts from last months Data Governance & Information Quality (DGIQ) Conference in Washington, D.C.

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Data Governance Data Quality 52

IBM Data Science in Practice

APRIL 26, 2024

Metadata Enrichment: Empowering Data Governance Data Quality Tab from Metadata Enrichment Metadata enrichment is a crucial aspect of data governance, enabling organizations to enhance the quality and context of their data assets. This dataset spans a wide range of ages, from teenagers to senior citizens.

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SQL Data Quality Data Governance Data Scientist 130

Machine Learning Research at Apple

FEBRUARY 19, 2025

Foundation models are trained on large-scale web-crawled datasets, which often contain noise, biases, and irrelevant information. This motivates the use of data selection techniques, which can be divided into model-free variants -- relying on heuristic rules and downstream datasets -- and model-based, e.g., using influence functions.

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Data Quality 130

Precisely

JANUARY 27, 2025

That number jumps to 60% when asked specifically about obstacles to AI readiness, making it clear that the scarcity of skilled professionals makes it difficult for organizations to fully capitalize on their data assets and implement effective AI solutions. In fact, its second only to data quality. Youre not alone.

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Data Quality Data Governance Analytics Analytics 59

Flipboard

DECEMBER 27, 2023

Presented by SQream The challenges of AI compound as it hurtles forward: demands of data preparation, large data sets and data quality, the time sink of long-running queries, batch processes and more. In this VB Spotlight, William Benton, principal product architect at NVIDIA, and others explain how …

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Data Preparation Analytics Analytics Data Quality 181

Precisely

NOVEMBER 18, 2024

Key Takeaways: Data integrity is required for AI initiatives, better decision-making, and more – but data trust is on the decline. Data quality and data governance are the top data integrity challenges, and priorities. Plan for data quality and governance of AI models from day one.

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Analytics Analytics AI AI 72

Smart Data Collective

AUGUST 11, 2022

How Artificial Intelligence is Impacting Data Quality. Artificial intelligence has the potential to combat human error by taking up the tasking responsibilities associated with the analysis, drilling, and dissection of large volumes of data. Data quality is crucial in the age of artificial intelligence.

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Data Quality Artificial Intelligence Artificial Intelligence Machine Learning 139

Dataconomy

DECEMBER 17, 2024

Businesses project planning is key to success and now they are increasingly rely on data projects to make informed decisions, enhance operations, and achieve strategic goals. However, the success of any data project hinges on a critical, often overlooked phase: gathering requirements. What are the data quality expectations?

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Data Quality Power BI Data Engineering Data Engineering 91

Precisely

JANUARY 15, 2024

When you delve into the intricacies of data quality, however, these two important pieces of the puzzle are distinctly different. Knowing the distinction can help you to better understand the bigger picture of data quality. What Is Data Validation? For a list of addresses that includes countries outside the U.S.,

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Data Quality Database Artificial Intelligence Artificial Intelligence 114

Dataconomy

MARCH 21, 2025

Steps to create a golden dataset Developing a golden dataset involves a careful and structured approach to ensure its quality and effectiveness. Data collection The first step is gathering information from trustworthy and diverse sources to build a robust dataset.

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ML ML Algorithm Artificial Intelligence 91

Dataconomy

MARCH 6, 2025

Links Links illustrate the connections between different hubs, providing context for how various data elements interact with one another. Satellites Satellites contain the descriptive information related to the data stored in hubs. Traceability One of the standout features of data vault is its strong focus on traceability.

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Data Warehouse Data Quality Data Models Data Modeling 91

Data Science Dojo

JULY 30, 2024

The relentless tide of data preserve—customer behavior, market trends, and hidden insights—all waiting to be harnessed. They ignore the call of data analytics, forsaking efficiency, ROI, and informed decisions. Meanwhile, their rivals ride the data-driven wave, steering toward success.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

For example, you can use Amazon Bedrock Guardrails to filter out harmful user inputs and toxic model outputs, redact by either blocking or masking sensitive information from user inputs and model outputs, or help prevent your application from responding to unsafe or undesired topics.

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Dataversity

APRIL 4, 2025

These takeaways include my overall professional impressions and a high-level review of the most prominenttopics discussed in the conferences core subject areas: data governance, data quality, and AI governance.

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Data Governance Data Quality AI AI 52

Dataconomy

MARCH 11, 2025

Change detection Identifying changes in datasets is vital for maintaining data quality. Multiple snapshots provide clarity in discrepancies, facilitating easier debugging and understanding of data evolution. Organizations must assess their specific needs to make informed choices.

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Machine Learning Machine Learning Data Scientist Data Quality 91