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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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.

Data Governance 103

Data Governance Data Quality Artificial Intelligence Artificial Intelligence 103

Dataconomy

APRIL 21, 2023

Data fidelity refers to the accuracy, completeness, consistency, and timeliness of data. In other words, it’s the degree to which data can be trusted to be accurate and reliable. Definition and explanation Accuracy refers to how close the data is to the true or actual value.

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Data Quality Data Governance Data Visualization Power BI 182

Dataconomy

APRIL 29, 2025

Understanding the concept of skew The skew between training and serving datasets can be characterized by several factors, primarily focusing on the differences in distribution and data properties. When training data does not accurately represent the data routine found in deployment, models may struggle to generalize.

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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

Precisely

APRIL 24, 2025

Key Takeaways: Lack of shared data definitions, ownership, and built-in compliance creates risk and inefficiencies across your organization. Business-friendly governance and stewardship frameworks empower teams to trust, manage, and use data with confidence. If people dont trust the data, they wont use it. Feel familiar?

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Data Governance Data Quality Analytics Analytics 64

Dataconomy

JUNE 3, 2025

Understanding data corruption Data corruption poses a significant threat to data integrity. It refers to any unintended modifications or loss of data, leading to inaccuracies and potential loss of trust. Such corruption can render data unusable or lead to inaccurate conclusions from data analysis.

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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 Lakes Data Governance 119

Dataversity

JULY 17, 2024

It helps maintain consistency across disparate systems, enhancing data reliability and improving decision-making. So, to get started with […] The post Data Synchronization: Definition, Tips, Myths, and Best Practices appeared first on DATAVERSITY.

Data Governance 59

Data Governance Data Quality 59

Dataconomy

APRIL 21, 2023

Data fidelity refers to the accuracy, completeness, consistency, and timeliness of data. In other words, it’s the degree to which data can be trusted to be accurate and reliable. Definition and explanation Accuracy refers to how close the data is to the true or actual value.

Data Quality 149

Data Quality Data Governance Data Visualization Power BI 149

Precisely

JULY 27, 2023

For the report, more than 450 data and analytics professionals worldwide were surveyed about the state of their data programs. Low data quality is a pervasive theme across the survey results, reducing trust in data used for decision-making and challenging organizations’ ability to achieve success in their data programs.

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Data Quality Analytics Analytics Data Governance 52

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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Dataconomy

FEBRUARY 11, 2025

Definition and importance AI readiness refers to the comprehensive preparation an organization undertakes to effectively integrate and implement artificial intelligence systems. These tools analyze factors such as data availability, system compatibility, and workforce readiness, providing actionable insights to guide AI adoption strategies.

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AI AI Artificial Intelligence Artificial Intelligence 121

Precisely

NOVEMBER 10, 2023

To measure data quality – and track the effectiveness of data quality improvement efforts – you need, well, data. Keep reading for a look at the types of data and metrics that organizations can use to measure data Businesses today are increasingly dependent on an ever-growing flood of information.

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Dataconomy

MARCH 25, 2025

Understanding baseline models The definition of baseline models emphasizes their purpose in machine learning: they define a minimum level of performance that any model must achieve to be considered useful. This concept is particularly relevant in predictive modeling, where accurately predicting outcomes is vital.

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Decision Trees Machine Learning Machine Learning Data Scientist 91

Alation

SEPTEMBER 28, 2022

In essence, DataOps is a practice that helps organizations manage and govern data more effectively. However, there is a lot more to know about DataOps, as it has its own definition, principles, benefits, and applications in real-life companies today – which we will cover in this article! Automated testing to ensure data quality.

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DataOps Data Pipeline Data Quality Analytics 52

Tableau

OCTOBER 7, 2024

Tableau is a leader in the analytics market, known for helping organizations see and understand their data, but we recognize that gaps still exist: while many of our joint customers already benefit from dbt and trust the metrics that result from these workflows, they are often disconnected and obscured from Tableau’s analytics layer.

Tableau 138

Tableau Data Quality Analytics Analytics 138

Smart Data Collective

APRIL 5, 2022

Drones Surveyors Are Pioneers in the Data Analytics Field. While this is definitely true, there are a few best practices that users need to learn to use it properly. Drone surveyors must also know how to gather and use data properly. They will need to be aware of the potential that data can bring to entities using drones.

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Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

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Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

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Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

Data Quality 52

Data Quality ML ML AI 52

The MLOps Blog

MARCH 21, 2023

Olalekan said that most of the random people they talked to initially wanted a platform to handle data quality better, but after the survey, he found out that this was the fifth most crucial need. Machine Learning Operations (MLOps): Overview, Definition, and Architecture (by Kreuzberger, et al., AIIA MLOps blueprints.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

Dataconomy

MARCH 20, 2025

Definition and functionality of LLM app platforms These platforms encompass various capabilities specifically tailored for LLM development. Data collection and preparation Quality data is paramount in training an effective LLM. Subpar data can lead to inaccurate outputs and diminished application effectiveness.

Data Preparation 91

Data Preparation Data Pipeline Data Quality Database 91

AWS Machine Learning Blog

FEBRUARY 27, 2024

The SageMaker project template includes seed code corresponding to each step of the build and deploy pipelines (we discuss these steps in more detail later in this post) as well as the pipeline definition—the recipe for how the steps should be run. Workflow B corresponds to model quality drift checks.

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Data Science Dojo

JULY 30, 2024

Why Should you Adopt Data Driven Marketing Companies should focus on data-driven marketing for several key reasons, all of which contribute to more effective and efficient marketing strategies. Implementation: Use website analytics, social media data, and customer data to gain comprehensive insights.

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Dataconomy

MARCH 10, 2025

Accurate ground truth data ensures that a model learns from examples that reflect real-world scenarios, allowing it to generalize better when making predictions in unfamiliar situations. Impact of data quality and quantity The quality and quantity of data significantly affect an algorithm’s efficiency.

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JANUARY 17, 2023

As they do so, access to traditional and modern data sources is required. Poor data quality and information silos tend to emerge as early challenges. Customer data quality, for example, tends to erode very quickly as consumers experience various life changes.

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Data Quality Machine Learning Machine Learning Data Silos 97

The Data Administration Newsletter

SEPTEMBER 7, 2022

Fit for Purpose data has been a foundational concept of Data Governance for as long as I’ve been in the field…so that’s 10-15 years now. Most data quality definitions take Fit-for-Purpose as a given.

Data Governance 59

Data Governance Data Quality 59

AWS Machine Learning Blog

NOVEMBER 1, 2024

We discuss the important components of fine-tuning, including use case definition, data preparation, model customization, and performance evaluation. This post dives deep into key aspects such as hyperparameter optimization, data cleaning techniques, and the effectiveness of fine-tuning compared to base models.

Data Preparation 101

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Towards AI

MARCH 10, 2025

We began by preprocessing the images to enhance data quality. There are different types of connectivity, primarily 4-connectivity and 8-connectivity: 4-Connectivity: Definition: In 4-connectivity, a pixel (of interest) is considered connected to another pixel if they share an edge.

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Python Algorithm Data Quality Data Analysis 96

Dataconomy

FEBRUARY 11, 2025

As an organized approach, it offers definition, ranking, and guidance to ensure the best outcome for AI projects. A comprehensive assessment also highlights gaps in your current capabilities, such as insufficient data quality or outdated systems, which need to be addressed before proceeding with implementation.

AI 113

AI AI Predictive Analytics Data Scientist 113

The Data Administration Newsletter

JANUARY 3, 2024

Edwards Deming, the father of statistical quality control, said: “If you can’t describe what you are doing as a process, you don’t know what you’re doing.” When looking at the world of IT and applied to the dichotomy of software and data, Deming’s quote applies to the software part of that pair.

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Data Quality Data Governance 52

Dataconomy

MARCH 27, 2023

Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. Data quality and quantity:  Machine learning algorithms require high-quality, labeled data to be effective, and their accuracy may be limited by the amount of data available.

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ML ML Machine Learning Machine Learning 133

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SEPTEMBER 18, 2024

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, it’s second only to data quality. You’re not alone.

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Data Quality Data Governance Analytics Analytics 72

Smart Data Collective

SEPTEMBER 28, 2022

Big data management increases the reliability of your data. Big data management has many benefits. One of the most important is that it helps to increase the reliability of your data. Data quality issues can arise from a variety of sources, including: Duplicate records Missing records Incorrect data.

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Big Data Big Data Big Data Analytics Big Data Analytics 126

IBM Journey to AI blog

JANUARY 23, 2023

Simply put, data governance is the process of establishing policies, procedures, and standards for managing data within an organization. It involves defining roles and responsibilities, setting standards for data quality, and ensuring that data is being used in a way that is consistent with the organization’s goals and values.

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Tableau

APRIL 18, 2022

We’ve infused our values into our platform, which supports data fabric designs with a data management layer right inside our platform, helping you break down silos and streamline support for the entire data and analytics life cycle. . Analytics data catalog. Data quality and lineage. Metadata management.

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Tableau Data Quality Analytics Analytics 102