Data Science Dojo

OCTOBER 10, 2023

The event is for anyone interested in learning about generative AI and data storytelling, including business leaders, data scientists, and enthusiasts. Data storytelling is the process of using data to communicate a story in a way that is engaging and informative.

AI 243

AI AI Data Observability Artificial Intelligence 243

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?

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Dataversity

APRIL 25, 2022

If data processes are not at peak performance and efficiency, businesses are just collecting massive stores of data for no reason. Data without insight is useless, and the energy spent collecting it, is wasted. The post Solving Three Data Problems with Data Observability appeared first on DATAVERSITY.

Data Observability 52

Data Observability Data Silos Data Quality Data Governance 52

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.

Data Quality 72

Data Quality Data Observability Data Governance 72

ODSC - Open Data Science

FEBRUARY 28, 2023

Making Data Observable Bigeye The quality of the data powering your machine learning algorithms should not be a mystery. Bigeye’s data observability platform helps data science teams “measure, improve, and communicate data quality at any scale.”

Data Science 96

Data Science Machine Learning Machine Learning AI 96

The MLOps Blog

JUNE 27, 2023

To provide you with a comprehensive overview, this article explores the key players in the MLOps and FMOps (or LLMOps) ecosystems, encompassing both open-source and closed-source tools, with a focus on highlighting their key features and contributions. Data monitoring tools help monitor the quality of the data.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

FEBRUARY 14, 2023

Beyond Monitoring: The Rise of Data Observability Shane Murray Field | CTO | Monte Carlo This session addresses the problem of “data downtime” — periods of time when data is partial, erroneous, missing or otherwise inaccurate — and how to eliminate it in your data ecosystem with end-to-end data observability.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Precisely

MARCH 7, 2024

Yet experts warn that without proactive attention to data quality and data governance, AI projects could face considerable roadblocks. This article focuses on some fundamental prerequisites for the successful use of generative AI in the claims management process.

AI 72

AI AI Data Governance Data Quality 72

Dataversity

MAY 26, 2023

Suppose you’re in charge of maintaining a large set of data pipelines from cloud storage or streaming data into a data warehouse. How can you ensure that your data meets expectations after every transformation? That’s where data quality testing comes in.

Data Pipeline 52

Data Pipeline Data Warehouse Data Quality Data Observability 52

IBM Journey to AI blog

AUGUST 23, 2023

Making LLMs useful as enterprise software requires governing the training data so that companies can trust the safety of the data and have an audit trail for the LLM’s consumption of the data. Data governance for LLMs The best breakdown of LLM architecture I’ve seen comes from this article by a16z (image below).

Data Governance 66

Data Governance AI AI Artificial Intelligence 66

Dataversity

JUNE 19, 2023

In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline.

Data Pipeline 52

Data Pipeline Database Data Models Data Modeling 52

Precisely

FEBRUARY 2, 2023

Accuracy: Data That Can Be Used With Confidence In tenuous times the environment is much less forgiving, making the margin for error very small. This makes an executive’s confidence in the data paramount. Accurate, consistent contextual data is key to managers’ ability to lead an agile organization.

Data Pipeline 52

Data Pipeline Data Silos Database Data Observability 52

Dataversity

JUNE 26, 2023

The same expectation applies to data, […] The post Leveraging Data Pipelines to Meet the Needs of the Business: Why the Speed of Data Matters appeared first on DATAVERSITY. Today, businesses and individuals expect instant access to information and swift delivery of services.

Data Pipeline 52

Data Pipeline Data Observability Data Quality Data Governance 52

Dataversity

DECEMBER 14, 2021

Watching closely the evolution of metadata platforms (later rechristened as Data Governance platforms due to their focus), as somebody who has implemented and built Data Governance solutions on top of these platforms, I see a significant evolution in their architecture as well as the use cases they support.

Data Governance 52

Data Governance Data Observability ETL Data Quality 52

The MLOps Blog

AUGUST 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Piotr: Sounds like something with data, right?

ML 52

ML ML Data Scientist Machine Learning 52

Dataversity

MARCH 26, 2025

Unreliable or outdated data can have huge negative consequences for even the best-laid plans, especially if youre not aware there were issues with the data in the first place. Thats why data observability […] The post Implementing Data Observability to Proactively Address Data Quality Issues appeared first on DATAVERSITY.

Data Observability 69

Data Observability Data Quality Data Governance 69

Dataversity

JANUARY 16, 2025

Test cases, data, and validation procedures are crucial for data transformations, requiring an understanding of transformation requirements, scenarios, and specific techniques for accuracy and integrity.

Data Observability 52

Data Observability Data Quality Data Governance Analytics 52