AltexSoft

AUGUST 3, 2021

Explaining Data Annotation for ML. Data annotation follows a meticulous process of adding metadata to a dataset. This metadata is always in the form of tags, which then can be added to various data types like text, images, and video. Guaranteeing high-quality data with consistency.

Datasets 59

Datasets High Quality Data Metadata Raw Data 59

Data Engineering Weekly

SEPTEMBER 15, 2024

Whether or not Data Mesh is a separate product is debatable, but it is certainly an impactful framework for scaling data platforms. link] Miro: Data Products Reliability - The Power of Metadata Miro writes about its adoption of Data Contracts. But can it predict presidential elections?

Data Engineering 67

Data Engineering Data Engineer Engineering Data Lake 67

Data Engineering Weekly

AUGUST 25, 2024

[link] Sponsored: Guide to Data Orchestration for Generative AI When it comes to success with Generative AI, it’s all about the data — specifically, your data. However, it’s only by combining these with rich proprietary datasets and operational data streams that organizations can find true differentiation.

Data Engineering 70

Data Engineering Data Engineer Engineering Database-centric 70

Databand.ai

AUGUST 30, 2023

This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. Additionally, high-quality data reduces costly errors stemming from inaccurate information.

Building 52

Building Data Cleanse Data Governance Datasets 52

Monte Carlo

JANUARY 23, 2024

This means that data scientists and engineers will be able to spend less time on painstakingly preparing datasets for analytics or writing complex code. Within the next year or so, John says, most of the practical use cases for gen AI in data will be centered around productivity. Those who don’t embrace it will be left behind.

Database-centric 52

Database-centric Pipeline-centric Metadata Unstructured Data 52

Ascend.io

FEBRUARY 28, 2024

While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data. Here lies the critical role of data engineering: preparing and managing data to feed AI models.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Monte Carlo

JANUARY 16, 2024

Table of Contents Solve data silos starting at the people-level Keep data governance approachable Oliver Gomes’ data governance best practices Manage and promote the value of high-quality data How will Generative AI impact data quality at Fox? But visibility isn’t just for the data team.

Government 52

Government High Quality Data Data Governance Entertainment 52

Monte Carlo

AUGUST 30, 2024

With Monte Carlo, data teams can easily discover virtualized datasets within the platform and create SQL Rules to monitor every data source to which Dremio is connected. Now data teams can leverage this lightning fast, cost optimized query engine while being assured they are delivering high quality data.

SQL 40

SQL Engineering Data Lake High Quality Data 40

Monte Carlo

OCTOBER 5, 2023

Ensure data quality Even if there are no errors during the ETL process, you still have to make sure the data meets quality standards. High-quality data is crucial for accurate analysis and informed decision-making. increased vigilance in maintaining thorough documentation and metadata.

Data Cleanse 52

Data Cleanse ETL Tools Datasets High Quality Data 52

Monte Carlo

FEBRUARY 8, 2024

Data observability tools should offer both broad automated metadata monitoring across all the tables once they have been added to your selected schemas, as well as deep monitoring for issues inherent in the data itself. AI ready Building differentiated, useful generative AI applications requires first party data.

BI 59

BI Data Pipeline Data Warehouse Data 59

Monte Carlo

JULY 11, 2023

When crucial information is omitted or unavailable, the analysis or conclusions drawn from the data may be flawed or misleading. Inconsistent data: Inconsistencies within a dataset can indicate inaccuracies. This can include contradictory information or data points that do not align with established patterns or trends.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Monte Carlo

AUGUST 30, 2023

A passing test means you’ve improved the trustworthiness of your data. Schedule and automate You’ll need to run schema tests continuously to keep up with your ever-changing data. If your datasets are updated or refreshed daily, you’ll want to run your schema tests on a similar schedule.

SQL 52

SQL Datasets Database High Quality Data 52

Ascend.io

JUNE 8, 2023

Although point solutions for data lineage exist, fully integrated data pipeline lineage capabilities make superior use of operational metadata and produce better quality and more timely reports. Data catalogs help them find and locate the best candidates, and solidify their requests for data products that don’t yet exist.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Scribd Technology

JULY 27, 2021

Scribd offers a variety of publisher and user-uploaded content to our users and while the publisher content is rich in metadata, user-uploaded content typically is not. Additional insights from data In the first iteration of the model, we had a dataset for training collected by our experts to fit the definition of each category.

High Quality Data 52

High Quality Data Metadata Algorithm Datasets 52

Monte Carlo

JUNE 20, 2023

So, just to be safe you take the extra time to recreate the dataset. Data products and trustworthiness According to Zhamak Dehgahi, data products should be discoverable, addressable, trustworthy, self-describing, interoperable and secure. In our experience, most data products only support one or two use cases.

Building 52

Building Data Datasets High Quality Data 52

Precisely

JANUARY 16, 2024

To remain competitive, you must proactively and systematically pursue new ways to leverage data to your advantage. As the value of data reaches new highs, the fundamental rules that govern data-driven decision-making haven’t changed. To make good decisions, you need high-quality data.

Data Governance 69

Data Governance Government Metadata Data 69

Monte Carlo

MAY 17, 2023

Move Your Generative AI Strategy From Pitch Deck To Reality If generative AI is a gold rush, high quality data is the pickaxe. As the leaked Google memo says, “data quality scales better than data size” for these types of projects which can save time by training on “small, highly curated datasets.”

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Move Your Generative AI Strategy From Pitch Deck To Reality If generative AI is a goldrush, high quality data is the pickaxe. As the leaked Google memo says, “data quality scales better than data size” for these types of projects which can save time by training on “small, highly curated datasets.”

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Data 52

Monte Carlo

JUNE 23, 2022

Technologies like data observability can bring data monitoring to scale so there doesn’t need to be a trade off between quantity and big data quality, but the point remains data volume alone is insufficient to make a fraction of the impact of a well-maintained, high quality data product.

Big Data 40

Big Data Management Machine Learning Data Warehouse 40