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. Automated profiling tools can quickly detect anomalies or patterns indicating potential dataset integrity issues.

Building 52

Building Data Cleanse Data Governance Datasets 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

Precisely

SEPTEMBER 25, 2023

When it comes to third-party data, you just need to find the best quality data and sources that deliver the results you need – whether you’re using that information for business intelligence dashboards, problem-solving, analytics, or AI/ML applications. Streamline the Process with Precisely Let’s talk about address data.

Data Validation 100

Data Validation Process Raw Data Data Cleanse 100

Databand.ai

JUNE 14, 2023

Ryan Yackel June 14, 2023 Understanding Data Quality Testing Data quality testing refers to the evaluation and validation of a dataset’s accuracy, consistency, completeness, and reliability. Risk mitigation: Data errors can result in expensive mistakes or even legal issues.

Amazon Web Services 52

Amazon Web Services Datasets High Quality Data ETL Tools 52

Monte Carlo

JANUARY 10, 2024

On the other hand, “Can the marketing team easily segment the customer data for targeted communications?” usability) would be about extrinsic data quality. Use of Data Quality Tools Refresh your intrinsic data quality with data observability 1.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Monte Carlo

JANUARY 3, 2024

Chances are if you had a perfect RAG pipeline, fine tuned model, and clear use case ready to go tomorrow ( and wouldn’t that be nice? ), you still wouldn’t have clean, well-modeled datasets to plug it all into. away from your data infrastructure being GenAI ready. Your data engineering team is the backbone for ensuring data health.

Technology 113

Technology Database Data Governance Data Engineering 113

Monte Carlo

JANUARY 5, 2024

With object storage decoupled from compute, organizations can use their preferred tool or APIs to read objects directly from the storage layer using open file formats, like Parquet, and metadata, where the schemas of structured and unstructured datasets are kept.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

With object storage decoupled from compute, organizations can use their preferred tool or APIs to read objects directly from the storage layer using open file formats, like Parquet, and metadata, where the schemas of structured and unstructured datasets are kept.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Edureka

SEPTEMBER 11, 2023

Generative algorithms go beyond the capabilities of discriminative models, which are best at identifying and classifying elements within a given data set, such as determining whether an email is spam. The generator produces high-quality data because the two networks are trained together in a game-like setting.

Algorithm 52

Algorithm Entertainment Healthcare Deep Learning 52

The Modern Data Company

JULY 18, 2023

It should address specific data challenges, such as improving operational efficiency, enhancing customer experience, or driving data-driven decision-making. Data Quality and Reliability Ensuring data quality is crucial for any data product.

Data Governance 69

Data Governance High Quality Data Government Data 69

Monte Carlo

JANUARY 29, 2024

Improve coverage with automated anomaly detection Monte Carlo uses machine learning detectors to monitor the health of data pipelines across dimensions like: Data freshness : Did the data arrive when we expected? Schema: Did the organization of the dataset change in a way that will break other data operations downstream?

Metadata 64

Metadata High Quality Data Data Pipeline Machine Learning 64

Precisely

FEBRUARY 23, 2024

Their ability to generate business value is directly related to the quality of their data, however. Unless they have high-quality data, business users simply cannot deliver optimal results. Forests of so-called “red and dead” standing timber present a much higher risk of wildfire than healthy woodlands.

Insurance 64

Insurance Data Validation Government High Quality Data 64

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.

Data Cleanse 52

Data Cleanse ETL Tools Datasets High Quality Data 52

Knowledge Hut

JANUARY 25, 2024

What is Data Cleaning? Data cleaning, also known as data cleansing, is the essential process of identifying and rectifying errors, inaccuracies, inconsistencies, and imperfections in a dataset. It involves removing or correcting incorrect, corrupted, improperly formatted, duplicate, or incomplete data.

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

Precisely

MARCH 14, 2023

New technologies are making it easier for customers to process increasingly large datasets more rapidly. If you happen to be a user of these products, you already know about the results that high-quality data produces: more and happier customers, lower costs and higher efficiency, and compliance with complex regulations – to name just a few.

Data Integration 52

Data Integration High Quality Data BI Data 52

Data Engineering Weekly

MARCH 3, 2024

Here is the agenda, 1) Data Application Lifecycle Management - Harish Kumar( Paypal) Hear from the team in PayPal on how they build the data product lifecycle management (DPLM) systems. 3) DataOPS at AstraZeneca The AstraZeneca team talks about data ops best practices internally established and what worked and what didn’t work!!!

Data Engineering 67

Data Engineering Data Engineer Pipeline-centric Engineering 67

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

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.

Pipeline-centric 52

Pipeline-centric Database-centric Metadata Unstructured Data 52

The Modern Data Company

JULY 18, 2023

It should address specific data challenges, such as improving operational efficiency, enhancing customer experience, or driving data-driven decision-making. Data Quality and Reliability Ensuring data quality is crucial for any data product.

Data Governance 52

Data Governance High Quality Data Government Data 52

The Modern Data Company

JULY 18, 2023

It should address specific data challenges, such as improving operational efficiency, enhancing customer experience, or driving data-driven decision-making. Data Quality and Reliability Ensuring data quality is crucial for any data product.

Data Governance 52

Data Governance High Quality Data Government Data 52

Knowledge Hut

JANUARY 24, 2024

This blog offers an exclusive glimpse into the daily rituals, challenges, and moments of triumph that punctuate the professional journey of a data scientist. The primary objective of a data scientist is to analyze complex datasets to uncover patterns, trends, and valuable information that can aid in informed decision-making.

Database-centric 52

Database-centric Data Science Machine Learning Datasets 52

Cloudera

MAY 18, 2022

Those algorithms require high quality data to deliver meaningful results. Data, whether structured, unstructured, or partly structured, comes in from various sources and needs to be sorted and analyzed with a data management platform.

Manufacturing 91

Manufacturing Retail High Quality Data Algorithm 91

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

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

NOVEMBER 8, 2023

Expanding end-to-end coverage across batch, streaming, and RAG pipelines enables organizations to realize the full potential of their AI initiatives with trusted, high-quality data. Data Product Dashboard – Speed is critical to maintaining reliable data products.

Kafka 64

Kafka Database High Quality Data Datasets 64

Databand.ai

AUGUST 30, 2023

Data Accuracy vs Data Integrity: Key Similarities Contribution to Data Quality Data accuracy and data integrity are both essential components of data quality. As mentioned earlier, data quality encompasses a range of attributes, including accuracy, consistency, completeness, and timeliness.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

AltexSoft

AUGUST 25, 2021

You can’t simply feed the system your whole dataset of emails and expect it to understand what you want from it. Now, when we understand the methodologies and principles behind building NLP models, let’s tackle the main component of all ML projects — a dataset. Preparing an NLP dataset. But what makes data great?

Process 139

Process Deep Learning Datasets Machine Learning 139

Monte Carlo

OCTOBER 11, 2022

We’ll then discuss how they can be avoided with an organizational commitment to high-quality data. Imagine this You’re a data scientist with a swagger working on a predictive model to optimize a fast-growing company’s digital marketing spend. Model design You see the LinkedIn ad click data has.1%

IT 52

IT Datasets Data Warehouse Data Analysis 52

Databand.ai

JULY 5, 2023

Improved Collaboration Among Teams Data engineering teams frequently collaborate with other departments, such as analysts or scientists, who depend on accurate datasets for their tasks. Boosting Operational Efficiency A well-monitored data pipeline can significantly increase an organization’s operational efficiency.

Data Pipeline 52

Data Pipeline Data Lake Data Warehouse Datasets 52

Knowledge Hut

JUNE 28, 2023

By examining these factors, organizations can make informed decisions on which approach best suits their data analysis and decision-making needs. Parameter Data Mining Business Intelligence (BI) Definition The process of uncovering patterns, relationships, and insights from extensive datasets.

Data Mining 52

Data Mining Business Intelligence BI Datasets 52

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

Picnic Engineering

DECEMBER 12, 2023

The rich context provided by our Snowflake-powered Data Warehouse enhances their performance, allowing us to create a robust feature set for training. Training these models on our historical demand and assessing their performance is manageable as a one-time project since concepts like data drift are still not a big concern.

Machine Learning 52

Machine Learning Deep Learning Food High Quality Data 52

Monte Carlo

DECEMBER 19, 2022

Here are the 7 must-have checks to improve data quality and ensure reliability for your most critical assets. Data quality testing is the process of validating that key characteristics of a dataset match what is anticipated prior to its consumption. million per year.

High Quality Data 52

High Quality Data Bytes Data SQL 52

Lyft Engineering

OCTOBER 3, 2023

High-quality data is necessary for the success of every data-driven company. It is now the norm for tech companies to have a well-developed data platform. This makes it easy for engineers to generate, transform, store, and analyze data at the petabyte scale.

Big Data 84

Big Data Metadata Data Warehouse Data 84

Precisely

MARCH 23, 2023

The stakes are high and there isn’t a tolerance for error. Read Being transparent and having this type of information readily available is what builds confidence and trust in your brand and makes reporting and compliance processes more streamlined.

Data Integration 52

Data Integration Government High Quality Data Data 52

Monte Carlo

FEBRUARY 9, 2024

Monte Carlo’s recently released feature, Performance , allows users to easily filter queries related to specific DAGs, users, dbt models, warehouses, or datasets. Users can then drill down to spot anomalies and determine how query performance was impacted by changes in code, data, and warehouse configuration.

Data 64

Data High Quality Data Architecture Coding 64

Databand.ai

JULY 17, 2023

By adopting a set of best practices inspired by Agile methodologies, DevOps principles, and statistical process control techniques, DataOps helps organizations deliver high-quality data insights more efficiently. In some cases, organizations may benefit from adopting elements from both methodologies.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52