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 97

Manufacturing High Quality Data Retail Algorithm 97

Monte Carlo

DECEMBER 9, 2024

What Happens When Data Quality for AI Fails? Amazons Hiring Tool Gone Wrong IBM Watson for Oncology Microsofts Tay Chatbot Disaster How Much Data is Enough for AI? Best Practices: High-Quality Data for AI How to Maintain Data Quality for AI The Role of Data Quality for AI AI lives and breathes data.

High Quality Data 52

High Quality Data Unstructured Data Datasets Data 52

Precisely

MARCH 4, 2024

From AI-generated briefs filled with inaccuracies to scandals that never were , these incidents highlight how easily inadequate data can create flawed results with significant business implications – while simultaneously demonstrating the importance of feeding your AI with trusted, high-quality data.

Amazon Web Services 72

Amazon Web Services Data Integration High Quality Data Government 72

Precisely

SEPTEMBER 9, 2024

How confident are you in the quality of your data? Across industries and business objectives, high-quality data is a must for innovation and data-driven decision-making that keeps you ahead of the competition. Can you trust it for fast, confident decision-making when you need it most?

Management 64

Management High Quality Data Structured Data Data Lake 64

Monte Carlo

APRIL 1, 2024

Data cleaning is an essential step to ensure your data is safe from the adage “garbage in, garbage out.” Because effective data cleaning best practices fix and remove incorrect, inaccurate, corrupted, duplicate, or incomplete data in your dataset; data cleaning removes the garbage before it enters your pipelines.

High Quality Data 52

High Quality Data Datasets Data Data Pipeline 52

Precisely

OCTOBER 8, 2024

This is what makes the breadth and depth of your AI data so essential. Without expansive high-quality data, even the most sophisticated algorithms can lead your business astray. Contextual : The data provides the necessary background for your model to make informed predictions. Read What is Data Enrichment?

High Quality Data 52

High Quality Data Datasets Algorithm Portfolio 52

Precisely

AUGUST 29, 2024

To maximize your investments in AI, you need to prioritize data governance, quality, and observability. Solving the Challenge of Untrustworthy AI Results AI has the potential to revolutionize industries by analyzing vast datasets and streamlining complex processes – but only when the tools are trained on high-quality data.

Data Governance 52

Data Governance Government High Quality Data Datasets 52

Databand.ai

AUGUST 30, 2023

Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

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

Precisely

NOVEMBER 21, 2024

Data input and maintenance : Automation plays a key role here by streamlining how data enters your systems. With automation you become more agile, thanks to the ability to gather high-quality data efficiently and maintain it over time – reducing errors and manual processes. Find out more in our eBook.

Data Integration 59

Data Integration High Quality Data Manufacturing Data 59

Monte Carlo

JUNE 20, 2024

It’s the mantra for data teams, and it underlines the importance of data quality anomaly detection for any organization. The quality of the input affects the quality of the output – and in order for data teams to produce high-quality data products, they need high-quality data from the very start.

High Quality Data 52

High Quality Data Machine Learning Algorithm Datasets 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

Precisely

SEPTEMBER 26, 2024

Data integration struggles = biased outcomes Ensuring comprehensive data integration across your organization is often the first stumbling block. If your business operates with fragmented data across silos, then your AI models are working with incomplete or inconsistent datasets. The impact?

Project 75

Project Government Data Governance Data Integration 75

U-Next

FEBRUARY 27, 2023

Normalization helps keep your data consistent and reliable so that you can make better business decisions with confidence. As a whole, data normalization plays an essential role in business for those who have to deal with large datasets as a part of their daily operations.

IT 98

IT Bytes Database Recruitment 98

Monte Carlo

FEBRUARY 27, 2024

Organizations need to connect LLMs with their proprietary data and business context to actually create value for their customers and employees. They need robust data pipelines, high-quality data, well-guarded privacy, and cost-effective scalability. Data engineers. Who can deliver?

Data Engineering 59

Data Engineering Data Engineer Engineering High Quality Data 59

Data Engineering Weekly

SEPTEMBER 15, 2024

link] Sponsored: IMPACT - Speaker Promo We know high-quality data is powerful. Run the entire pipeline with a sample dataset to verify the chain of tasks executed as expected. But can it predict presidential elections? presidential winners for decades, as our keynote speaker!

Data Engineer 67

Data Engineer Data Engineering Engineering Data Lake 67

DataKitchen

NOVEMBER 27, 2024

Similarly, data teams might struggle to determine actionable steps if the metrics do not highlight specific datasets, systems, or processes contributing to poor data quality. Tracking the quality of specific datasets or features critical to their projects can give the project owner leverage to improve data quality.

Banking 87

Banking Data Datasets Retail 87

U-Next

MARCH 7, 2023

Normalization helps keep your data consistent and reliable so that you can make better business decisions with confidence. As a whole, data normalization plays an essential role in business for those who have to deal with large datasets as a part of their daily operations.

IT 98

IT Bytes Database Recruitment 98

Knowledge Hut

APRIL 25, 2024

Recognizing the difference between big data and machine learning is crucial since big data involves managing and processing extensive datasets, while machine learning revolves around creating algorithms and models to extract valuable information and make data-driven predictions.

Machine Learning 52

Machine Learning Big Data Unstructured Data Data Mining 52

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

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 Engineer 70

Data Engineer Data Engineering Engineering Database-centric 70

Striim

JULY 11, 2024

This continuous data flow guarantees that the most up-to-date, accurate information is always available for immediate analysis. Data Transformation and Enrichment Striim enhances data quality by transforming and enriching it before it reaches Snowflake, ensuring high-quality data for analysis.

Data Ingestion 52

Data Ingestion Datasets PostgreSQL Data Integration 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

Netflix Tech

APRIL 26, 2022

ML algorithms can be only as good as the data that we provide to it. This post will focus on the large volume of high-quality data stored in Axion?—?our Each of these models are trained with different datasets and features along with different stratification and objectives. How do we monitor the quality of data?

Metadata 78

Metadata Datasets Machine Learning Designing 78

Hevo

SEPTEMBER 25, 2024

The importance of data quality within an organization cannot be overemphasized as it is a critical aspect of running and maintaining an efficient data warehouse. It tells us how well a dataset meets certain criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness and fitness for purpose.

Data Warehouse 40

Data Warehouse High Quality Data Datasets Data 40

Monte Carlo

MAY 3, 2024

But even though the data landscape is evolving, many enterprise data organizations are still managing data quality the “old” way: with simple data quality monitoring. The basics haven’t changed: high-quality data is still critical to successful business operations.

High Quality Data 52

High Quality Data Data Architecture Media 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 86

Big Data Metadata Data Warehouse Data 86

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 31, 2024

Key Differences Between AI Data Engineers and Traditional Data Engineers While traditional data engineers and AI data engineers have similar responsibilities, they ultimately differ in where they focus their efforts. Let’s examine a few.

Data Engineering 52

Data Engineering Data Engineer Engineering Unstructured Data 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

Knowledge Hut

APRIL 23, 2024

Big data has revolutionized the world of data science altogether. With the help of big data analytics, we can gain insights from large datasets and reveal previously concealed patterns, trends, and correlations. Learn more about the 4 Vs of big data with examples by going for the Big Data certification online course.

Big Data 52

Big Data Media Datasets Unstructured Data 52

Data Science Blog: Data Engineering

AUGUST 22, 2024

Improved Data Integration Data often comes from various sources, and integrating this data smoothly is essential. Advanced data integration tools now facilitate the merging of different data sets, creating a cohesive and comprehensive dataset for analysis.

Data Preparation 52

Data Preparation Transportation High Quality Data Data Science 52

Monte Carlo

AUGUST 30, 2024

This helps keep runtimes and costs low, while giving teams flexibility in how they build and deliver data. 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.

SQL 40

SQL Engineering Data Lake High Quality Data 40

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

AltexSoft

OCTOBER 13, 2022

Discriminative modeling is used to classify existing data points (e.g., Generative modeling tries to understand the dataset structure and generate similar examples (e.g., is compared to the expected output (y) from the training dataset. When we say “enough data,” we mean “enough high-quality data.”

Algorithm 90

Algorithm Deep Learning Machine Learning Datasets 90

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 Engineer 67

Data Engineer Data Engineering Pipeline-centric Engineering 67

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