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

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

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

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. The top quartile, in contrast, reported better results, which were supported by better processes and technology.

Insurance 64

Insurance Data Validation Government High Quality Data 64

Precisely

AUGUST 8, 2024

Better data-driven decision-making, higher ROI, stronger compliance – what do all these outcomes have in common? They rely on high-quality data. But the truth is, it’s harder than ever for organizations to maintain that level of data quality. With a robust approach to data integrity.

Data Integration 52

Data Integration Data Management Data 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

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring (#2) Introduction Ensuring the accuracy and timeliness of data ingestion is a cornerstone for maintaining the integrity of data systems. This process is critical as it ensures data quality from the onset.

Data Ingestion 56

Data Ingestion Transportation High Quality Data Data Schemas 56

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. This is known as data governance.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Monte Carlo

JANUARY 5, 2024

A visualization of the flow of data in data lakehouse architecture vs. data warehouse and data lake. Innovations in data lakehouse architecture have been an important step toward more flexible and powerful data management systems. Image courtesy of Databricks.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

JANUARY 5, 2024

A visualization of the flow of data in data lakehouse architecture vs. data warehouse and data lake. Innovations in data lakehouse architecture have been an important step toward more flexible and powerful data management systems. Image courtesy of Databricks.

Architecture 52

Architecture Data Lake Metadata Unstructured Data 52

Monte Carlo

OCTOBER 10, 2024

As the use of AI becomes more ubiquitous across data organizations and beyond, data quality rises in importance right alongside it. After all, you can’t have high-quality AI models without high-quality data feeding them. Table of Contents What Does an AI Data Quality Analyst Do?

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

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

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. link] Nvidia: What Is Sovereign AI?

Data Engineering 67

Data Engineering Data Engineer Pipeline-centric Engineering 67

Precisely

AUGUST 15, 2024

An increasing number of GenAI tools use large language models that automate key data engineering, governance, and master data management tasks. These tools can generate automated outputs including SQL and Python code, synthetic datasets, data visualizations, and predictions – significantly streamlining your data pipeline.

Government 64

Government Business Analyst High Quality Data Data 64

Precisely

MARCH 14, 2023

New technologies are making it easier for customers to process increasingly large datasets more rapidly. But early adopters realized that the expertise and hardware needed to manage these systems properly were complex and expensive. Design in the cloud, deploy anywhere – with an array of deployment options for complex data processes.

Data Integration 52

Data Integration High Quality Data BI 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 23, 2023

The stakes are high and there isn’t a tolerance for error. The Data Integrity Capabilities You Need for ESG Reporting Now that we’ve covered why you need to have clear, accurate, and readily accessible data to show the progress of your ESG efforts, let’s walk through a snapshot of what that means for your data management capabilities.

Data Integration 52

Data Integration Government High Quality Data Data 52

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. Ready to leap to the next level of data management prowess?

Data Cleanse 52

Data Cleanse ETL Tools Datasets High Quality Data 52

Databand.ai

JULY 17, 2023

DataOps , short for Data Operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data management processes. It aims to streamline the entire data lifecycle—from ingestion and preparation to analytics and reporting.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

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

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

Striim

JULY 10, 2024

This step is pivotal in ensuring data consistency and relevance, essential for the accuracy of subsequent predictive models. The heart of the process lies in training advanced machine learning models on this refined dataset. Take UPS, for instance.

Management 52

Management Transportation Machine Learning High Quality 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 Algorithm 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

MARCH 24, 2023

The key differences are that data integrity refers to having complete and consistent data, while data validity refers to correctness and real-world meaning – validity requires integrity but integrity alone does not guarantee validity. What is Data Integrity? How Do You Maintain Data Validity?

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 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

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 40

Data Pipeline Data Lake Data Warehouse Datasets 40

Monte Carlo

FEBRUARY 8, 2024

Gartner While Gartner hasn’t produced a data observability magic quadrant or report ranking data observability vendors, they have named it one of the hottest emerging technologies and placed it on the 2023 Data Management Hype Cycle. That means data engineers and high quality data are integral to the solution.

BI 59

BI Data Pipeline Data Warehouse Data 59

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

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. The Hub Data Journey provides the raw data and adds value through a ‘contract.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

ProjectPro

FEBRUARY 21, 2023

Due to the enormous amount of data being generated and used in recent years, there is a high demand for data professionals, such as data engineers, who can perform tasks such as data management, data analysis, data preparation, etc. big data and ETL tools, etc.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Monte Carlo

MARCH 28, 2024

A data product, coined by DJ Patil, Former Chief Data Scientist of the United States, has several components, including a product management process, the domain wrapper comprising a semantic layer, business logic and metrics, and access. Simply put, a data product is much more than a dataset alone.

Data 52

Data High Quality Data Machine Learning Accessibility 52

Databand.ai

JANUARY 24, 2023

DataOps is an automated, process-oriented methodology used by analytics and data teams to improve quality and reduce the cycle times of data and analytics. The goal of DataOps is to deliver value faster by creating predictable delivery and change management of data, data models, and related artifacts.”

Retail 52

Retail Banking Government Manufacturing 52

Ascend.io

APRIL 27, 2023

In this article, we’ll recap the key takeaways from the summit and the groundbreaking advancements in data pipeline automation that we’re working on at Ascend. Link data products within and across data clouds , allowing users to access and analyze data in a unified, consistent manner.

Data Pipeline 52

Data Pipeline Pipeline-centric Data Validation Data Engineering 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