The Modern Data Company

JULY 18, 2023

Data Quality and Reliability Ensuring data quality is crucial for any data product. High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights.

Data Governance 69

Data Governance High Quality Data Government Data 69

Databand.ai

AUGUST 30, 2023

It plays a critical role in ensuring that users of the data can trust the information they are accessing. There are several ways to ensure data consistency, including implementing data validation rules, using data standardization techniques, and employing data synchronization processes.

Data Integration 52

Data Integration Data Cleanse Data Validation High Quality Data 52

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

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. In this article, we present six intrinsic data quality techniques that serve as both compass and map in the quest to refine the inner beauty of your data.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Precisely

AUGUST 29, 2024

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. So, the risk of entering into these initiatives without taking care of your data first is simply too high.

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

Monte Carlo

APRIL 1, 2024

Implement Routine Data Audits Build a data cleaning cadence into your data teams’ schedule. Routine data quality checks will not only help to reduce the risk of discrepancies in your data, but it will also help to fortify a culture of high-quality data throughout your organization.

High Quality Data 52

High Quality Data Datasets Data Data Pipeline 52

Databand.ai

AUGUST 30, 2023

Accurate data ensures that these decisions and strategies are based on a solid foundation, minimizing the risk of negative consequences resulting from poor data quality. There are various ways to ensure data accuracy. It can be done at the time of data entry or afterward.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

Airbnb Tech

NOVEMBER 28, 2023

Enable full visibility into the quality of our offline data warehouse and individual data assets. Composing the Score Before diving into the nuances of measuring data quality, we drove alignment on the vision by defining our DQ Score guiding principles. whereas others were more like proxies for quality (e.g.,

Data Warehouse 98

Data Warehouse Metadata Data Certification 98

The Modern Data Company

JULY 18, 2023

Data Quality and Reliability Ensuring data quality is crucial for any data product. High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights.

Data Governance 52

Data Governance High Quality Data Government Data 52

The Modern Data Company

JULY 18, 2023

Data Quality and Reliability Ensuring data quality is crucial for any data product. High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights.

Data Governance 52

Data Governance High Quality Data Government 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. Attention to Detail : Critical for identifying data anomalies.

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

Databand.ai

JULY 11, 2023

There are several reasons why organizations need a data quality platform to ensure the accuracy and reliability of their data. With a data quality platform in place, decision-makers can trust the data they use, reducing the risk of costly mistakes and missed opportunities.

Data Cleanse 52

Data Cleanse Telecommunication High Quality Data BI 52

DataKitchen

SEPTEMBER 21, 2023

Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors. There are multiple locations where problems can happen in a data and analytic system. What is Data in Use?

Raw Data 72

Raw Data Data Business Intelligence Data Engineering 72

Databand.ai

AUGUST 30, 2023

Data cleansing: Implement corrective measures to address identified issues and improve dataset accuracy levels. Data validation: Ensure new database entries adhere to predefined rules or standards to maintain dataset consistency. Additionally, high-quality data reduces costly errors stemming from inaccurate information.

Building 52

Building Data Cleanse Data Governance Datasets 52

Precisely

MARCH 14, 2023

Read Quality data you can depend on – today, tomorrow, and beyond For many years Precisely customers have ensured the accuracy of data across their organizations by leveraging our leading data solutions including Trillium Quality, Spectrum Quality, and Data360 DQ+. What does all this mean for your business?

Data Integration 52

Data Integration High Quality Data BI Data 52

U-Next

SEPTEMBER 7, 2022

The HR team can make better judgments regarding employee engagement programs, governmental requirements, and other issues if it has correct employee data. . A data validation program can be useful. . We are aware that data are the source of information. Operational Effectiveness . One database, multiple locations: .

Data Management 52

Data Management Management Electronics Unstructured 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 Software Engineer 52

Databand.ai

JUNE 14, 2023

It enables: Enhanced decision-making: Accurate and reliable data allows businesses to make well-informed decisions, leading to increased revenue and improved operational efficiency. Risk mitigation: Data errors can result in expensive mistakes or even legal issues.

Amazon Web Services 40

Amazon Web Services Datasets High Quality Data ETL Tools 40

Knowledge Hut

JANUARY 25, 2024

Fixing Errors: The Gremlin Hunt Errors in data are like hidden gremlins. Use spell-checkers and data validation checks to uncover and fix them. Automated data validation tools can also help detect anomalies, outliers, and inconsistencies. Trustworthy Analytics: Reliable data supports accurate statistical analysis.

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

Monte Carlo

JULY 11, 2023

Data accuracy vs. data quality Data accuracy and data quality are related concepts but they are not synonymous. While accurate data is free from errors or mistakes, high-quality data goes beyond accuracy to encompass additional aspects that contribute to its overall value and usefulness.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Monte Carlo

JANUARY 25, 2023

There is, however, an added dimension to this relationship: data producers are often consumers of upstream data sources. Data warehouse producers wear both hats working with upstream producers so they can consume high-quality data and producing high-quality data to provide to their consumers.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

DataKitchen

JUNE 19, 2023

.” – Take A Bow, Rihanna (I may have heard it wrong) Validating data quality at rest is critica l to the overall success of any Data Journey. Using automated data validation tests, you can ensure that the data stored within your systems is accurate, complete, consistent, and relevant to the problem at hand.

Data 40

Data Data Validation Utilities High Quality Data 40

Ascend.io

JUNE 8, 2023

Data engineers also need fine-grained cost information about data products and the pipelines that run them to optimize the transformations, make informed infrastructure changes, and apply other cost management techniques. Actionable data quality: Stakeholders are counting on having the right data in the right format at the right time.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Airbnb Tech

NOVEMBER 24, 2020

The comprehensive Midas quality guarantee, coupled with clear identification of certified data across Airbnb’s internal tools, became our big bet to guarantee access to high quality data across the company. Figure 4: An overview of the nine steps in the Midas Certification process.

Data Warehouse 52

Data Warehouse Certification Data Pipeline Data 52

Ascend.io

APRIL 27, 2023

Enhanced scalability: Data pipeline automation enables organizations to easily scale their data infrastructure to handle increasing data volumes and complexity, without compromising performance or agility. The sessions revealed how data pipelines built with automation offer superior results compared to traditional approaches.

Data Pipeline 52

Data Pipeline Pipeline-centric Data Validation Data Engineering 52