AWS Big Data

JUNE 6, 2023

We are excited to announce the General Availability of AWS Glue Data Quality. Our journey started by working backward from our customers who create, manage, and operate data lakes and data warehouses for analytics and machine learning. It takes days for data engineers to identify and implement data quality rules.

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AWS Big Data

JUNE 6, 2023

Alerts and notifications play a crucial role in maintaining data quality because they facilitate prompt and efficient responses to any data quality issues that may arise within a dataset. This proactive approach helps mitigate the risk of making decisions based on inaccurate information.

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AWS Big Data

MAY 23, 2024

They establish data quality rules to ensure the extracted data is of high quality for accurate business decisions. These rules assess the data based on fixed criteria reflecting current business states. We are excited to talk about how to use dynamic rules , a new capability of AWS Glue Data Quality.

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datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

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AWS Big Data

MARCH 12, 2024

In recent years, data lakes have become a mainstream architecture, and data quality validation is a critical factor to improve the reusability and consistency of the data. In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset.

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Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

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Alation

JANUARY 20, 2022

Several weeks ago (prior to the Omicron wave), I got to attend my first conference in roughly two years: Dataversity’s Data Quality and Information Quality Conference. Ryan Doupe, Chief Data Officer of American Fidelity, held a thought-provoking session that resonated with me. Step 2: Data Definitions.

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Data Quality Data Governance Metrics Statistics 94

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before. Machine learning adds uncertainty.

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AWS Big Data

DECEMBER 4, 2024

It’s the preferred choice when customers need more control and customization over the data integration process or require complex transformations. This flexibility makes Glue ETL suitable for scenarios where data must be transformed or enriched before analysis. The status and statistics of the CDC load are published into CloudWatch.

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datapine

APRIL 16, 2019

While sometimes it’s okay to follow your instincts, the vast majority of your business-based decisions should be backed by metrics, facts, or figures related to your aims, goals, or initiatives that can ensure a stable backbone to your management reports and business operations. Quantitative data analysis focuses on numbers and statistics.

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DataKitchen

MAY 10, 2021

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. That’s a fair point, and it places emphasis on what is most important – what best practices should data teams employ to apply observability to data analytics. It’s not about data quality

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datapine

DECEMBER 17, 2019

The purpose is not to track every statistic possible, as you risk being drowned in data and losing focus. Inclusivity: Expanding on decision-making, as these kinds of dashboards and reports serve up digestible data visualizations, members of your IT department will be able to use these reporting tools with ease, even under pressure.

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O'Reilly on Data

DECEMBER 12, 2019

Residual plots place input data and predictions into a two-dimensional visualization where influential outliers, data-quality problems, and other types of bugs often become plainly visible. For model training and selection, we recommend considering fairness metrics when selecting hyperparameters and decision cutoff thresholds.

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AWS Big Data

JULY 29, 2024

In this blog post, we’ll discuss how the metadata layer of Apache Iceberg can be used to make data lakes more efficient. You will learn about an open-source solution that can collect important metrics from the Iceberg metadata layer. It is essential for optimizing read and write performance. The default output is log based.

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CIO Business Intelligence

FEBRUARY 20, 2025

By collecting and evaluating large amounts of data, HR managers can make better personnel decisions faster that are not (only) based on intuition and experience. However, it is often unclear where the data needed for reporting is stored and what quality it is in.

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DataKitchen

OCTOBER 11, 2021

Based on business rules, additional data quality tests check the dimensional model after the ETL job completes. While implementing a DataOps solution, we make sure that the pipeline has enough automated tests to ensure data quality and reduce the fear of failure. Data Completeness – check for missing data.

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Anmut

JULY 12, 2021

Facts, events, statements, and statistics without proper context have little value and only lead to questions and confusion.?This This is true for life in general, but it’s especially applicable to the data you use to power your business. Data quality vs data condition: basic definitions & differences.

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DataKitchen

JUNE 24, 2021

GE formed its Digital League to create a data culture. One of the keys for our success was really focusing that effort on what our key business initiatives were and what sorts of metrics mattered most to our customers. Chapin also mentioned that measuring cycle time and benchmarking metrics upfront was absolutely critical. “It

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DataKitchen

NOVEMBER 18, 2022

In addition to the tracking of relationships and quality metrics, DataOps Observability journeys allow users to establish baselines?concrete concrete expectations for run schedules, run durations, data quality, and upstream and downstream dependencies. An interface for both business and technical users.

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Sisense

SEPTEMBER 3, 2020

Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization.

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Octopai

OCTOBER 26, 2022

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. Continuous pipeline monitoring with SPC (statistical process control). Results (i.e.

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DataKitchen

JUNE 7, 2021

Hopefully, with metrics in place, you can show measured improvements in productivity and quality that will win converts. Improve Collaboration, both Inter- and Intra -team – If the individuals in your data-analytics team don’t work together, it can impact analytics-cycle time, data quality, governance, security and more.

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IBM Big Data Hub

NOVEMBER 29, 2023

Although adding noise slightly reduces output accuracy (this is the “cost” of differential privacy), it does not compromise utility or data quality compared to traditional data masking techniques. Utility AI models require sufficient data for effective training and obtaining real datasets can be time-consuming.

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Cloudera

DECEMBER 1, 2022

Data observability provides insight into the condition and evolution of the data resources from source through the delivery of the data products. Barr Moses of Monte Carlo presents it as a combination of data flow, data quality, data governance, and data lineage.

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DataKitchen

JANUARY 19, 2022

For example, after a Matillion job completes, DataKitchen pulls runtime variables like rowCount, invalid orders, and invalid zip codes and can perform historical balance, location balance, and statistical process control on these values.

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datapine

DECEMBER 4, 2019

Applied to business, it is used to analyze current and historical data in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company. The accuracy of the predictions depends on the data used to create the model. Graph Analytics.

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AWS Big Data

AUGUST 1, 2024

Migrating to Amazon Redshift offers organizations the potential for improved price-performance, enhanced data processing, faster query response times, and better integration with technologies such as machine learning (ML) and artificial intelligence (AI). This can help identify any discrepancies in data values or data types.

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AWS Big Data

JULY 13, 2023

Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices. Goals – Lay the foundation for a data platform that can be used in the future by internal and external stakeholders.

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Peter James Thomas

AUGUST 12, 2018

However, often the biggest stumbling block is a human one, getting people to buy in to the idea that the care and attention they pay to data capture will pay dividends later in the process. These and other areas are covered in greater detail in an older article, Using BI to drive improvements in data quality.

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Alation

JULY 19, 2022

In this way, a data scientist benefits from business knowledge that they might not otherwise have access to. The catalog facilitates the synergy of the domain experts’ subject matter expertise with the data scientists statistical and coding expertise. Modern data catalogs surface a wide range of data asset types.

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Metadata Data Quality Statistics Data Science 59

Occam's Razor

JUNE 8, 2006

In the morass of data quality and TV and UV and cookie values and ab test id’s and sessions and shopper_ids we look at massive amounts of data and forget that real people are using our websites. A vast majority of us fail at this, we face bad or incomplete data and we get paralysed.

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KPI Measurement Statistics Metrics 94

Domino Data Lab

JUNE 23, 2019

Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. That’s another pattern.

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AWS Big Data

JULY 27, 2023

Then the claims data is ingested into the catalog (so it’s visible to analysts), after enriching it with some relevant details about the corresponding medical providers coming from a separate source. Claim Amount values will likely be used for some calculations, so convert to number, and Claim Data should be converted to date type.

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Visualization Cost-Benefit Data Quality Publishing 98

Ontotext

JUNE 22, 2023

Recent statistics shed light on the realities in the world of current drug development: out of about 10,000 compounds that undergo clinical research, only 1 emerges successfully as an approved drug. The current process involves costly wet lab experiments, which are often performed multiple times to achieve statistically significant results.

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Metrics Statistics Visualization Data-driven 52

IBM Big Data Hub

JULY 28, 2022

Metadata enrichment is about scaling the onboarding of new data into a governed data landscape by taking data and applying the appropriate business terms, data classes and quality assessments so it can be discovered, governed and utilized effectively. Support for BI reporting. Public API.

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Metadata Machine Learning Data Quality Statistics 56

DataRobot Blog

MAY 26, 2022

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

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Risk Modeling Metrics Business Objectives 52

Wayne Yaddow

FEBRUARY 26, 2025

If your source data structure changes or new business logic is added, the process AI can create corresponding tests on the fly, reducing the maintenance burden on your QA team. This leads to faster iteration cycles and helps maintain high data quality standards, even as data pipelines grow morecomplex.

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