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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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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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

JUNE 6, 2023

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning (ML), and application development. Hundreds of thousands of customers use data lakes for analytics and ML to make data-driven business decisions.

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Data Quality Data-driven Data Lake Metrics 98

DataKitchen

SEPTEMBER 21, 2023

These layers help teams delineate different stages of data processing, storage, and access, offering a structured approach to data management. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

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Testing Data Quality Predictive Modeling Metrics 173

DataKitchen

MAY 10, 2024

The Third of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production. This process is critical as it ensures data quality from the onset. Examples include regular loading of CRM data and anomaly detection.

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DataKitchen

NOVEMBER 10, 2023

Extrinsic Control Deficit: Many of these changes stem from tools and processes beyond the immediate control of the data team. Unregulated ETL/ELT Processes: The absence of stringent data quality tests in ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes further exacerbates the problem.

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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. This ensures that each change is tracked and reversible, enhancing data governance and auditability.

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Jim Harris

DECEMBER 24, 2015

Have you ever experienced that sinking feeling, where you sense if you don’t find data quality, then data quality will find you? These discussions are a critical prerequisite for determining data usage, standards, and the business relevant metrics for measuring and improving data quality.

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AWS Big Data

JANUARY 15, 2025

cycle_end";') con.close() With this, as the data lands in the curated data lake (Amazon S3 in parquet format) in the producer account, the data science and AI teams gain instant access to the source data eliminating traditional delays in the data availability.

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Wayne Yaddow

MARCH 3, 2025

When implementing automated validation, AI-driven regression testing, real-time canary pipelines, synthetic data generation, freshness enforcement, KPI tracking, and CI/CD automation, organizations can shift from reactive data observability to proactive data quality assurance.

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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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Alation

MAY 24, 2022

In a sea of questionable data, how do you know what to trust? Data quality tells you the answer. It signals what data is trustworthy, reliable, and safe to use. It empowers engineers to oversee data pipelines that deliver trusted data to the wider organization. Today, as part of its 2022.2

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Data Quality Metadata Reporting Metrics 52

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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Testing Manufacturing Data Quality Statistics 214

Alation

AUGUST 5, 2021

What is Data Quality? Data quality is defined as: the degree to which data meets a company’s expectations of accuracy, validity, completeness, and consistency. By tracking data quality , a business can pinpoint potential issues harming quality, and ensure that shared data is fit to be used for a given purpose.

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DataKitchen

JANUARY 19, 2022

The Matillion data integration and transformation platform enables enterprises to perform advanced analytics and business intelligence using cross-cloud platform-as-a-service offerings such as Snowflake. DataOps recommends that tests monitor data continuously in addition to checks performed when pipelines are run on demand.

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BizAcuity

NOVEMBER 13, 2024

Beyond mere data collection, BI consulting helps businesses create a cohesive data strategy that aligns with organizational goals. This approach involves everything from identifying key metrics to implementing analytics systems and designing dashboards.

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DataKitchen

JUNE 19, 2023

Another way to look at the five pillars is to see them in the context of a typical complex data estate. Monitoring is another pillar of Data Journeys, extending down the stack. Moreover, cost monitoring ensures that your data operations stay within budget and that resources are used efficiently. Donkey: Oh, they have layers.

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AWS Big Data

SEPTEMBER 11, 2024

The application supports custom workflows to allow demand and supply planning teams to collaborate, plan, source, and fulfill customer orders, then track fulfillment metrics via persona-based operational and management reports and dashboards. The data quality (DQ) checks are managed using DQ configurations stored in Aurora PostgreSQL tables.

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CIO Business Intelligence

NOVEMBER 29, 2022

Here, I’ll highlight the where and why of these important “data integration points” that are key determinants of success in an organization’s data and analytics strategy. Layering technology on the overall data architecture introduces more complexity. Data and cloud strategy must align.

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CIO Business Intelligence

APRIL 29, 2022

Despite soundings on this from leading thinkers such as Andrew Ng , the AI community remains largely oblivious to the important data management capabilities, practices, and – importantly – the tools that ensure the success of AI development and deployment. Further, data management activities don’t end once the AI model has been developed.

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AWS Big Data

FEBRUARY 29, 2024

Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications.

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DataKitchen

OCTOBER 2, 2023

Data Journeys track and monitor all levels of the data stack, from data to tools to code to tests across all critical dimensions. A Data Journey supplies real-time statuses and alerts on start times, processing durations, test results, and infrastructure events, among other metrics.

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AWS Big Data

JULY 20, 2023

Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack. Moreover, running advanced analytics and ML on disparate data sources proved challenging.

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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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AWS Big Data

MARCH 26, 2024

In 2022, AWS commissioned a study conducted by the American Productivity and Quality Center (APQC) to quantify the Business Value of Customer 360. The following figure shows some of the metrics derived from the study. Data exploration Data exploration helps unearth inconsistencies, outliers, or errors.

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datapine

FEBRUARY 22, 2022

IT should be involved to ensure governance, knowledge transfer, data integrity, and the actual implementation. Clean data in, clean analytics out. Cleaning your data may not be quite as simple, but it will ensure the success of your BI. Indeed, every year low-quality data is estimated to cost over $9.7

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CIO Business Intelligence

JUNE 5, 2024

With the help of Hawk-Eye, Statcast tracks and quantifies all manner of data: pitching (including velocity, spin rate and direction, and movement), hitting (exit velocity, launch angle, batted ball distance), running (sprint speed, base-to-base times), and fielding (arm strength, catch probability, catcher pop time).

Optimization 125

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FineReport

MAY 31, 2023

Financial Performance Dashboard The financial performance dashboard provides a comprehensive overview of key metrics related to your balance sheet, shedding light on the efficiency of your capital expenditure. While sales dashboards focus on future prospects, accounting primarily focuses on analyzing the same metrics retrospectively.

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Octopai

JUNE 9, 2024

For instance, aligning patient care data from Oracle databases with operational metrics from Power BI was daunting without clear data lineage. Different departments managed their data independently, leading to silos and inconsistencies. This led to better integration and consistency across the organization.

IT 52

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FineReport

MAY 25, 2023

An HR dashboard functions as an advanced analytics tool that utilizes interactive data visualizations to present crucial HR metrics. Similar to various other business departments, human resources is gradually transforming into a data-centric function. Otherwise, it may become a ‘vanity metric’ in the HR dashboard.

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Dashboards Metrics Key Performance Indicator Cost-Benefit 52

Wayne Yaddow

FEBRUARY 26, 2025

Photo by Markus Spiske on Unsplash Introduction Senior data engineers and data scientists are increasingly incorporating artificial intelligence (AI) and machine learning (ML) into data validation procedures to increase the quality, efficiency, and scalability of data transformations and conversions.

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Smarten

APRIL 19, 2024

Key Influencer Analytics to understand interrelationships and impact of data columns with each other and target columns Sentiment Analysis This sophisticated analytical technique goes beyond quantitative questionnaires and surveys to capture the real opinions, feelings and sentiments of consumers, employees, and other stakeholders.

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Alation

JANUARY 27, 2022

Creating a single view of any data, however, requires the integration of data from disparate sources. Data integration is valuable for businesses of all sizes due to the many benefits of analyzing data from different sources. But data integration is not trivial. Establishes Trust in Data.

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AWS Big Data

MARCH 3, 2023

It has been well published since the State of DevOps 2019 DORA Metrics were published that with DevOps, companies can deploy software 208 times more often and 106 times faster, recover from incidents 2,604 times faster, and release 7 times fewer defects. Finally, data integrity is of paramount importance.

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Jet Global

MAY 1, 2020

They are going to have different ways of combining numbers into metrics. We can almost guarantee you different results from each, and you end up with no data integrity whatsoever. Data quality issues. Here’s the ugly truth: Everybody has a data quality problem. Learn how to prepare your data for BI.

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IBM Big Data Hub

OCTOBER 12, 2023

Security and privacy —When all data scientists and AI models are given access to data through a single point of entry, data integrity and security are improved. They can also spot and root out bias and drift proactively by monitoring, cataloging and governing their models.

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