AWS Big Data

APRIL 3, 2024

Today, we are pleased to announce that Amazon DataZone is now able to present data quality information for data assets. Other organizations monitor the quality of their data through third-party solutions. Additionally, Amazon DataZone now offers APIs for importing data quality scores from external systems.

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CIO Business Intelligence

NOVEMBER 27, 2024

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

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CIO Business Intelligence

MARCH 25, 2025

Data debt that undermines decision-making In Digital Trailblazer , I share a story of a private company that reported a profitable year to the board, only to return after the holiday to find that data quality issues and calculation mistakes turned it into an unprofitable one.

Risk 140

Risk Cost-Benefit Data-driven Digital Transformation 140

O'Reilly on Data

JUNE 19, 2019

In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. Quality depends not just on code, but also on data, tuning, regular updates, and retraining.

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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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DataKitchen

JUNE 21, 2024

Navigating the Storm: How Data Engineering Teams Can Overcome a Data Quality Crisis Ah, the data quality crisis. It’s that moment when your carefully crafted data pipelines start spewing out numbers that make as much sense as a cat trying to bark. You’ve got yourself a recipe for data disaster.

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DataKitchen

MARCH 12, 2024

Key Success Metrics, Benefits, and Results for Data Observability Using DataKitchen Software Lowering Serious Production Errors Key Benefit Errors in production can come from many sources – poor data, problems in the production process, being late, or infrastructure problems. Data errors can cause compliance risks.

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Metrics Software Cost-Benefit Testing 117

CIO Business Intelligence

JANUARY 30, 2025

Metrics should include system downtime and reliability, security incidents, incident response times, data quality issues and system performance. Set goals and report metrics to determine if you are achieving the goals set out by the organization or the AI governance committee. version 0125). Its date is May 31st, 2025.

Testing 93

Testing Metrics Cost-Benefit Modeling 93

O'Reilly on Data

OCTOBER 13, 2020

In Bringing an AI Product to Market , we distinguished the debugging phase of product development from pre-deployment evaluation and testing. During testing and evaluation, application performance is important, but not critical to success. require not only disclosure, but also monitored testing. Debugging AI Products.

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AWS Big Data

OCTOBER 9, 2024

Some customers build custom in-house data parity frameworks to validate data during migration. Others use open source data quality products for data parity use cases. This takes away important person hours from the actual migration effort into building and maintaining a data parity framework.

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Data Quality Data Lake Data Warehouse Metrics 114

DataKitchen

MAY 7, 2024

DataKitchen Training And Certification Offerings For Individual contributors with a background in Data Analytics/Science/Engineering Overall Ideas and Principles of DataOps DataOps Cookbook (200 page book over 30,000 readers, free): DataOps Certificatio n (3 hours, online, free, signup online): DataOps Manifesto (over 30,000 signatures) One (..)

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Data Quality Testing Metrics Consulting 161

DataKitchen

MARCH 16, 2023

So it’s Monday, and you lead a data analytics team of perhaps 30 people. But wait, she asks you for your team metrics. Like most leaders of data analytic teams, you have been doing very little to quantify your team’s success. Where is your metrics report? What should be in that report about your data team?

Metrics 130

Metrics Data Analytics Analytics Measurement 130

DataKitchen

SEPTEMBER 21, 2023

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. Moreover, advanced metrics like Percentage Regional Sales Growth can provide nuanced insights into business performance. What is Data in Use?

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Testing Data Quality Predictive Modeling Metrics 169

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.

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Testing Manufacturing Data Quality Statistics 214

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).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

DataKitchen

APRIL 26, 2021

GSK had been pursuing DataOps capabilities such as automation, containerization, automated testing and monitoring, and reusability, for several years. At Workiva, they recognized that they are only as good as their data, so they centered their initial DataOps efforts around lowering errors. Multiple Metrics for Success.

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AWS Big Data

OCTOBER 10, 2023

Due to the volume, velocity, and variety of data being ingested in data lakes, it can get challenging to develop and maintain policies and procedures to ensure data governance at scale for your data lake. Data confidentiality and data quality are the two essential themes for data governance.

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Data Quality Data Governance Data Lake Testing 111

Octopai

JULY 11, 2022

And when business users don’t complain, but you know the data isn’t good enough to make these types of calls wisely, that’s an even bigger problem. How are you, as a data quality evangelist (if you’re reading this post, that must describe you at least somewhat, right?), Tie data quality directly to business objectives.

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Data Quality Strategy Business Objectives Sales 98

CIO Business Intelligence

NOVEMBER 13, 2024

Managers tend to incentivize activity metrics and measure inputs versus outputs,” she adds. Woolley recommends that companies consolidate around the minimum number of tools they need to get things done, and have a sandbox process to test and evaluate new tools that don’t get in the way of people doing actual work.

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IT Consulting ROI Cost-Benefit 141

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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O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. It’s a very simple and powerful idea: simulate data that you find interesting and see what a model predicts for that data. 6] Debugging may focus on a variety of failure modes (i.e.,

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

datapine

APRIL 15, 2020

Your Chance: Want to test an agile business intelligence solution? Business intelligence is moving away from the traditional engineering model: analysis, design, construction, testing, and implementation. Test BI in a small group and deploy the software internally. Finalize testing. Test throughout the lifecycle.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

O'Reilly on Data

MARCH 31, 2020

The model outputs produced by the same code will vary with changes to things like the size of the training data (number of labeled examples), network training parameters, and training run time. This has serious implications for software testing, versioning, deployment, and other core development processes.

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DataKitchen

OCTOBER 11, 2021

The data engineer then emails the BI Team, who refreshes a Tableau dashboard. Figure 1: Example data pipeline with manual processes. There are no automated tests , so errors frequently pass through the pipeline. The pipeline has automated tests at each step, making sure that each step completes successfully.

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Testing Metadata Dashboards Statistics 152

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

NOVEMBER 22, 2024

Now, with support for dbt Cloud, you can access a managed, cloud-based environment that automates and enhances your data transformation workflows. This upgrade allows you to build, test, and deploy data models in dbt with greater ease and efficiency, using all the features that dbt Cloud provides.

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Data Lake Data Warehouse Cost-Benefit Data Transformation 103

Wayne Yaddow

MARCH 3, 2025

Photo by CDC on Unsplash Many data pipeline failures and quality issues that are detected by data observability tools in production could have been prevented earlier in the pipeline lifecycle with better pre-production testing strategies. Crucial for time-sensitive analytics and reporting processes.

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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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Testing IT Data-driven Measurement 130

Smart Data Collective

AUGUST 11, 2022

How Artificial Intelligence is Impacting Data Quality. Artificial intelligence has the potential to combat human error by taking up the tasking responsibilities associated with the analysis, drilling, and dissection of large volumes of data. Data quality is crucial in the age of artificial intelligence.

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Data Quality Machine Learning Digital Transformation Big Data 139

DataKitchen

FEBRUARY 23, 2024

Webinar: Beyond Data Observability: Personalization DataKitchen DataOps Observability Problem Statement White Paper: ‘Taming Chaos’ Technical Product Overview Four-minute online demo Detailed Product: Documentation Webinar: Data Observability Demo Day DataKitchen DataOps TestGen Problem Statement White Paper: ‘Mystery Box Full Of Data Errors’ (..)

Testing 117

Testing Data-driven Data Quality Metrics 117

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

Metrics 211

Metrics ROI Measurement Cost-Benefit 211

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. In most cases, this can prove detrimental to the business.

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CIO Business Intelligence

JULY 18, 2022

While this may seem like an odd question, understanding organizational change levers can help uncover opportunities for improving your data supply-chain processes. A key challenge with organizational alignment is communicating across all departments the goals, metrics, and benefits of a process or function.

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DataKitchen

NOVEMBER 18, 2022

As he thinks through the various journeys that data take in his company, Jason sees that his dashboard idea would require extracting or testing for events along the way. So, the only way for a data journey to truly observe what’s happening is to get his tools and pipelines to auto-report events. Data and tool tests.

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CIO Business Intelligence

SEPTEMBER 10, 2024

The testing phase, particularly user acceptance testing (UAT), can become a labor-intensive bottleneck — and a budget breaker. According to a 2023 Capgemini report , companies spend about 35% of their IT budget on testing — a figure that has remained stubbornly high despite advancements in automation.

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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. The image above shows an example ‘’data at rest’ test result. For example, a test can check the top fifty customers or suppliers.

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Occam's Razor

AUGUST 13, 2012

The questions reveal a bunch of things we used to worry about, and continue to, like data quality and creating data driven cultures. That means: All of these metrics are off. This is exactly why the Page Value metric (in the past called $index value) was created. "Was the data correct?" EU Cookies!)

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