DataKitchen

DECEMBER 9, 2024

data engineers delivered over 100 lines of code and 1.5 data quality tests every day to support a cast of analysts and customers. The team used DataKitchen’s DataOps Automation Software, which provided one place to collaborate and orchestrate source code, data quality, and deliver features into production.

Data Quality 161

Data Quality Data Lake Testing Statistics 161

DataKitchen

SEPTEMBER 18, 2024

A DataOps Approach to Data Quality The Growing Complexity of Data Quality Data quality issues are widespread, affecting organizations across industries, from manufacturing to healthcare and financial services. 73% of data practitioners do not trust their data (IDC).

Scorecard 177

Scorecard Data Quality Measurement Testing 177

DataKitchen

MARCH 20, 2025

We’ve identified two distinct types of data teams: process-centric and data-centric. Understanding this framework offers valuable insights into team efficiency, operational excellence, and data quality. Process-centric data teams focus their energies predominantly on orchestrating and automating workflows.

Data Quality 161

Data Quality Testing Metrics Management 161

DataKitchen

JULY 12, 2023

The Syntax, Semantics, and Pragmatics Gap in Data Quality Validate Testing Data Teams often have too many things on their ‘to-do’ list. They have a backlog full of new customer features or data requests, and they go to work every day knowing that they won’t and can’t meet customer expectations.

Data Quality 130

Data Quality Testing Manufacturing Finance 130

DataKitchen

JANUARY 4, 2023

Why do 78% of data engineers wish their job came with a therapist to help manage work-related stress? THEY DO NOT TEST. The post ON-DEMAND WEBINAR: Managing Stress in Data Engineering: Data Quality and Testing Techniques for Data Observability first appeared on DataKitchen.

Data Quality 130

Data Quality Testing Management 130

DataKitchen

AUGUST 6, 2024

DataKitchen’s Data Quality TestGen found 18 potential data quality issues in a few minutes (including install time) on data.boston.gov building permit data! Imagine a free tool that you can point at any dataset and find actionable data quality issues immediately! first appeared on DataKitchen.

Data Quality 124

Data Quality Testing Cost-Benefit Software 124

David Menninger's Analyst Perspectives

NOVEMBER 19, 2024

To improve data reliability, enterprises were largely dependent on data-quality tools that required manual effort by data engineers, data architects, data scientists and data analysts.  With the aim of rectifying that situation, Bigeye’s founders set out to build a business around data observability.

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Data Quality Dashboards Data-driven Software 130

DataKitchen

APRIL 13, 2021

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Genie — Distributed big data orchestration service by Netflix.

Testing 300

Testing Machine Learning Consulting Data Quality 300

CIO Business Intelligence

APRIL 8, 2025

As technology and business leaders, your strategic initiatives, from AI-powered decision-making to predictive insights and personalized experiences, are all fueled by data. Yet, despite growing investments in advanced analytics and AI, organizations continue to grapple with a persistent and often underestimated challenge: poor data quality.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102

DataKitchen

NOVEMBER 18, 2022

Question: What is the difference between Data Quality and Observability in DataOps? Data Quality is static. It is the measure of data sets at any point in time. A financial analogy: Data Quality is your Balance Sheet, Data Observability is your Cash Flow Statement.

Data Quality 130

Data Quality Testing Measurement Data Integration 130

Smart Data Collective

APRIL 20, 2022

It takes a lot of split-testing and data collection to optimize your strategy to approach these types of conversion rates. Companies with an in-depth understanding of data analytics will have more successful Amazon PPC marketing strategies. However, it is important to make sure the data is reliable.

Data Quality 128

Data Quality Analytics Testing Sales 128

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.

Enterprise 141

Enterprise Data Quality Structured Data Modeling 141

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.

Data Quality 130

Data Quality Measurement Metrics Data Collection 130

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.

Data Quality 103

Data Quality Visualization Metadata Metrics 103

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Ensuring Data Quality in New Data Sources (#1) Introduction to Data Evaluation in Data Observability Ensuring their quality and integrity before incorporating new data sources into production is paramount.

Data Quality 124

Data Quality Interactive Dashboards Testing 124

DataKitchen

DECEMBER 29, 2022

The Terms and Conditions of a Data Contract are Automated Production Data Tests. A data contract is a formal agreement between two parties that defines the structure and format of data that will be exchanged between them. The best data contract is an automated production data test.

Testing 130

Testing Statistics Data Quality Data Integration 130

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.

Data Quality 103

Data Quality Measurement Testing Visualization 103

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

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. Tie tests to alerts.

Testing 214

Testing Manufacturing Data Quality Statistics 214

O'Reilly on Data

JUNE 19, 2019

More precisely, models built or tuned for specific applications (in reality, this means models + data ) will need to be managed and protected : A database for authorization and security: who has read/write access to certain models. A catalog or a database that lists models, including when they were tested, trained, and deployed.

Modeling 249

Modeling Machine Learning Testing Metrics 249

DataKitchen

JANUARY 25, 2022

Ensuring that data is available, secure, correct, and fit for purpose is neither simple nor cheap. Companies end up paying outside consultants enormous fees while still having to suffer the effects of poor data quality and lengthy cycle time. . When a job is automated, there is little advantage to outsourcing. .

Consulting 244

Consulting Testing Data Lake Data Quality 244

O'Reilly on Data

MARCH 24, 2020

However, attempting to repurpose pre-existing data can muddy the water by shifting the semantics from why the data was collected to the question you hope to answer. ” One of his more egregious errors was to continually test already collected data for new hypotheses until one stuck, after his initial hypothesis failed [4].

Machine Learning 364

Machine Learning Statistics Data Quality Modeling 364

Analytics Vidhya

MARCH 13, 2024

Introduction Whether you’re a fresher or an experienced professional in the Data industry, did you know that ML models can experience up to a 20% performance drop in their first year? Monitoring these models is crucial, yet it poses challenges such as data changes, concept alterations, and data quality issues.

Data Quality 319

Data Quality Modeling Analytics IT 319

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

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.

Data Quality 103

Data Quality Data Governance Data Lake Testing 103

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.

Data Quality 95

Data Quality Data Lake Data Warehouse Metrics 95

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.

Data Quality 98

Data Quality Strategy Business Objectives Sales 98

Jim Harris

JANUARY 1, 2018

The Chicken Littles of Data Quality use sound bites like “data quality problems cost businesses more than $600 billion a year!” or “poor data quality costs organizations 35% of their revenue!” Furthermore, the reason that citing specific examples of poor data quality (e.g.,

Data Quality 77

Data Quality Cost-Benefit Consulting Dashboards 77

CIO Business Intelligence

AUGUST 2, 2022

We are also combining that with data from different sources as a pilot to see if it makes sense and tests out a hypothesis. The second is the data quality in our legacy systems. So, data quality is definitely one of our biggest challenges that is tied closely to the foundational changes. That’s one.

Data Quality 97

Data Quality Digital Transformation Machine Learning Predictive Analytics 97

TDAN

MARCH 16, 2021

The purpose of this article is to provide a model to conduct a self-assessment of your organization’s data environment when preparing to build your Data Governance program. Take the […].

Data Governance 105

Data Governance Testing Modeling Data Quality 105

AWS Big Data

JUNE 6, 2023

AWS Glue Data Quality allows you to measure and monitor the quality of data in your data repositories. It’s important for business users to be able to see quality scores and metrics to make confident business decisions and debug data quality issues. An AWS Glue crawler crawls the results.

Data Quality 78

Data Quality Metrics Visualization Dashboards 78

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 (..)

Data Quality 161

Data Quality Testing Metrics Consulting 161

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. Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

Rocket-Powered Data Science

FEBRUARY 15, 2023

Clean it, annotate it, catalog it, and bring it into the data family (connect the dots and see what happens). Keep it agile, with short design, develop, test, release, and feedback cycles: keep it lean, and build on incremental changes. Test early and often. Test and refine the chatbot. Expect continuous improvement.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

O'Reilly on Data

NOVEMBER 28, 2023

Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing. Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3% Developers are learning how to find quality data and build models that work.

Enterprise 339

Enterprise Testing Modeling Reporting 339

CIO Business Intelligence

JANUARY 7, 2025

Defining policies and other AI governance was a priority at many organizations trying to channel how employees used copilots while protecting sensitive data from leaking to public LLMs. For AI to deliver safe and reliable results, data teams must classify data properly before feeding it to those hungry LLMs.

Digital Transformation 143

Digital Transformation Experimentation Cost-Benefit Strategy 143

CIO Business Intelligence

JANUARY 30, 2025

Metrics should include system downtime and reliability, security incidents, incident response times, data quality issues and system performance. Organizations need to have a data governance policy in place. You need to perform testing of the new model and ensure that you are setting aside enough time for testing and evaluation.

Testing 93

Testing Metrics Cost-Benefit Modeling 93